diff --git a/.codex b/.codex
new file mode 100644
index 0000000..e69de29
diff --git a/.superpowers/brainstorm/31513-1776930411/.server-stopped b/.superpowers/brainstorm/31513-1776930411/.server-stopped
new file mode 100644
index 0000000..031081f
--- /dev/null
+++ b/.superpowers/brainstorm/31513-1776930411/.server-stopped
@@ -0,0 +1 @@
+{"reason":"idle timeout","timestamp":1776932392614}
diff --git a/.superpowers/brainstorm/31513-1776930411/.server.pid b/.superpowers/brainstorm/31513-1776930411/.server.pid
new file mode 100644
index 0000000..234cca7
--- /dev/null
+++ b/.superpowers/brainstorm/31513-1776930411/.server.pid
@@ -0,0 +1 @@
+31521
diff --git a/.superpowers/brainstorm/31513-1776930411/scene-layout.html b/.superpowers/brainstorm/31513-1776930411/scene-layout.html
new file mode 100644
index 0000000..fea996b
--- /dev/null
+++ b/.superpowers/brainstorm/31513-1776930411/scene-layout.html
@@ -0,0 +1,48 @@
+<h2>新任务场景草图：双臂空中穿环插入</h2>
+<p class="subtitle">保持双 Diana + 同一张桌子；左侧放环状木块，右侧放长条木块，先各自抓起，再在空中完成穿孔插入。</p>
+
+<div class="split">
+  <div class="mockup">
+    <div class="mockup-header">俯视布局（初始）</div>
+    <div class="mockup-body">
+      <div style="position:relative;height:340px;background:#f3f4f6;border-radius:16px;border:1px solid #d1d5db;overflow:hidden;">
+        <div style="position:absolute;left:12%;top:18%;width:22%;height:64%;border:2px dashed #60a5fa;border-radius:18px;background:rgba(96,165,250,.08);display:flex;align-items:flex-start;justify-content:center;padding-top:8px;font-weight:700;">左臂采样区（环）</div>
+        <div style="position:absolute;right:12%;top:18%;width:22%;height:64%;border:2px dashed #f59e0b;border-radius:18px;background:rgba(245,158,11,.08);display:flex;align-items:flex-start;justify-content:center;padding-top:8px;font-weight:700;">右臂采样区（长条）</div>
+        <div style="position:absolute;left:50%;top:50%;transform:translate(-50%,-50%);width:120px;height:120px;border:10px solid #ef4444;background:white;box-sizing:border-box;display:flex;align-items:center;justify-content:center;font-weight:700;color:#ef4444;">环块</div>
+        <div style="position:absolute;left:72%;top:50%;transform:translate(-50%,-50%) rotate(0deg);width:110px;height:22px;background:#10b981;border:2px solid #065f46;border-radius:6px;display:flex;align-items:center;justify-content:center;color:white;font-size:12px;font-weight:700;">长条</div>
+        <div style="position:absolute;left:50%;top:12%;transform:translateX(-50%);padding:6px 10px;background:#111827;color:white;border-radius:999px;font-size:12px;">桌面中心 / 空中插入区上方</div>
+      </div>
+    </div>
+  </div>
+  <div class="mockup">
+    <div class="mockup-header">任务阶段</div>
+    <div class="mockup-body">
+      <div class="section">
+        <div class="label">Phase 1</div>
+        <h3>左右臂分别接近目标</h3>
+        <p>左臂只抓环状木块，右臂只抓长条木块。两物体初始姿态固定，只随机平面位置。</p>
+      </div>
+      <div class="section">
+        <div class="label">Phase 2</div>
+        <h3>同时抓起并离桌</h3>
+        <p>两个物体都需要离开桌面后，才允许触发最终成功奖励。</p>
+      </div>
+      <div class="section">
+        <div class="label">Phase 3</div>
+        <h3>空中对齐并插入</h3>
+        <p>在桌面上方的会合区完成对齐，让长条沿自身长轴穿过环孔；一旦穿孔成功即判成功。</p>
+      </div>
+    </div>
+  </div>
+</div>
+
+<div class="section">
+  <h3>奖励草案（总分 5）</h3>
+  <div class="cards">
+    <div class="card"><div class="card-body"><h3>+1</h3><p>左臂碰到环块</p></div></div>
+    <div class="card"><div class="card-body"><h3>+1</h3><p>右臂碰到长条</p></div></div>
+    <div class="card"><div class="card-body"><h3>+1</h3><p>环块离桌</p></div></div>
+    <div class="card"><div class="card-body"><h3>+1</h3><p>长条离桌</p></div></div>
+    <div class="card"><div class="card-body"><h3>+1</h3><p>两者离桌时，长条穿过环孔</p></div></div>
+  </div>
+</div>
diff --git a/.superpowers/brainstorm/31513-1776930411/waiting-1.html b/.superpowers/brainstorm/31513-1776930411/waiting-1.html
new file mode 100644
index 0000000..3db9ea1
--- /dev/null
+++ b/.superpowers/brainstorm/31513-1776930411/waiting-1.html
@@ -0,0 +1 @@
+<div style="display:flex;align-items:center;justify-content:center;min-height:60vh"><p class="subtitle">Continuing in terminal...</p></div>
\ No newline at end of file
diff --git a/AGENTS.md b/AGENTS.md
new file mode 100644
index 0000000..91f2e41
--- /dev/null
+++ b/AGENTS.md
@@ -0,0 +1,39 @@
+# Repository Guidelines
+
+## Project Structure & Module Organization
+- `roboimi/` is the main package: simulation code in `roboimi/envs/`, assets/helpers in `roboimi/assets/`, VLA models/config in `roboimi/vla/`, and runnable scripts in `roboimi/demos/` plus `roboimi/demos/vla_scripts/`.
+- `tests/` holds `unittest` regressions for training, rollout, dataset loading, and asset resolution.
+- `docs/superpowers/` stores design specs and plans.
+- `docs/experiment-guide.md` store how beginning experiment
+- `runs/`, `outputs/`, `swanlog/`, `.worktrees/`, and `checkpoints/` are generated experiment artifacts, not source modules.
+
+## Build, Test, and Development Commands
+- `conda env create -f environment.yml && conda activate roboimi` — create the standard environment.
+- `pip install -e .` — editable install.
+- `python -m unittest discover -s tests -p 'test_*.py'` — run all tests.
+- `python -m unittest tests.test_train_vla_rollout_validation -v` — run one focused test module.
+- `python roboimi/vla/scripts/calculate_stats.py` — regenerate normalization stats.
+- `python roboimi/demos/vla_scripts/train_vla.py train.max_steps=1000` — quick training smoke test.
+- `python roboimi/demos/vla_scripts/eval_vla.py eval.ckpt_path=checkpoints/vla_model_best.pt eval.num_episodes=5` — evaluate a checkpoint.
+
+## Coding Style & Naming Conventions
+- Use Python, 4-space indentation, and PEP 8–style naming.
+- Prefer `snake_case` for files, functions, variables, and Hydra keys; use `CamelCase` for classes.
+- Match existing Hydra namespaces such as `train.*`, `eval.*`, and `agent.*`.
+- Keep diffs focused and imports tidy. Experiment names usually encode host/GPU, e.g. `...-l20g3-...`, `...-5880g1-...`, or `...-5090-...`.
+
+## Testing Guidelines
+- Tests use `unittest` and `unittest.mock`.
+- Name files `tests/test_*.py` and classes `*Test`.
+- Add direct regression coverage for changes to training, rollout, config, or dataset code paths.
+- No strict coverage target is defined, but changed behavior should be tested and affected suites should pass before review.
+
+## Commit & Pull Request Guidelines
+- Follow existing prefixes: `feat`, `fix`, `chore`, `docs`, `debug`, optionally with scope, e.g. `feat(eval): export rollout video timing`.
+- Keep commit messages imperative and specific.
+- PRs should state the goal, touched configs/scripts, exact test commands, and dataset/checkpoint assumptions. Attach visual artifacts only when outputs change.
+
+## Experiment Host Reference
+- **Local workstation (`droid-z790eagleax`)** — local shell; **1× RTX 5090 32 GB**; env: `/home/droid/.conda/envs/roboimi/bin/python`.
+- **5880 node (`droid-System-Product-Name`)** — username: `droid`; IP: `100.73.14.65`; connect with `ssh droid@100.73.14.65`; **2× RTX 5880 Ada 48 GB**; env: `/home/droid/miniforge3/envs/roboimi/bin/python`; trusted tailnet host.
+- **L20 node (`droid-G5500-V7`)** — username: `droid`; IP: `100.119.99.14`; connect with `ssh droid@100.119.99.14`; **8× NVIDIA L20 46 GB**; env: `/home/droid/miniforge3/envs/roboimi/bin/python`; store large experiment data under `/data`.
diff --git a/MUJOCO_LOG.TXT b/MUJOCO_LOG.TXT
new file mode 100644
index 0000000..1bacf47
--- /dev/null
+++ b/MUJOCO_LOG.TXT
@@ -0,0 +1,126 @@
+Mon Mar 30 13:35:31 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 13:35:31 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 13:39:46 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 13:39:46 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 14:30:24 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 14:30:24 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 14:41:26 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 14:41:26 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 15:21:19 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 15:21:19 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 20:58:52 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 20:59:05 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Mon Mar 30 22:05:37 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:00:10 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:00:10 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:03:41 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:03:41 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:05:09 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:05:09 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:06:00 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:06:00 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:09:24 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:09:24 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:14:36 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:14:36 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:20:44 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:23:07 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Tue Mar 31 16:29:49 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Thu Apr 23 11:55:22 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Thu Apr 23 11:55:22 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Thu Apr 23 12:43:50 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Thu Apr 23 12:43:50 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Thu Apr 23 12:43:50 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Thu Apr 23 12:43:50 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Thu Apr 23 12:43:50 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Thu Apr 23 12:43:50 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Thu Apr 23 12:43:50 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Thu Apr 23 12:43:50 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Sun May 24 21:50:49 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Sun May 24 21:51:10 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Sun May 24 21:53:08 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
+Sun May 24 21:53:28 2026
+WARNING: OpenGL error 0x502 in or before mjr_makeContext
+
diff --git a/capture_sim_frames.py b/capture_sim_frames.py
new file mode 100644
index 0000000..e5974ee
--- /dev/null
+++ b/capture_sim_frames.py
@@ -0,0 +1,66 @@
+"""Capture first and last (success) frames from front camera for both sim tasks."""
+import os
+import time
+import numpy as np
+import cv2
+from roboimi.envs.double_pos_ctrl_env import make_sim_env
+from roboimi.demos.diana_policy import TestPickAndTransferPolicy
+from roboimi.demos.diana_air_insert_policy import TestAirInsertPolicy
+from roboimi.utils.act_ex_utils import sample_transfer_pose, sample_air_insert_socket_peg_state
+
+OUTPUT_DIR = "sim_frames"
+IMG_SIZE = (256, 256)
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+
+def capture_task(task_name, policy, task_state, episode_len):
+    env = make_sim_env(task_name, headless=True)
+    env.reset(task_state)
+    time.sleep(1)
+
+    env._update_camera_images_sync()
+    first_frame = cv2.resize(env.front, IMG_SIZE)
+
+    last_success_frame = None
+    max_reward = 0
+
+    for step in range(episode_len):
+        action = policy.predict(task_state, step)
+        env.step(action)
+        env._update_camera_images_sync()
+        if env.rew is not None and env.rew >= max_reward:
+            max_reward = env.rew
+            if env.rew == env.max_reward:
+                last_success_frame = cv2.resize(env.front, IMG_SIZE)
+
+    if last_success_frame is None:
+        last_success_frame = cv2.resize(env.front, IMG_SIZE)
+        print(f"  [WARN] {task_name}: max_reward={max_reward}/{env.max_reward}, using last frame")
+    else:
+        print(f"  {task_name}: success! max_reward={max_reward}")
+
+    return first_frame, last_success_frame
+
+
+def main():
+    # Transfer task (episode_len=700, policy covers full range)
+    print("Running sim_transfer...")
+    task_state_transfer = sample_transfer_pose()
+    policy_transfer = TestPickAndTransferPolicy(inject_noise=False)
+    first_t, last_t = capture_task('sim_transfer', policy_transfer, task_state_transfer, 700)
+    cv2.imwrite(os.path.join(OUTPUT_DIR, "transfer_front_first.png"), first_t)
+    cv2.imwrite(os.path.join(OUTPUT_DIR, "transfer_front_last.png"), last_t)
+
+    # Air insert / peg-in-socket (policy EPISODE_END_T=600)
+    print("Running sim_air_insert_socket_peg...")
+    task_state_insert = sample_air_insert_socket_peg_state()
+    policy_insert = TestAirInsertPolicy(inject_noise=False)
+    first_i, last_i = capture_task('sim_air_insert_socket_peg', policy_insert, task_state_insert, 600)
+    cv2.imwrite(os.path.join(OUTPUT_DIR, "air_insert_front_first.png"), first_i)
+    cv2.imwrite(os.path.join(OUTPUT_DIR, "air_insert_front_last.png"), last_i)
+
+    print(f"\nDone! Images saved to {OUTPUT_DIR}/")
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docs/experiment-guide.md b/docs/experiment-guide.md
new file mode 100644
index 0000000..9eab88e
--- /dev/null
+++ b/docs/experiment-guide.md
@@ -0,0 +1,498 @@
+# 实验操作指南
+
+## 0. 适用范围与当前起点
+
+这份文档面向 **当前 `main` 分支** 的通用训练 / 验证 / 评估流程，不绑定某一条历史实验分支。
+
+当前默认 Hydra 起点：
+- agent：`resnet_transformer`
+- data：`simpe_robot_dataset`
+- eval：`eval`
+
+当前仓库里常见可选 agent 配置包括：
+- `resnet_transformer`
+- `resnet_diffusion`
+- `resnet_gr00t_dit`
+- `resnet_imf_attnres`
+- `resnet_imf_attnres_multitoken`
+- `siglip2_imf_attnres`
+- `lewm_imf_attnres`
+
+说明：
+- `experiment_suites/` 下的历史目录仍然可以作为参考，但**正文以当前 `main` 的通用流程为准**。
+- 如果你只是想快速开始，优先从默认 `resnet_transformer` 配置出发，再按实验需求替换 agent。
+
+---
+
+## 1. 关键文件与入口
+
+| 路径 | 作用 |
+| --- | --- |
+| `roboimi/demos/vla_scripts/train_vla.py` | 主训练入口；负责数据集、checkpoint、`val/loss`、held-out action MSE、训练期 rollout 验证、SwanLab |
+| `roboimi/demos/vla_scripts/eval_vla.py` | 单次 rollout / 离线评估入口；支持 headless、summary、trajectory image、video artifact、多进程并行 rollout |
+| `roboimi/vla/conf/config.yaml` | 全局 Hydra 训练配置 |
+| `roboimi/vla/conf/data/simpe_robot_dataset.yaml` | 默认数据集配置；数据路径、相机名、图像 resize 都在这里 |
+| `roboimi/vla/conf/eval/eval.yaml` | eval 默认配置；`eval.ckpt_path`、`eval.num_episodes`、artifact 开关、多进程 rollout 配置都在这里 |
+| `roboimi/vla/conf/agent/*.yaml` | 可选 agent 配置集合 |
+| `roboimi/vla/data/simpe_robot_dataset.py` | HDF5 懒加载数据集；支持 `episode_indices` 过滤与 `available_episode_indices` 元信息 |
+| `roboimi/vla/scripts/calculate_stats.py` | 重算 `dataset_stats.pkl` |
+| `experiment_suites/` | 历史实验目录；可参考，但不作为当前主流程的唯一事实来源 |
+
+---
+
+## 2. 三台机器与环境
+
+| 机器 | GPU | Python | 常用数据集路径 | 备注 |
+| --- | --- | --- | --- | --- |
+| 本地 `droid-z790eagleax` | 1× RTX 5090 32GB | `/home/droid/.conda/envs/roboimi/bin/python` | `/home/droid/project/diana_sim/sim_transfer` | 适合 smoke、单条主跑、本地调参 |
+| 5880 节点 `100.73.14.65` | 2× RTX 5880 Ada 48GB | `/home/droid/miniforge3/envs/roboimi/bin/python` | `/home/droid/sim_dataset/sim_transfer` | 适合 2 条并行主跑 |
+| L20 节点 `100.119.99.14` | 8× NVIDIA L20 46GB | `/home/droid/miniforge3/envs/roboimi/bin/python` | `/data/simtransfer/current` | 适合大规模 grid；建议数据与 run 放 `/data` |
+
+连接：
+- 5880：`ssh droid@100.73.14.65`
+- L20：`ssh droid@100.119.99.14`
+
+说明：
+- repo / worktree 路径可能会随当前 checkout 或同步目录变化，**以你当前机器上的实际 repo 路径为准**。
+- 如果训练目录会被 Hydra 自动切走，建议显式设置 `hydra.run.dir=...` 以固定输出目录。
+
+---
+
+## 3. 训练流怎么走
+
+`train_vla.py` 当前主流程如下：
+
+1. 读取 Hydra 配置并打印完整 cfg
+2. 根据配置确定数据集图像 resize：
+   - 默认用 `data.image_resize_shape`
+   - 如果 `agent.vision_backbone.dataset_image_resize_shape` 存在，则优先用 backbone 覆盖值
+3. 通过 `build_train_val_datasets()` 构建 dataset / train dataset / val dataset
+   - 若设置 `train.val_episode_indices`：按显式 episode 切出 held-out val
+   - 否则按 `train.val_split` 做随机划分
+4. 用 `DataLoader` 建 train / val loader
+5. 从 `dataset_dir/dataset_stats.pkl` 读取归一化统计
+6. 实例化当前指定的 agent：`instantiate(cfg.agent, dataset_stats=...)`
+7. 可选加载：
+   - `train.pretrained_ckpt`：微调起点
+   - `train.resume_ckpt`：断点续训（支持显式路径或 `auto`）
+8. 建 optimizer 与 scheduler
+9. 训练循环里按 `log_freq` 记录 train loss / lr
+10. 按 `save_freq` 保存 `checkpoints/vla_model_step_*.pt`，并在有 val loader 时计算 `val/loss`
+11. 若设置了显式 held-out val，并且 `train.action_mse_val_freq_epochs > 0`，则按 epoch 计算 held-out `action MSE`
+12. 按 `train.rollout_val_freq_epochs` 跑训练期 rollout 验证
+13. 最后写：
+    - `checkpoints/vla_model_best.pt`
+    - `checkpoints/vla_model_final.pt`
+
+当前 best model 选择逻辑：
+- **第一次拿到 rollout reward 之前**：先用 `val_loss`（或 train loss 回退）挑 best
+- **第一次 rollout 之后**：优先用 `rollout_avg_reward` 挑 best
+
+输出目录：
+- 当前代码实际输出写到 **Hydra runtime output dir** 下
+- checkpoint 默认在：`<hydra_output_dir>/checkpoints/`
+- 如果不显式指定 `hydra.run.dir`，Hydra 会自动生成目录
+
+---
+
+## 4. 验证流怎么走
+
+### 4.1 常规 `val/loss`
+
+常规验证有两种来源：
+
+1. **随机划分验证集**
+   - 用 `train.val_split > 0`
+   - 训练脚本会在保存 checkpoint 时计算 `val/loss`
+
+2. **显式 held-out episode 验证集**
+   - 用 `train.val_episode_indices=[...]`
+   - 训练集 = 全部 episode - held-out episode
+   - 验证集 = held-out episode
+   - checkpoint 保存时同样会计算 `val/loss`
+
+说明：
+- 若 `train.val_split=0.0` 且未设置 `train.val_episode_indices`，则不会有 `val/loss`。
+
+### 4.2 held-out action MSE
+
+如果想对固定 held-out episode 做更稳定、可复现的动作预测误差验证，推荐使用：
+
+- `train.val_split=0.0`
+- `train.val_episode_indices=[100]`（或你指定的 episode 列表）
+- `train.action_mse_val_freq_epochs=1`
+
+当前 `main` 中这套逻辑会：
+- 每隔 `action_mse_val_freq_epochs` 个 epoch
+- 对 held-out val loader 调 `agent.predict_action_chunk(...)`
+- 与 batch 中的 `action` 计算 masked MSE
+- 如果存在 `action_is_pad`，会自动 mask 掉 padding 部分
+
+日志 key：
+- 控制台 / `train_vla.log`：`held-out action MSE`
+- SwanLab：`val/action_mse`
+
+重要约束：
+- `train.action_mse_val_freq_epochs > 0` **必须搭配** `train.val_episode_indices`
+- 如果只设了 `action_mse_val_freq_epochs` 而没有设 `val_episode_indices`，训练会直接报错
+
+### 4.3 rollout 验证
+
+训练内 rollout 验证由：
+- `train_vla.py -> run_rollout_validation() -> eval_vla._run_eval()`
+
+当前训练内 rollout 会强制：
+- `headless=true`
+- `verbose_action=false`
+- `record_video=false`
+- `save_trajectory_image=true`
+- `trajectory_image_camera_name=front`
+- `save_summary_json=true`
+
+当前这套路径是**配置驱动**的：
+- `train.rollout_device`：默认跟随 `train.device`
+- `train.rollout_num_workers`：默认 `null`
+  - 当 rollout 设备是 CPU 时，自动退化为 `1`
+  - 当 rollout 设备是 CUDA 时，自动推断为 `min(train.rollout_num_episodes, 8)`
+- `train.rollout_cuda_devices`：默认 `null`，等价于逻辑 GPU `[0]`
+- `train.rollout_response_timeout_s`
+- `train.rollout_server_startup_timeout_s`
+
+所以现在：
+- 训练在 CUDA 上时，训练期 rollout 默认也会走 GPU
+- `rollout_num_workers > 1` 时，会走并行 rollout
+- 可以是 **单 GPU 多 worker 共用一个 inference server**
+- 也可以是 **多 GPU 多 server 分摊 worker**
+
+训练内 rollout artifact 默认落到：
+- `<hydra_output_dir>/rollout_artifacts/<checkpoint_stem>/`
+
+常见文件：
+- `rollout_summary.json`
+- `rollout_front_ep01_trajectory.png` ...
+
+日志重点看：
+- `Epoch X rollout 平均奖励`
+- `最佳模型已更新`
+
+---
+
+## 5. 数据集加载与 `val_episode_indices` 机制
+
+### 5.1 数据集格式
+
+`SimpleRobotDataset` 读取 `dataset_dir` 下的 `*.hdf5` / `episode_*.hdf5`，每个 episode 文件至少要有：
+- `action`
+- `observations/qpos`
+- `observations/images/{cam_name}`
+
+默认数据配置中的相机：
+- `r_vis`
+- `top`
+- `front`
+
+### 5.2 懒加载行为
+
+`roboimi/vla/data/simpe_robot_dataset.py` 是按帧懒加载，不会一次性把整套 HDF5 全读进内存。
+
+它会：
+- 扫描目录下的 HDF5 文件
+- 在 worker 内做 HDF5 文件句柄 LRU 缓存
+- 根据文件名中的 `episode_XXX` 建立 `available_episode_indices`
+
+### 5.3 `val_episode_indices` 怎么切
+
+`build_train_val_datasets()` 的逻辑是：
+
+1. 先 instantiate 一次完整 dataset
+2. 读取 `dataset.available_episode_indices`
+3. 检查 `train.val_episode_indices` 是否都存在
+4. 用 `episode_indices=` 再各 instantiate 一次：
+   - train dataset = 全部 episode - held-out episode
+   - val dataset = 只包含 held-out episode
+
+因此：
+- `train.val_episode_indices=[100]` 的意思是把 `episode_100.hdf5` 整个拿去做 held-out val
+- 如果 episode 不存在，会直接报错
+- 如果你把所有 episode 都塞进 `val_episode_indices`，也会直接报错，因为训练集会变空
+
+### 5.4 图像 resize 与返回字段
+
+dataset 侧 resize 默认来自：
+- `data.image_resize_shape`
+- 如果 backbone 额外覆盖，则优先 `agent.vision_backbone.dataset_image_resize_shape`
+
+当前通用 batch 返回字段包括：
+- `observation.state`
+- `observation_is_pad`
+- `observation.<cam>`
+- `action`
+- `action_is_pad`
+- `task`
+
+### 5.5 统计文件
+
+训练和推理都默认依赖 `dataset_stats.pkl`。数据集更新后需要重算：
+
+```bash
+/home/droid/.conda/envs/roboimi/bin/python roboimi/vla/scripts/calculate_stats.py \
+  --dataset_dir /home/droid/project/diana_sim/sim_transfer
+```
+
+远端只要把：
+- Python 路径
+- `--dataset_dir`
+
+替换成对应机器上的实际路径即可。
+
+---
+
+## 6. SwanLab 行为
+
+当前默认配置里：
+- `train.use_swanlab=false`
+
+如果要开启，通常显式设置：
+- `train.use_swanlab=true`
+- `train.swanlab_project=roboimi-vla`
+- `train.swanlab_run_name=<run_name>`
+
+`train_vla.py` 当前会记录：
+- 初始化时上传 `train` / `data` / `agent` 三段 config
+- 训练中：
+  - `train/loss`
+  - `train/lr`
+  - `train/best_loss`
+  - `train/step`
+- checkpoint 验证时：
+  - `val/loss`
+- held-out 数值验证时：
+  - `val/action_mse`
+- rollout 验证时：
+  - `rollout/avg_reward`
+  - `rollout/epoch`
+- 训练结束时：
+  - `final/checkpoint_path`
+  - `final/best_checkpoint_path`
+
+训练期 rollout 生成的前视图轨迹 PNG 会 best-effort 上传到 SwanLab；上传失败只会 warning，不会让训练中断。
+
+---
+
+## 7. 并行 rollout 说明
+
+### 7.1 这套能力现在在哪里
+
+当前主仓库已经**内置**多进程并行 rollout 能力，入口就是：
+- `roboimi/demos/vla_scripts/eval_vla.py`
+
+控制参数：
+- `eval.num_workers`
+- `eval.cuda_devices`
+
+语义：
+- `eval.num_workers`：环境 worker 数，按 episode 切分
+- `eval.cuda_devices`：推理 server 绑定到哪些逻辑 GPU
+
+### 7.2 两种常见模式
+
+1. **单机单卡，多 worker 共用同一张 GPU**
+   - 典型：5090 只有 1 卡，但想让 4 个 rollout worker 并行跑环境
+   - 形式：`eval.device=cuda eval.num_workers=4 'eval.cuda_devices=[0]'`
+   - 这时是 **1 个 CUDA inference server + 4 个 env worker**
+
+2. **单机多卡，多 server 分摊 worker**
+   - 典型：5880 / L20 有多卡
+   - 形式：`eval.device=cuda eval.num_workers=8 'eval.cuda_devices=[0,1]'`
+   - worker 会按 round-robin 分到多个 server 上
+
+### 7.3 操作约束
+
+- 并行 rollout 依赖 **多进程 eval 路径**，不是 `train.num_workers`
+- `train.num_workers` 是 DataLoader worker，和 rollout 并行不是一回事
+- `eval.num_workers > 1` 时必须 `eval.headless=true`
+- worker 数会自动 cap 到 `eval.num_episodes`
+- 多 worker 时**不支持**同时导出：
+  - `eval.record_video=true`
+  - `eval.save_trajectory=true`
+  - `eval.save_trajectory_npz=true`
+- `eval.save_trajectory_image=true` 可以开，适合并行 reward + 定性检查一起做
+
+---
+
+## 8. 常用命令模板
+
+下面的模板都以 **当前 `main` 通用流程** 为准。远端机器只需要替换：
+- Python 路径
+- repo 路径
+- `data.dataset_dir`
+- `hydra.run.dir`
+
+### 8.1 本地 smoke train
+
+```bash
+/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
+  agent=resnet_transformer \
+  data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
+  train.device=cuda \
+  train.batch_size=8 \
+  train.max_steps=1000 \
+  train.num_workers=4 \
+  train.save_freq=200 \
+  hydra.run.dir=/home/droid/project/roboimi/runs/smoke-resnet-transformer
+```
+
+### 8.2 通用训练模板
+
+```bash
+/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
+  agent=resnet_transformer \
+  data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
+  train.device=cuda \
+  train.batch_size=16 \
+  train.lr=0.0001 \
+  train.max_steps=100000 \
+  train.num_workers=4 \
+  train.save_freq=2000 \
+  train.use_swanlab=true \
+  train.swanlab_project=roboimi-vla \
+  train.swanlab_run_name=<run_name> \
+  hydra.run.dir=/home/droid/project/roboimi/runs/<run_name>
+```
+
+### 8.3 带 held-out episode 验证的训练模板
+
+```bash
+/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
+  agent=resnet_transformer \
+  data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
+  train.device=cuda \
+  train.batch_size=16 \
+  train.lr=0.0001 \
+  train.max_steps=100000 \
+  train.num_workers=4 \
+  train.val_split=0.0 \
+  'train.val_episode_indices=[100]' \
+  train.action_mse_val_freq_epochs=1 \
+  train.rollout_val_freq_epochs=5 \
+  train.rollout_num_episodes=10 \
+  train.use_swanlab=true \
+  train.swanlab_project=roboimi-vla \
+  train.swanlab_run_name=<run_name> \
+  hydra.run.dir=/home/droid/project/roboimi/runs/<run_name>
+```
+
+### 8.4 断点续训模板
+
+`train_vla.py` 支持通过 CLI override 使用 `train.resume_ckpt`：
+
+```bash
+/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
+  agent=resnet_transformer \
+  data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
+  train.device=cuda \
+  train.resume_ckpt=auto \
+  hydra.run.dir=/home/droid/project/roboimi/runs/<run_name>
+```
+
+也可以把 `train.resume_ckpt` 换成显式 checkpoint 路径。
+
+### 8.5 单次离线评估
+
+```bash
+/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/eval_vla.py \
+  agent=resnet_transformer \
+  data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
+  train.device=cuda eval.device=cuda \
+  eval.ckpt_path=/home/droid/project/roboimi/runs/<run_name>/checkpoints/vla_model_best.pt \
+  eval.num_episodes=10 \
+  eval.headless=true \
+  eval.verbose_action=false \
+  eval.save_summary_json=true \
+  eval.save_trajectory_image=true \
+  eval.trajectory_image_camera_name=front \
+  eval.artifact_dir=/tmp/roboimi_eval_front
+```
+
+### 8.6 并行离线评估
+
+```bash
+/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/eval_vla.py \
+  agent=resnet_transformer \
+  data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
+  train.device=cuda eval.device=cuda \
+  eval.ckpt_path=/home/droid/project/roboimi/runs/<run_name>/checkpoints/vla_model_best.pt \
+  eval.num_episodes=10 \
+  eval.num_workers=4 \
+  'eval.cuda_devices=[0]' \
+  eval.headless=true \
+  eval.verbose_action=false \
+  eval.save_summary_json=true \
+  eval.save_trajectory_image=true \
+  eval.trajectory_image_camera_name=front \
+  eval.artifact_dir=/tmp/roboimi_parallel_eval
+```
+
+### 8.7 训练内启用并行 GPU rollout
+
+```bash
+/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
+  agent=resnet_transformer \
+  data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
+  train.device=cuda \
+  train.batch_size=16 \
+  train.lr=0.0001 \
+  train.max_steps=100000 \
+  train.num_workers=4 \
+  train.rollout_val_freq_epochs=5 \
+  train.rollout_num_episodes=10 \
+  train.rollout_device=cuda \
+  train.rollout_num_workers=4 \
+  'train.rollout_cuda_devices=[0]' \
+  train.rollout_validate_on_checkpoint=false \
+  train.use_swanlab=true \
+  train.swanlab_project=roboimi-vla \
+  train.swanlab_run_name=<run_name> \
+  hydra.run.dir=/home/droid/project/roboimi/runs/<run_name>
+```
+
+### 8.8 重算统计文件
+
+```bash
+/home/droid/.conda/envs/roboimi/bin/python roboimi/vla/scripts/calculate_stats.py \
+  --dataset_dir /home/droid/project/diana_sim/sim_transfer
+```
+
+### 8.9 监控日志
+
+```bash
+tail -f runs/<run_name>/train_vla.log
+```
+
+如果你显式设置了 `hydra.run.dir`，优先直接 tail 对应绝对路径下的日志文件。
+
+---
+
+## 9. 操作建议
+
+- **优先固定 `hydra.run.dir`**，避免输出目录分散在 Hydra 自动生成路径里
+- 如果只需要常规验证，使用：
+  - `train.val_split > 0`
+- 如果需要稳定、可复现的 held-out 指标，使用：
+  - `train.val_split=0.0`
+  - `train.val_episode_indices=[...]`
+  - `train.action_mse_val_freq_epochs=1`
+- `train.num_workers` 是 DataLoader worker，不等于 rollout 并行度
+- rollout 并行优先看：
+  - `train.rollout_num_workers`
+  - `train.rollout_cuda_devices`
+  - `eval.num_workers`
+  - `eval.cuda_devices`
+- 数据集更新后记得重算 `dataset_stats.pkl`
+- 若同时设置了：
+  - `train.pretrained_ckpt`
+  - `train.resume_ckpt`
+  当前逻辑会优先走 `resume_ckpt`
+- 建议 run name 中带上关键信息：
+  - agent / horizon / batch size / lr / host / gpu / date
+- `experiment_suites/` 下的历史目录适合作为参考资料，但不要默认把某一条旧 suite 当作当前主流程的唯一标准
diff --git a/docs/superpowers/plans/2026-03-30-align-transformer-diffusion-to-external.md b/docs/superpowers/plans/2026-03-30-align-transformer-diffusion-to-external.md
new file mode 100644
index 0000000..bac99a0
--- /dev/null
+++ b/docs/superpowers/plans/2026-03-30-align-transformer-diffusion-to-external.md
@@ -0,0 +1,256 @@
+# Align ResNet Transformer Diffusion To External Repo Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development to implement this plan task-by-task. Do not switch to inline execution. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** 将当前仓库的 `resnet_transformer` 对齐到 external repo `/home/droid/project/diffusion_policy` 的原生 DDPM Transformer Diffusion（非 PMF、非 DiT、非 UNet）实现，并采用 external 中已存在的 full-attention / nocausal 变体（`causal_attn=false`），固定为三相机图像条件输入，同时保持 EE action 语义的推理执行路径正确。
+
+**Architecture:** 保留当前仓库较轻量的训练脚本和数据集组织，但把 Transformer denoiser 本体对齐到 external repo 的 `TransformerForDiffusion` 实现与接口语义；视觉编码器保留当前仓库的 ResNet+SpatialSoftmax 路线，仅保证其输出维度与三相机条件输入兼容。评估路径统一按 EE action 调 `env.step(action)`，训练/推理配置显式固定三相机 `r_vis/top/front` 且图像永远作为条件输入。
+
+**Tech Stack:** Python, PyTorch, diffusers DDPM/DDIM, Hydra/OmegaConf, unittest, h5py, OpenCV
+
+---
+
+### Task 0: 执行前提与分支约束
+
+**Files:**
+- Verify only
+
+- [ ] **Step 1: Confirm execution stays on the feature branch**
+
+Run: `git branch --show-current`
+Expected: `feat-align-dp-transformer-ee`
+
+If the current branch is not `feat-align-dp-transformer-ee`, create/switch to it before continuing:
+
+Run: `git checkout -b feat-align-dp-transformer-ee || git checkout feat-align-dp-transformer-ee`
+Expected: working branch becomes `feat-align-dp-transformer-ee`
+
+- [ ] **Step 2: Confirm implementation is executed with subagents**
+
+Use `superpowers:subagent-driven-development` for implementation tasks and reviews. Do not switch to inline/manual execution unless the human explicitly changes course.
+
+- [ ] **Step 3: Confirm the current branch is the only target branch for this work**
+
+Do not create or switch to another branch/worktree during implementation unless the human explicitly asks for it. All changes for this migration stay on `feat-align-dp-transformer-ee`.
+
+### Task 1: 验证 EE action 推理执行语义已固定
+
+**Files:**
+- Verify: `roboimi/vla/eval_utils.py`
+- Verify: `roboimi/demos/vla_scripts/eval_vla.py`
+- Verify: `tests/test_eval_vla_execution.py`
+
+- [ ] **Step 1: Verify the test still passes on the feature branch**
+
+Run: `mamba run -n roboimi python -m unittest tests.test_eval_vla_execution -v`
+Expected: PASS
+
+- [ ] **Step 2: Verify the real eval script no longer executes joint-action stepping**
+
+Run:
+```bash
+python - <<'PY'
+from pathlib import Path
+src = Path('roboimi/demos/vla_scripts/eval_vla.py').read_text()
+assert 'execute_policy_action(env, action)' in src
+assert 'env.step_jnt(action)' not in src
+print('eval_vla execution path verified')
+PY
+```
+Expected: PASS
+
+### Task 2: 通过对拍测试把当前 Transformer head 对齐到 external repo 的 `TransformerForDiffusion`
+
+**Files:**
+- Modify: `roboimi/vla/models/heads/transformer1d.py`
+- Modify: `roboimi/vla/conf/head/transformer1d.yaml`
+- Test: `tests/test_transformer1d_external_alignment.py`
+
+- [ ] **Step 1: Confirm the alignment test exists and captures the intended parity contract**
+
+Write a test that imports external repo's `TransformerForDiffusion` from:
+`/home/droid/project/diffusion_policy/diffusion_policy/model/diffusion/transformer_for_diffusion.py`
+and verifies the local `Transformer1D` can load the external model's `state_dict` and produce numerically identical outputs for the same inputs when configured equivalently. Use the full-attention / nocausal configuration (`causal_attn=False`) as the target behavior.
+
+Required assertions:
+- same parameter key structure (or compatible `load_state_dict(strict=True)`)
+- same output shape `(B, T, action_dim)`
+- `torch.allclose(local_out, external_out, atol=1e-6, rtol=1e-5)`
+- local model exposes `get_optim_groups(weight_decay=...)` like external repo
+- use an explicit import path / loader that does not depend on installing the external repo as a package
+- account for external `ModuleAttrMixin`-style optimizer grouping expectations, including `_dummy_variable` / no-decay bookkeeping if needed for strict state-dict parity
+- set both models to `eval()` and fix the random seed inside the test so dropout does not create false mismatches
+
+- [ ] **Step 2: If the test still fails on this branch, use that red state as the TDD starting point**
+
+Run: `mamba run -n roboimi python -m unittest tests.test_transformer1d_external_alignment -v`
+Expected: either FAIL before implementation on a fresh checkout, or PASS if Task 2 has already been completed on this branch.
+
+- [ ] **Step 3a: Port API and state-dict compatibility first**
+
+Match constructor args, parameter names, embeddings, masks, and strict `state_dict` layout with external `TransformerForDiffusion`.
+
+- [ ] **Step 3b: Port `_init_weights` and optimizer grouping**
+
+Match external `_init_weights`, `get_optim_groups`, and `configure_optimizers`.
+
+- [ ] **Step 3c: Port forward semantics**
+
+Match external `forward(sample, timestep, cond)` behavior under the full-attention / nocausal configuration.
+
+- [ ] **Step 3d: Port the minimal external implementation**
+
+Update `roboimi/vla/models/heads/transformer1d.py` so it matches external repo's native DDPM transformer implementation semantics:
+- same constructor arguments and defaults where relevant
+- same token accounting (`time_as_cond`, `obs_as_cond`, `T_cond`)
+- same parameter naming/layout for embeddings, encoder, decoder, masks
+- same `_init_weights`
+- same `get_optim_groups` / `configure_optimizers`
+- same `forward(sample, timestep, cond)` behavior
+
+Do **not** port PMF / IMF / DiT branches.
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `mamba run -n roboimi python -m unittest tests.test_transformer1d_external_alignment -v`
+Expected: PASS with strict state-dict load and matching outputs.
+
+### Task 3: 将当前 Agent 与配置固定到“三相机图像作为条件”的 Transformer diffusion 路线
+
+**Files:**
+- Modify: `roboimi/vla/agent.py`
+- Modify: `roboimi/vla/conf/agent/resnet_transformer.yaml`
+- Modify: `roboimi/vla/conf/data/simpe_robot_dataset.yaml`
+- Modify: `roboimi/vla/conf/eval/eval.yaml`
+- Modify: `roboimi/vla/models/backbones/resnet_diffusion.py`
+- Test: `tests/test_resnet_transformer_agent_wiring.py`
+
+- [ ] **Step 1: Write the failing test**
+
+Write a wiring test that instantiates the Transformer agent config and checks:
+- `head_type == "transformer"`
+- `cfg.data.camera_names == cfg.eval.camera_names == ["r_vis", "top", "front"]`
+- `num_cams == 3`
+- Transformer `cond_dim == single_cam_feat_dim * 3 + obs_dim`
+- `predict_action(...)` accepts image conditions and returns `(B, pred_horizon, action_dim)`
+- test setup must not download weights; override `pretrained_backbone_weights=null` or stub the backbone
+- assert camera order used for conditioning is tied to the required three cameras, not a stray constant
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `mamba run -n roboimi python -m unittest tests.test_resnet_transformer_agent_wiring -v`
+Expected: FAIL if configs/head wiring do not yet guarantee three-camera conditional setup.
+
+- [ ] **Step 3: Implement minimal alignment**
+
+Make the transformer path explicit and stable:
+- keep image observations always as condition (`cond_dim > 0`, `obs_as_cond` semantics)
+- keep exactly three cameras: `r_vis`, `top`, `front`
+- keep current ResNet+SpatialSoftmax backbone unless a test proves incompatibility
+- make the camera feature order deterministic and aligned to the required three-camera list, not generic key sorting
+- ensure `agent.per_step_cond_dim` and config `head.cond_dim` stay consistent
+- ensure eval config uses the same three camera names as training
+- ensure transformer config follows the external full-attention variant (`causal_attn=false`) instead of the external default causal setting
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `mamba run -n roboimi python -m unittest tests.test_resnet_transformer_agent_wiring -v`
+Expected: PASS
+
+### Task 4: 让训练脚本在 Transformer 路线上尽量遵循 external repo 的 optimizer/head 使用方式
+
+**Files:**
+- Modify: `roboimi/demos/vla_scripts/train_vla.py`
+- Test: `tests/test_train_vla_transformer_optimizer.py`
+
+- [ ] **Step 1: Write the failing test**
+
+Write a test that builds a transformer agent and verifies the training script prefers the head/model supplied optimizer grouping when available (via `get_optim_groups`) instead of blindly using one flat `AdamW(agent.parameters(), ...)`.
+The test must also prove that every remaining trainable non-head parameter is included exactly once in the optimizer (no silent drops, no duplicates).
+
+- [ ] **Step 2: Run test to verify it fails**
+
+Run: `mamba run -n roboimi python -m unittest tests.test_train_vla_transformer_optimizer -v`
+Expected: FAIL because current training script uses flat optimizer construction.
+
+- [ ] **Step 3: Implement minimal optimizer alignment**
+
+Update `train_vla.py` so that:
+- for transformer head paths, if `agent.noise_pred_net` exposes `get_optim_groups`, build optimizer groups like external repo
+- explicitly include the remaining trainable non-head parameters (for example the ResNet backbone's non-frozen projection / pooling layers) in the optimizer instead of accidentally dropping them
+- keep the rest of the simple training loop intact (no EMA unless required later)
+- do not over-port external workspace abstractions
+
+- [ ] **Step 4: Run test to verify it passes**
+
+Run: `mamba run -n roboimi python -m unittest tests.test_train_vla_transformer_optimizer -v`
+Expected: PASS
+
+### Task 5: 端到端实例化与最小推理验收
+
+**Files:**
+- Verify only
+
+- [ ] **Step 1: Run all focused unit tests**
+
+Run:
+```bash
+mamba run -n roboimi python -m unittest \
+  tests.test_eval_vla_execution \
+  tests.test_transformer1d_external_alignment \
+  tests.test_resnet_transformer_agent_wiring \
+  tests.test_train_vla_transformer_optimizer -v
+```
+Expected: all PASS
+
+- [ ] **Step 2: Run syntax checks on changed training/eval/model files**
+
+Run:
+```bash
+mamba run -n roboimi python -m py_compile \
+  roboimi/vla/eval_utils.py \
+  roboimi/vla/models/heads/transformer1d.py \
+  roboimi/vla/agent.py \
+  roboimi/demos/vla_scripts/train_vla.py \
+  roboimi/demos/vla_scripts/eval_vla.py
+```
+Expected: no syntax errors
+
+- [ ] **Step 3: Run a local instantiation smoke test**
+
+Run:
+
+```bash
+/home/droid/.conda/envs/roboimi/bin/python - <<'PY'
+import torch
+from hydra import compose, initialize_config_dir
+from hydra.utils import instantiate
+from pathlib import Path
+
+config_dir = str((Path.cwd() / "roboimi" / "vla" / "conf").resolve())
+with initialize_config_dir(version_base=None, config_dir=config_dir):
+    cfg = compose(config_name="config", overrides=[
+        "agent=resnet_transformer",
+        "agent.vision_backbone.pretrained_backbone_weights=null",
+    ])
+
+agent = instantiate(cfg.agent, dataset_stats=None)
+images = {
+    "r_vis": torch.rand(1, cfg.agent.obs_horizon, 3, 224, 224),
+    "top": torch.rand(1, cfg.agent.obs_horizon, 3, 224, 224),
+    "front": torch.rand(1, cfg.agent.obs_horizon, 3, 224, 224),
+}
+qpos = torch.rand(1, cfg.agent.obs_horizon, cfg.agent.obs_dim)
+out = agent.predict_action(images, qpos)
+assert out.shape == (1, cfg.agent.pred_horizon, cfg.agent.action_dim), out.shape
+print("smoke_shape=", out.shape)
+PY
+```
+
+that:
+- instantiates `agent=resnet_transformer`
+- constructs fake three-camera input (`r_vis`, `top`, `front`)
+- calls `agent.predict_action(...)`
+- verifies output shape is `(1, pred_horizon, 16)`
+
+Expected: PASS
diff --git a/docs/superpowers/plans/2026-03-30-vla-experiment-sweep-execution.md b/docs/superpowers/plans/2026-03-30-vla-experiment-sweep-execution.md
new file mode 100644
index 0000000..da043f1
--- /dev/null
+++ b/docs/superpowers/plans/2026-03-30-vla-experiment-sweep-execution.md
@@ -0,0 +1,202 @@
+# VLA Experiment Sweep Execution Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Probe the largest safe shared batch size, prepare the 10 unique VLA sweep runs, and launch the serial GPU experiment queue with SwanLab logging and 20-epoch headless rollout validation.
+
+**Architecture:** Use the existing `train_vla.py` / `eval_vla.py` path without changing model code unless launch-blocking issues appear. A small external launcher contract will manage per-run directories, logs, pids, and serial execution while Hydra places trainer-local `checkpoints/` under each run directory.
+
+**Tech Stack:** zsh, mamba env `roboimi`, Hydra overrides, PyTorch CUDA, SwanLab, existing `train_vla.py`
+
+---
+
+### Task 1: Reconfirm schedule math and current runtime assumptions
+
+**Files:**
+- Modify: `docs/superpowers/specs/2026-03-30-vla-experiment-sweep-design.md` (only if execution reveals a spec mismatch)
+- Test: none
+
+- [ ] **Step 1: Print dataset size and epoch math for the baseline batch size**
+
+Run:
+```bash
+python - <<'PY'
+num_samples = 70000
+for batch_size in (32, 48, 64, 80):
+    steps_per_epoch = num_samples // batch_size
+    print(batch_size, steps_per_epoch, 20 * steps_per_epoch, 200 * steps_per_epoch)
+PY
+```
+
+Expected: integer epoch-step mappings for candidate batch sizes.
+
+- [ ] **Step 2: Reconfirm no stale training process is already active**
+
+Run:
+```bash
+ps -ef | grep -E 'train_vla.py|python .*roboimi/demos/vla_scripts/train_vla.py' | grep -v grep || true
+```
+
+Expected: no active training process unless intentionally started by this session.
+
+- [ ] **Step 3: Record the execution assumptions**
+
+Capture in session notes:
+- dataset path
+- camera list
+- current branch
+- no active conflicting training process
+
+
+### Task 2: Probe the largest safe shared batch size
+
+**Files:**
+- Create: `runs/batch-probe-<timestamp>/` (runtime artifact)
+- Test: probe command output only
+
+- [ ] **Step 1: Write the candidate probe command for the largest model**
+
+Use overrides:
+- `data.dataset_dir=/home/droid/project/diana_sim/sim_transfer`
+- `data.camera_names=[r_vis,top,front]`
+- `agent.head.n_emb=384`
+- `agent.head.n_layer=12`
+- `agent.head.n_head=4`
+- `agent.vision_backbone.pretrained_backbone_weights=IMAGENET1K_V1`
+- `agent.vision_backbone.freeze_backbone=true`
+- `agent.vision_backbone.use_separate_rgb_encoder_per_camera=true`
+- `train.device=cuda`
+- `train.val_split=0.0`
+- `train.seed=42`
+- `train.use_swanlab=false`
+- `train.rollout_val_freq_epochs=0`
+- `train.rollout_validate_on_checkpoint=false`
+- `train.max_steps=4`
+
+- [ ] **Step 2: Run the probe for `batch_size=32`**
+
+Expected pass condition:
+- no CUDA OOM
+- no crash/abort
+- no NaN/Inf loss
+- 4 steps complete
+
+- [ ] **Step 3: Repeat for `batch_size=48`, `64`, then optionally `80`**
+
+Stop increasing once one candidate fails.
+
+- [ ] **Step 4: Compute the shared LR and 200-epoch step budget**
+
+Using:
+```text
+lr = 1e-4 * (batch_size / 16)
+max_steps = 200 * floor(70000 / batch_size)
+```
+
+- [ ] **Step 5: Write down the chosen shared batch size and derived max_steps**
+
+This becomes the execution contract for all runs.
+
+
+### Task 3: Materialize the 10 unique run matrix
+
+**Files:**
+- Create: `runs/vla-sweep-<timestamp>/manifest.txt` (runtime artifact)
+- Create: `runs/vla-sweep-<timestamp>/launch_queue.sh` (runtime artifact)
+- Test: shell syntax / manifest inspection
+
+- [ ] **Step 1: Enumerate the 2-point pretraining comparison**
+
+Canonical runs:
+1. `sim-transfer-baseline-pretrain-on-emb128-layer4`
+2. `sim-transfer-pretrain-off`
+
+- [ ] **Step 2: Enumerate the remaining 8 unique architecture-sweep runs**
+
+Skip the duplicate `(pretrain-on, emb128, layer4)` because it reuses the baseline run.
+
+- [ ] **Step 3: For each run, define exact Hydra overrides**
+
+Every run must pin:
+- dataset path
+- three cameras
+- `train.device=cuda`
+- `train.val_split=0.0`
+- `train.num_workers=12`
+- `train.seed=42`
+- `train.rollout_val_freq_epochs=20`
+- `train.rollout_validate_on_checkpoint=false`
+- `train.rollout_num_episodes=3`
+- `train.use_swanlab=true`
+- a unique `train.swanlab_run_name`
+- shared batch size
+- shared LR
+- derived `max_steps`
+- `agent.head.n_head=4`
+- `agent.vision_backbone.freeze_backbone=true`
+- `agent.vision_backbone.use_separate_rgb_encoder_per_camera=true`
+
+- [ ] **Step 4: Write the queue launcher script**
+
+Launcher responsibilities:
+- create run directory
+- export `LD_LIBRARY_PATH` CUDA/cuDNN fixup
+- export `MPLCONFIGDIR=/tmp/mpl`
+- set `hydra.job.chdir=true`
+- set `hydra.run.dir=<run_dir>`
+- tee logs to `<run_dir>/train.log`
+- write `<run_dir>/train.pid`
+- run jobs serially
+
+- [ ] **Step 5: Verify the queue script syntax**
+
+Run:
+```bash
+zsh -n runs/vla-sweep-<timestamp>/launch_queue.sh
+```
+
+Expected: exit 0.
+
+
+### Task 4: Launch the first probe-selected production run and verify startup
+
+**Files:**
+- Create: `runs/vla-sweep-<timestamp>/<run-name>/` (runtime artifact)
+- Test: live log inspection / process inspection
+
+- [ ] **Step 1: Start the first run in the queue**
+
+Use the generated launcher contract.
+
+- [ ] **Step 2: Inspect the first 100-200 log lines**
+
+Expected:
+- config resolves correctly
+- dataset loads
+- agent initializes on CUDA
+- training loop starts
+- SwanLab initializes
+
+- [ ] **Step 3: Confirm process identity and run directory**
+
+Collect:
+- PID
+- run dir
+- log path
+- SwanLab run name
+
+- [ ] **Step 4: If startup is clean, continue the remaining serial queue**
+
+If startup fails, stop and debug before launching more runs.
+
+
+### Task 5: Report the launched sweep contract back to the user
+
+**Files:**
+- Test: log evidence only
+
+- [ ] **Step 1: Summarize the chosen batch size, LR, max_steps, and rollout cadence**
+
+- [ ] **Step 2: Provide the active run directory / pid / first-run status**
+
+- [ ] **Step 3: State the remaining queued runs by name**
diff --git a/docs/superpowers/plans/2026-03-30-vla-training-headless-swanlab.md b/docs/superpowers/plans/2026-03-30-vla-training-headless-swanlab.md
new file mode 100644
index 0000000..b5e23b6
--- /dev/null
+++ b/docs/superpowers/plans/2026-03-30-vla-training-headless-swanlab.md
@@ -0,0 +1,454 @@
+# VLA Training + Headless Rollout + SwanLab Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** 补齐当前 `Transformer1D` 训练依赖，在 `/home/droid/project/diana_sim/sim_transfer` 上启动训练，接入 SwanLab 标量日志，并提供训练期可选的 headless rollout validation 路径。
+
+**Architecture:** 保持现有 `train_vla.py` / `eval_vla.py` 主体不变，只做最小必要改造：补依赖、补 stats 生成入口、在训练脚本里加轻量 SwanLab logger 和可选 checkpoint-time rollout wrapper、在环境侧把图像更新与 GUI 显示解耦。训练默认仍走当前 `resnet_transformer + Transformer1D` 路线，rollout validation 作为薄封装默认关闭。
+
+**Tech Stack:** Python, mamba/conda, pip, PyTorch, Hydra, diffusers, torchvision, einops, SwanLab, MuJoCo, OpenCV, unittest
+
+---
+
+### Task 0: 执行前提与分支/环境确认
+
+**Files:**
+- Verify only
+
+- [ ] **Step 1: 确认当前分支仍是目标分支**
+
+Run: `git branch --show-current`
+Expected: `feat-align-dp-transformer-ee`
+
+- [ ] **Step 2: 记录当前 Python 解释器与环境名**
+
+Run: `/home/droid/.conda/envs/roboimi/bin/python - <<'PY'
+import sys
+print(sys.executable)
+PY`
+Expected: 输出 `/home/droid/.conda/envs/roboimi/bin/python`
+
+- [ ] **Step 3: 记录当前数据集目录存在性与 episode 数量**
+
+Run: `/usr/bin/zsh -lc 'echo DATASET=/home/droid/project/diana_sim/sim_transfer; find /home/droid/project/diana_sim/sim_transfer -maxdepth 1 -name "episode_*.hdf5" | wc -l'`
+Expected: 输出目录路径与 `100`
+
+### Task 1: 补齐训练依赖并把 resolved versions 写回环境定义
+
+**Files:**
+- Modify: `environment.yml`
+- Verify only: local `roboimi` env
+
+- [ ] **Step 1: 写出缺失依赖的最小清单**
+
+需要补齐：
+- `diffusers`
+- `torchvision`
+- `einops`
+- `swanlab`
+
+- [ ] **Step 2: 先用 dry-run 解析候选版本，确认不会升级 Torch**
+
+Run:
+```bash
+/home/droid/.conda/envs/roboimi/bin/python -m pip install --dry-run \
+  diffusers torchvision einops swanlab
+```
+Expected: 输出候选版本；若显示会升级/替换 `torch`，则停止并改用显式兼容版本
+
+- [ ] **Step 3: 安装与当前 Torch 兼容的缺失依赖到现有环境**
+
+Run:
+```bash
+/home/droid/.conda/envs/roboimi/bin/python -m pip install \
+  diffusers torchvision einops swanlab
+```
+Expected: 安装成功，且不替换当前 `torch==2.4.0`
+
+- [ ] **Step 4: 运行 import 验证**
+
+Run:
+```bash
+/home/droid/.conda/envs/roboimi/bin/python - <<'PY'
+mods=['torch','hydra','omegaconf','diffusers','torchvision','einops','cv2','h5py','swanlab','mujoco']
+for m in mods:
+    __import__(m)
+    print('OK', m)
+PY
+```
+Expected: 每个模块都输出 `OK <module>`
+
+- [ ] **Step 5: 记录实际安装版本**
+
+Run:
+```bash
+/home/droid/.conda/envs/roboimi/bin/python - <<'PY'
+import diffusers, torchvision, einops, swanlab
+print('diffusers', getattr(diffusers,'__version__',''))
+print('torchvision', getattr(torchvision,'__version__',''))
+print('einops', getattr(einops,'__version__',''))
+print('swanlab', getattr(swanlab,'__version__',''))
+PY
+```
+Expected: 输出四个包的 resolved versions
+
+- [ ] **Step 6: 将 resolved versions 写回 `environment.yml`**
+
+把新增依赖补到 `environment.yml` 的现有依赖列表（若使用 `pip:` 段则更新该段）里，使用 Step 5 得到的**实际 resolved versions**，避免环境漂移，并避免重复 package 条目。
+
+- [ ] **Step 7: 语法检查环境定义文件仅作结构确认**
+
+Run: `python - <<'PY'
+from pathlib import Path
+text = Path('environment.yml').read_text()
+assert 'diffusers' in text
+assert 'torchvision' in text
+assert 'einops' in text
+assert 'swanlab' in text
+print('environment.yml updated')
+PY`
+Expected: `environment.yml updated`
+
+### Task 2: 让统计脚本支持外部数据目录并生成 dataset stats
+
+**Files:**
+- Modify: `roboimi/vla/scripts/calculate_stats.py`
+- Test: `tests/test_calculate_stats_cli.py`
+
+- [ ] **Step 1: 写 failing test，验证统计脚本可接受外部 `--dataset_dir` 并输出目标路径**
+
+Test file should:
+- 用临时目录创建最小 HDF5 episode
+- 调用脚本入口/函数时传入外部目录
+- 断言输出 `dataset_stats.pkl` 出现在该目录
+- 断言 pickle 内包含 `action_mean/qpos_mean/...`
+
+- [ ] **Step 2: 跑测试确认它先失败**
+
+Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_calculate_stats_cli -v`
+Expected: FAIL（当前脚本写死默认目录）
+
+- [ ] **Step 3: 最小实现 `--dataset_dir` 支持**
+
+要求：
+- 保留现有统计逻辑
+- 仅增加 CLI 参数解析
+- 输出仍写入 `<dataset_dir>/dataset_stats.pkl`
+
+- [ ] **Step 4: 重新跑测试确认转绿**
+
+Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_calculate_stats_cli -v`
+Expected: PASS
+
+- [ ] **Step 5: 用真实数据集生成 stats**
+
+Run:
+```bash
+/home/droid/.conda/envs/roboimi/bin/python roboimi/vla/scripts/calculate_stats.py \
+  --dataset_dir /home/droid/project/diana_sim/sim_transfer
+```
+Expected: 生成 `/home/droid/project/diana_sim/sim_transfer/dataset_stats.pkl`
+
+- [ ] **Step 6: 验证 stats 文件结构**
+
+Run:
+```bash
+/home/droid/.conda/envs/roboimi/bin/python - <<'PY'
+import pickle
+path='/home/droid/project/diana_sim/sim_transfer/dataset_stats.pkl'
+with open(path,'rb') as f:
+    stats=pickle.load(f)
+for k in ['action_mean','action_std','action_min','action_max','qpos_mean','qpos_std','qpos_min','qpos_max']:
+    assert k in stats, k
+print('stats_ok')
+PY
+```
+Expected: `stats_ok`
+
+### Task 3: 增加 SwanLab 训练日志集成
+
+**Files:**
+- Modify: `roboimi/demos/vla_scripts/train_vla.py`
+- Modify: `roboimi/vla/conf/config.yaml`
+- Test: `tests/test_train_vla_swanlab_logging.py`
+
+- [ ] **Step 1: 写 failing test，验证训练脚本在 `train.use_swanlab=true` 时会初始化 SwanLab 并记录标量**
+
+Test should:
+- stub `swanlab`
+- 调用训练脚本中抽取出的非 Hydra helper（如 `_run_training(cfg)`）的最小路径（`max_steps=0` 或很小）
+- 断言调用了：
+  - `swanlab.init(project='roboimi-vla', ...)`
+  - 至少一次 `swanlab.log({...})`
+- 断言当 `use_swanlab=false` 时不会 import/初始化 SwanLab
+- 断言当 `use_swanlab=true` 且 import `swanlab` 失败时会 fail fast
+- 断言当 `use_swanlab=true` 且认证/登录状态不可用时会 fail fast
+- 断言会记录最终/最佳 checkpoint 路径到 log/summary
+
+- [ ] **Step 2: 跑测试确认它先失败**
+
+Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_train_vla_swanlab_logging -v`
+Expected: FAIL（当前无 SwanLab 集成）
+
+- [ ] **Step 3: 在配置中增加最小 SwanLab 契约**
+
+在 `roboimi/vla/conf/config.yaml` 添加：
+- `train.use_swanlab: true`
+- `train.swanlab_project: roboimi-vla`
+- 可选 `train.swanlab_run_name: null`
+
+- [ ] **Step 4: 从 Hydra 入口提取可测试的训练 helper**
+
+要求：
+- 新增类似 `_run_training(cfg)` 的普通函数
+- `main()` 只做 Hydra 入口转发
+- 测试只调用 helper，不直接调用 Hydra-decorated `main(cfg)`
+
+- [ ] **Step 5: 在训练脚本中实现 SwanLab 初始化的 fail-fast 逻辑**
+
+要求：
+- `use_swanlab=true` 时：
+  - import 失败 -> 直接报错
+  - 本地未登录/认证失败 -> 直接报错
+  - 成功后执行 `swanlab.init(project=cfg.train.swanlab_project, ...)`
+
+- [ ] **Step 6: 在训练脚本中实现轻量 scalar logger**
+
+要求：
+- 仅 scalar logging，不引入自定义 callback 框架
+- 训练时记录 `train/loss`, `train/lr`, `train/best_loss`, `train/step`
+- 验证时记录 `val/loss`
+- 训练结束时记录：
+  - `train/final_checkpoint_path`
+  - `train/best_checkpoint_path`
+- 若库支持则显式 `finish/close`
+
+- [ ] **Step 7: 重新跑测试确认转绿**
+
+Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_train_vla_swanlab_logging -v`
+Expected: PASS
+
+- [ ] **Step 8: 用提供的 API key 完成本地登录**
+
+Run:
+```bash
+/usr/bin/zsh -lc 'SWANLAB_API_KEY="<user-provided>"; /home/droid/.conda/envs/roboimi/bin/swanlab login -k "$SWANLAB_API_KEY"'
+```
+Expected: 登录成功并保存本地凭证
+
+### Task 4: 为评估/rollout 路径增加 headless 模式
+
+**Files:**
+- Modify: `roboimi/envs/double_base.py`
+- Modify: `roboimi/envs/double_pos_ctrl_env.py`
+- Modify: `roboimi/vla/conf/eval/eval.yaml`
+- Modify: `roboimi/demos/vla_scripts/eval_vla.py`
+- Test: `tests/test_eval_vla_headless.py`
+
+- [ ] **Step 1: 写 failing test，验证 headless 路径不触发 GUI 调用**
+
+Test should stub:
+- `cv2.namedWindow`
+- `cv2.imshow`
+- `cv2.waitKey`
+- viewer launch/render path
+
+And assert:
+- `eval.headless=true` 时不调用这些 GUI 接口
+- 仍能获取图像观测并走到 policy action 执行前/后关键路径
+
+- [ ] **Step 2: 跑测试确认它先失败**
+
+Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_eval_vla_headless -v`
+Expected: FAIL（当前 env/eval 默认会开 viewer 和 cv2 窗口）
+
+- [ ] **Step 3: 统一配置开关为 `eval.headless`**
+
+在 `roboimi/vla/conf/eval/eval.yaml` 添加：
+- `headless: false`
+
+不要再引入第二个同义开关。
+
+- [ ] **Step 4: 在 env 工厂中接入 `headless`**
+
+在 `make_sim_env(...)` 中：
+- `eval.headless=true` -> `is_render=False`
+- 保留图像观测更新
+- 不创建 MuJoCo viewer
+
+- [ ] **Step 5: 将相机更新与 GUI 显示解耦**
+
+在 `double_base.py`：
+- 图像更新逻辑保留
+- `cv2.namedWindow/imshow/waitKey` 仅在非 headless 下执行
+
+- [ ] **Step 6: eval 脚本中在 headless 下跳过 `env.render()`**
+
+只在 `eval.headless=false` 时调用 `env.render()`。
+
+- [ ] **Step 7: 重新跑测试确认转绿**
+
+Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_eval_vla_headless -v`
+Expected: PASS
+
+### Task 5: 给训练脚本加可选 checkpoint-time rollout validation 薄封装
+
+**Files:**
+- Modify: `roboimi/demos/vla_scripts/train_vla.py`
+- Possibly modify: `roboimi/demos/vla_scripts/eval_vla.py`（仅当需要提取可复用入口）
+- Modify: `roboimi/vla/conf/config.yaml`
+- Test: `tests/test_train_vla_rollout_validation.py`
+
+- [ ] **Step 1: 写 failing test，验证 checkpoint 保存点可选调用 rollout validation，且会传 `eval.headless=true`**
+
+Test should:
+- stub rollout/eval helper
+- 开启 `train.rollout_validate_on_checkpoint=true`
+- 设置小 `save_freq`
+- 断言训练脚本在 checkpoint 时调用验证 helper
+- 断言调用参数带 `headless=true`
+
+- [ ] **Step 2: 跑测试确认它先失败**
+
+Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_train_vla_rollout_validation -v`
+Expected: FAIL（当前无该 hook）
+
+- [ ] **Step 3: 增加最小配置键**
+
+在 `config.yaml` 中添加：
+- `train.rollout_validate_on_checkpoint: false`
+- `train.rollout_num_episodes: 1`
+
+- [ ] **Step 4: 提取一个最小 rollout validation helper 接口**
+
+要求：
+- helper 输入至少包括 `cfg`, `ckpt_path`, `num_episodes`, `headless`
+- 默认训练侧调用时强制 `headless=True`
+- 优先复用现有 eval 逻辑，不引入第二套 validator 类
+
+- [ ] **Step 5: 在 checkpoint 保存路径中接入 rollout helper**
+
+要求：
+- 不重写第二套验证框架
+- 优先复用现有 eval 逻辑/工具
+- 默认关闭
+- 仅在 checkpoint 时少量调用
+- 强制 `eval.headless=true`
+
+- [ ] **Step 6: 重新跑测试确认转绿**
+
+Run: `/home/droid/.conda/envs/roboimi/bin/python -m unittest tests.test_train_vla_rollout_validation -v`
+Expected: PASS
+
+### Task 6: 启动训练前的集成 smoke verification
+
+**Files:**
+- Verify only
+
+- [ ] **Step 1: 跑所有新增/相关测试**
+
+Run:
+```bash
+/home/droid/.conda/envs/roboimi/bin/python -m unittest \
+  tests.test_calculate_stats_cli \
+  tests.test_train_vla_swanlab_logging \
+  tests.test_eval_vla_headless \
+  tests.test_train_vla_rollout_validation -v
+```
+Expected: 全部 PASS
+
+- [ ] **Step 2: 对关键修改文件做语法检查**
+
+Run:
+```bash
+/home/droid/.conda/envs/roboimi/bin/python -m py_compile \
+  roboimi/vla/scripts/calculate_stats.py \
+  roboimi/demos/vla_scripts/train_vla.py \
+  roboimi/demos/vla_scripts/eval_vla.py \
+  roboimi/envs/double_pos_ctrl_env.py \
+  roboimi/envs/double_base.py
+```
+Expected: 无语法错误
+
+- [ ] **Step 3: 运行训练 smoke run**
+
+Run:
+```bash
+SWANLAB_API_KEY='<user-provided>' \
+/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
+  data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
+  train.max_steps=20 \
+  train.log_freq=1 \
+  train.save_freq=10 \
+  train.use_swanlab=true \
+  train.swanlab_project=roboimi-vla \
+  train.rollout_validate_on_checkpoint=false
+```
+Expected:
+- 训练启动成功
+- 产生 `checkpoints/vla_model_step_10.pt` 或 `vla_model_final.pt`
+- 本地日志中无 ImportError
+
+- [ ] **Step 4: 运行一个最小 headless rollout-validation smoke run**
+
+Run:
+```bash
+SWANLAB_API_KEY='<user-provided>' \
+/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
+  data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
+  train.max_steps=10 \
+  train.log_freq=1 \
+  train.save_freq=5 \
+  train.use_swanlab=true \
+  train.swanlab_project=roboimi-vla \
+  train.rollout_validate_on_checkpoint=true \
+  train.rollout_num_episodes=1 \
+  eval.headless=true
+```
+Expected:
+- 到达 checkpoint-time rollout 调用
+- 不弹 MuJoCo viewer
+- 不执行 `cv2.namedWindow/imshow/waitKey`
+
+- [ ] **Step 5: 验证 checkpoint 文件已写出**
+
+Run: `/usr/bin/zsh -lc 'ls -lah checkpoints | sed -n "1,120p"'`
+Expected: 存在 `vla_model_step_10.pt` 或 `vla_model_final.pt`
+
+- [ ] **Step 6: 验证 SwanLab 已收到标量**
+
+验证方式：
+- 终端日志中确认 `swanlab.init` / run URL / run id
+- 若工具支持，确认 dashboard 中 `roboimi-vla` 项目下出现本次 run
+
+### Task 7: 启动正式训练
+
+**Files:**
+- Verify only
+
+- [ ] **Step 1: 用真实配置启动正式训练**
+
+Run:
+```bash
+SWANLAB_API_KEY='<user-provided>' \
+/home/droid/.conda/envs/roboimi/bin/python roboimi/demos/vla_scripts/train_vla.py \
+  data.dataset_dir=/home/droid/project/diana_sim/sim_transfer \
+  train.use_swanlab=true \
+  train.swanlab_project=roboimi-vla \
+  train.rollout_validate_on_checkpoint=true \
+  eval.headless=true
+```
+Expected:
+- 训练持续运行
+- checkpoint 周期性写出
+- SwanLab 周期性收到 train/val 标量
+- 若 checkpoint-time rollout 打开，则不弹 GUI
+
+- [ ] **Step 2: 记录启动信息并向用户汇报**
+
+汇报内容至少包括：
+- 使用的数据集路径
+- 训练命令/关键 overrides
+- checkpoint 输出目录
+- SwanLab project 名称
+- rollout validation 是否已启用以及是否 headless
diff --git a/docs/superpowers/plans/2026-05-06-held-out-episode-validation.md b/docs/superpowers/plans/2026-05-06-held-out-episode-validation.md
new file mode 100644
index 0000000..a285b55
--- /dev/null
+++ b/docs/superpowers/plans/2026-05-06-held-out-episode-validation.md
@@ -0,0 +1,43 @@
+# Held-out Episode Validation Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Add optional held-out episode validation to main training flow, including explicit val episode selection and periodic action MSE evaluation without merging LEWM-only model features.
+
+**Architecture:** Extend the generic dataset with optional episode filtering metadata, extend train config with explicit held-out validation knobs, and wire train_vla to choose between random split and explicit episode split. Reuse agent.predict_action_chunk for action MSE so the metric stays model-agnostic.
+
+**Tech Stack:** Python, Hydra/OmegaConf, PyTorch, unittest
+
+---
+
+### Task 1: Add failing tests for dataset episode filtering and config semantics
+
+**Files:**
+- Modify: `tests/test_simple_robot_dataset_image_loading.py`
+- Modify: `tests/test_train_vla_rollout_validation.py`
+
+- [ ] Add dataset tests for `episode_indices` and `available_episode_indices`.
+- [ ] Add training tests for explicit held-out episode splitting and fail-fast config validation.
+- [ ] Run focused tests and verify they fail for the expected missing behavior.
+
+### Task 2: Implement minimal dataset and training support
+
+**Files:**
+- Modify: `roboimi/vla/data/simpe_robot_dataset.py`
+- Modify: `roboimi/vla/conf/config.yaml`
+- Modify: `roboimi/demos/vla_scripts/train_vla.py`
+
+- [ ] Add config keys `train.val_episode_indices` and `train.action_mse_val_freq_epochs`.
+- [ ] Add optional dataset filtering by episode index plus `available_episode_indices` metadata.
+- [ ] Add explicit train/val dataset builder and held-out action MSE computation.
+- [ ] Log `val/action_mse` only when explicit held-out episode validation is configured.
+
+### Task 3: Verify focused coverage
+
+**Files:**
+- Test: `tests/test_simple_robot_dataset_image_loading.py`
+- Test: `tests/test_train_vla_rollout_validation.py`
+- Test: `tests/test_train_vla_swanlab_logging.py`
+
+- [ ] Run focused unittest targets for dataset filtering, held-out MSE, and SwanLab logging.
+- [ ] Fix any regressions with minimal code changes.
diff --git a/docs/superpowers/specs/2026-03-30-vla-experiment-sweep-design.md b/docs/superpowers/specs/2026-03-30-vla-experiment-sweep-design.md
new file mode 100644
index 0000000..535cfb8
--- /dev/null
+++ b/docs/superpowers/specs/2026-03-30-vla-experiment-sweep-design.md
@@ -0,0 +1,242 @@
+# VLA Experiment Sweep Design
+
+## Goal
+
+Run a matched-configuration training sweep on `/home/droid/project/diana_sim/sim_transfer` for the aligned Transformer diffusion policy, comparing:
+
+1. ResNet ImageNet pretraining vs no pretraining under matched settings.
+2. Transformer width/depth choices across a 3×3 grid.
+
+The sweep should use the full dataset, headless rollout validation every 20 epochs, SwanLab logging, and a total budget of about 200 epochs per run.
+
+## Current Context
+
+- Dataset: `100` episodes, `70000` frame-level samples.
+- Cameras used as image conditions: `r_vis`, `top`, `front`.
+- Current policy family: ResNet visual encoder + Transformer DDPM head.
+- Current training path already supports:
+  - full-dataset training (`val_split=0.0`)
+  - checkpoint-time and epoch-time rollout hooks
+  - headless eval path
+  - SwanLab logging
+  - best-checkpoint selection by rollout reward once rollout metrics exist
+- Current trainer behavior that matters for execution:
+  - step checkpoints are written to `checkpoints/` relative to the Hydra job working directory
+  - epoch-time rollout validation is controlled by `train.rollout_val_freq_epochs`
+  - checkpoint-time rollout validation is separately controlled by `train.rollout_validate_on_checkpoint`
+  - per-run log files, pid files, and run directories must be created by the external launcher, not by the trainer itself
+
+## Derived Schedule
+
+For a chosen global batch size `B`, define:
+
+- `steps_per_epoch = floor(70000 / B)` because training currently uses `drop_last=True` when the train set is larger than the batch.
+- `rollout_freq_steps = 20 * steps_per_epoch`
+- `max_steps = 200 * steps_per_epoch`
+
+Example at `B = 32`:
+
+- `steps_per_epoch = 2187`
+- `rollout_freq_steps = 43740`
+- `max_steps = 437400`
+
+This schedule must be recomputed after the batch-size probe chooses the final shared batch size.
+
+## Experiment Strategy
+
+### Recommended approach
+
+Use one **shared batch size across all runs**, chosen by probing the largest model that will appear in the sweep. This preserves fairness while still using as much GPU memory as safely available.
+
+### Why not per-run max batch size
+
+Allowing each model to use a different batch size would add another changing variable, making the 11-run comparison harder to interpret.
+
+### Unique executions vs logical comparisons
+
+There are `11` logical comparison points in the user request (`2` for the pretraining comparison and `9` for the emb/layer grid), but only `10` **unique** training configurations because:
+
+- the pretrained baseline in Section A (`pretrained_backbone_weights=IMAGENET1K_V1`, `n_emb=128`, `n_layer=4`) is also one cell of the Section B sweep.
+
+This design will therefore run **10 unique training jobs**, and the pretrained-on baseline will be reused as the `(128, 4)` architecture-sweep point. This avoids wasting one full 200-epoch run on an identical configuration.
+
+The reused baseline has one canonical identity throughout execution:
+
+- run name stem: `sim-transfer-baseline-pretrain-on-emb128-layer4`
+- this same run is counted both as:
+  - the pretrained-on arm of the 2-point pretraining comparison
+  - the `(n_emb=128, n_layer=4)` point of the 9-point architecture sweep
+
+## Batch-Size Probe
+
+Before launching the full sweep:
+
+1. Probe the largest planned model:
+   - `n_emb=384`
+   - `n_layer=12`
+   - ResNet pretrained enabled
+2. Try candidate batch sizes in ascending order:
+   - `32`
+   - `48`
+   - `64`
+   - optionally `80` if `64` is clearly safe
+3. For each candidate, run a short GPU smoke training with:
+   - real dataset
+   - three cameras
+   - no SwanLab
+   - no rollout validation
+   - `max_steps=4`
+4. A candidate **passes** if all 4 steps finish without:
+   - CUDA OOM
+   - process crash / abort
+   - NaN / Inf loss
+5. Choose the **highest passing** candidate and use it for all sweep runs.
+
+Learning rate should scale linearly using the current training rule:
+
+- config baseline: `lr=1e-4` at `batch_size=16`
+- equivalent rule: `lr = 1e-4 * (batch_size / 16)`
+- equivalently: `lr = 2e-4 * (batch_size / 32)`
+
+## Experiment Matrix
+
+Total logical comparison points: `11`
+
+Total unique training executions: `10`
+
+### A. ResNet pretraining comparison (2 runs)
+
+Fixed Transformer settings:
+
+- `n_emb=128`
+- `n_layer=4`
+- `n_head=4`
+- `freeze_backbone=true`
+- `use_separate_rgb_encoder_per_camera=true`
+
+Interpretation note:
+
+- This comparison keeps the current frozen-backbone recipe unchanged.
+- Therefore, the `pretrained_backbone_weights=null` run measures **random frozen visual features vs ImageNet frozen visual features**.
+- It does **not** test end-to-end training of the ResNet from scratch; that would be a separate experiment.
+
+Runs:
+
+1. `pretrained_backbone_weights=IMAGENET1K_V1`
+2. `pretrained_backbone_weights=null`
+
+### B. Transformer architecture sweep (9 runs)
+
+Fixed visual backbone setting:
+
+- `pretrained_backbone_weights=IMAGENET1K_V1`
+- `freeze_backbone=true`
+- `use_separate_rgb_encoder_per_camera=true`
+- `n_head=4`
+
+Rationale:
+
+- The 9-point emb/layer sweep fixes `pretrained_backbone_weights=IMAGENET1K_V1`.
+- The separate 2-run comparison in Section A isolates the effect of ResNet pretraining at the baseline architecture.
+
+Grid:
+
+- `n_emb ∈ {128, 256, 384}`
+- `n_layer ∈ {4, 8, 12}`
+
+## Shared Training Configuration
+
+All full runs should use:
+
+- dataset: `/home/droid/project/diana_sim/sim_transfer`
+- cameras: `[r_vis, top, front]`
+- device: `cuda`
+- `val_split=0.0`
+- `num_workers=12`
+- `seed=42`
+- headless rollout validation
+- `rollout_val_freq_epochs=20`
+- `rollout_validate_on_checkpoint=false`
+- `rollout_num_episodes=3`
+- `use_swanlab=true`
+- shared batch size from probe
+- shared learning-rate rule from probe
+- `max_steps` recomputed to represent about `200` epochs
+
+Reproducibility note:
+
+- `train.seed=42` will be fixed and recorded for all runs.
+- Under the current `train_vla.py`, this does **not** make the runs bitwise deterministic when `val_split=0.0`, because model initialization and shuffled training order are not fully seeded by the trainer today.
+- The sweep is therefore treated as a **matched-config comparison**, not a strict deterministic benchmark.
+
+## Rollout / Validation Policy
+
+- No held-out val split will be used.
+- Loss remains a fallback best-model metric until the first rollout reward exists.
+- After rollout metrics begin, the best checkpoint should be chosen by **higher average rollout reward**.
+- Rollout should run with:
+  - no GUI
+  - CPU eval device
+  - current evaluation helper path
+
+## Execution Model
+
+Runs should be launched **serially**, not concurrently, because:
+
+- the user currently has one GPU available
+- concurrent jobs would reduce throughput and complicate memory management
+- serial execution preserves clean logs and easier resume semantics
+
+Each run gets:
+
+- a dedicated run directory under `runs/`
+- its own log file
+- its own pid / launcher metadata if backgrounded
+- a unique SwanLab run name
+- `hydra.job.chdir=true`
+- `hydra.run.dir=<run_dir>` so the trainer-local `checkpoints/` land inside the run directory
+
+## Naming Convention
+
+Suggested run name pattern:
+
+- pretraining comparison:
+  - `sim-transfer-baseline-pretrain-on-emb128-layer4-bs{B}-lr{LR}-e200`
+  - `sim-transfer-pretrain-off-bs{B}-lr{LR}-e200`
+- architecture sweep:
+  - `(128,4)` reuses `sim-transfer-baseline-pretrain-on-emb128-layer4-bs{B}-lr{LR}-e200`
+  - `sim-transfer-emb{E}-layer{L}-bs{B}-lr{LR}-e200`
+
+## Success Criteria
+
+The sweep is successful when:
+
+1. A shared safe batch size has been found.
+2. All 10 unique runs have launchable commands and isolated run directories.
+3. Each run trains for about 200 epochs worth of steps.
+4. Rollout validation occurs every 20 epochs.
+5. SwanLab records train metrics and rollout rewards.
+6. Final comparison can be made using best rollout average reward and training traces across:
+   - 2-point pretraining comparison
+   - 9-point emb/layer sweep
+
+## Non-Goals
+
+- No change to dataset format.
+- No change to model architecture beyond the requested pretraining toggle and Transformer width/depth sweep.
+- No attempt to equalize wall-clock time across models.
+- No multi-GPU scheduling.
+
+## Risks and Mitigations
+
+### Risk: larger models may OOM
+
+Mitigation: probe the largest model first and choose one shared safe batch size.
+
+### Risk: long runs may be interrupted
+
+Mitigation: use per-run directories, periodic checkpoints, and resumable launch commands.
+
+### Risk: rollout validation increases total training time
+
+Mitigation: keep rollout headless, CPU-based, and limited to 3 episodes every 20 epochs.
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diff --git a/outputs/imf_attnres_eval_preview/init_states_goal8/init_states_first10_grid.png b/outputs/imf_attnres_eval_preview/init_states_goal8/init_states_first10_grid.png
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diff --git a/outputs/imf_attnres_eval_preview/init_states_goal8/poses_all50_summary.json b/outputs/imf_attnres_eval_preview/init_states_goal8/poses_all50_summary.json
new file mode 100644
index 0000000..96b0f9d
--- /dev/null
+++ b/outputs/imf_attnres_eval_preview/init_states_goal8/poses_all50_summary.json
@@ -0,0 +1,216 @@
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+}
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diff --git a/outputs/imf_attnres_eval_preview/init_states_goal8/poses_first10.json b/outputs/imf_attnres_eval_preview/init_states_goal8/poses_first10.json
new file mode 100644
index 0000000..0570ef7
--- /dev/null
+++ b/outputs/imf_attnres_eval_preview/init_states_goal8/poses_first10.json
@@ -0,0 +1,552 @@
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+]
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diff --git a/remote_rollouts/front_success_20260529/dp_cf60_success_front.mp4 b/remote_rollouts/front_success_20260529/dp_cf60_success_front.mp4
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index 0000000..7a5d994
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diff --git a/remote_rollouts/front_success_20260529/dp_cf60_success_summary.json b/remote_rollouts/front_success_20260529/dp_cf60_success_summary.json
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index 0000000..44f6f85
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diff --git a/remote_rollouts/front_success_20260529/imf_5880_success_front.mp4 b/remote_rollouts/front_success_20260529/imf_5880_success_front.mp4
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index 0000000..da1464d
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diff --git a/remote_rollouts/front_success_20260529/imf_5880_success_summary.json b/remote_rollouts/front_success_20260529/imf_5880_success_summary.json
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index 0000000..0abef79
--- /dev/null
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diff --git a/remote_rollouts/rollout-step83124-front-5x-20260403-090744/aggregate_summary.json b/remote_rollouts/rollout-step83124-front-5x-20260403-090744/aggregate_summary.json
new file mode 100644
index 0000000..487b471
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diff --git a/remote_rollouts/rollout-step83124-front-5x-20260403-090744/ep01/rollout_front.mp4 b/remote_rollouts/rollout-step83124-front-5x-20260403-090744/ep01/rollout_front.mp4
new file mode 100644
index 0000000..6db08d5
Binary files /dev/null and b/remote_rollouts/rollout-step83124-front-5x-20260403-090744/ep01/rollout_front.mp4 differ
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diff --git a/remote_rollouts/rollout-step83124-front-5x-20260403-090744/ep02/rollout_front.mp4 b/remote_rollouts/rollout-step83124-front-5x-20260403-090744/ep02/rollout_front.mp4
new file mode 100644
index 0000000..fe1792e
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new file mode 100644
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diff --git a/remote_rollouts/rollout-step83124-front-5x-20260403-090744/ep03/rollout_front.mp4 b/remote_rollouts/rollout-step83124-front-5x-20260403-090744/ep03/rollout_front.mp4
new file mode 100644
index 0000000..c9da1c7
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new file mode 100644
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new file mode 100644
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\ No newline at end of file
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new file mode 100644
index 0000000..effe710
Binary files /dev/null and b/remote_rollouts/scripted_success_20260529/sim_transfer_success_front.mp4 differ
diff --git a/remote_rollouts/scripted_success_20260529/sim_transfer_summary.json b/remote_rollouts/scripted_success_20260529/sim_transfer_summary.json
new file mode 100644
index 0000000..13d8fb1
--- /dev/null
+++ b/remote_rollouts/scripted_success_20260529/sim_transfer_summary.json
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\ No newline at end of file
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new file mode 100644
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Binary files /dev/null and b/remote_rollouts/scripted_success_20260529/socket_peg_success_front.mp4 differ
diff --git a/remote_rollouts/scripted_success_20260529/socket_peg_summary.json b/remote_rollouts/scripted_success_20260529/socket_peg_summary.json
new file mode 100644
index 0000000..68ad3f7
--- /dev/null
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_001/front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_001/front.mp4
new file mode 100644
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_001/summary.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_001/summary.json
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_002/front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_002/front.mp4
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index 0000000..e7fc6e6
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_002/summary.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_002/summary.json
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_003/front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_003/front.mp4
new file mode 100644
index 0000000..6c14eed
Binary files /dev/null and b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_003/front.mp4 differ
diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_003/summary.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_003/summary.json
new file mode 100644
index 0000000..65adb46
--- /dev/null
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_004/front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_004/front.mp4
new file mode 100644
index 0000000..94e488f
Binary files /dev/null and b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_004/front.mp4 differ
diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_004/summary.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_004/summary.json
new file mode 100644
index 0000000..0cc8cc2
--- /dev/null
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_005/front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_005/front.mp4
new file mode 100644
index 0000000..668113d
Binary files /dev/null and b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_005/front.mp4 differ
diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_005/summary.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_005/summary.json
new file mode 100644
index 0000000..759144e
--- /dev/null
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_006/front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_006/front.mp4
new file mode 100644
index 0000000..563739d
Binary files /dev/null and b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_006/front.mp4 differ
diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_006/summary.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_006/summary.json
new file mode 100644
index 0000000..c12d8e0
--- /dev/null
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_007/front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_007/front.mp4
new file mode 100644
index 0000000..696b15d
Binary files /dev/null and b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_007/front.mp4 differ
diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_007/summary.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_007/summary.json
new file mode 100644
index 0000000..3d20cf3
--- /dev/null
+++ b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_007/summary.json
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_008/front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_008/front.mp4
new file mode 100644
index 0000000..999dc2e
Binary files /dev/null and b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_008/front.mp4 differ
diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_008/summary.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_008/summary.json
new file mode 100644
index 0000000..79e8d15
--- /dev/null
+++ b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempt_008/summary.json
@@ -0,0 +1,9822 @@
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/attempts_summary.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempts_summary.json
new file mode 100644
index 0000000..b46bdbd
--- /dev/null
+++ b/remote_rollouts/sim_transfer_reward4_20260601/dp/attempts_summary.json
@@ -0,0 +1,78678 @@
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/resolved_config.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/resolved_config.json
new file mode 100644
index 0000000..1985953
--- /dev/null
+++ b/remote_rollouts/sim_transfer_reward4_20260601/dp/resolved_config.json
@@ -0,0 +1,191 @@
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/success_front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/dp/success_front.mp4
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index 0000000..999dc2e
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/success_result.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/success_result.json
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index 0000000..d44936f
--- /dev/null
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/dp/success_summary.json b/remote_rollouts/sim_transfer_reward4_20260601/dp/success_summary.json
new file mode 100644
index 0000000..79e8d15
--- /dev/null
+++ b/remote_rollouts/sim_transfer_reward4_20260601/dp/success_summary.json
@@ -0,0 +1,9822 @@
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_001/front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_001/front.mp4
new file mode 100644
index 0000000..a127f0f
Binary files /dev/null and b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_001/front.mp4 differ
diff --git a/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_001/summary.json b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_001/summary.json
new file mode 100644
index 0000000..661267c
--- /dev/null
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_002/front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_002/front.mp4
new file mode 100644
index 0000000..7226672
Binary files /dev/null and b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_002/front.mp4 differ
diff --git a/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_002/summary.json b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_002/summary.json
new file mode 100644
index 0000000..9e229ca
--- /dev/null
+++ b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_002/summary.json
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_003/front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_003/front.mp4
new file mode 100644
index 0000000..36c01c4
Binary files /dev/null and b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_003/front.mp4 differ
diff --git a/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_003/summary.json b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_003/summary.json
new file mode 100644
index 0000000..c61a4a9
--- /dev/null
+++ b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempt_003/summary.json
@@ -0,0 +1,9822 @@
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/imf/attempts_summary.json b/remote_rollouts/sim_transfer_reward4_20260601/imf/attempts_summary.json
new file mode 100644
index 0000000..e04e70b
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new file mode 100644
index 0000000..a4c0c17
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/imf/success_front.mp4 b/remote_rollouts/sim_transfer_reward4_20260601/imf/success_front.mp4
new file mode 100644
index 0000000..36c01c4
Binary files /dev/null and b/remote_rollouts/sim_transfer_reward4_20260601/imf/success_front.mp4 differ
diff --git a/remote_rollouts/sim_transfer_reward4_20260601/imf/success_result.json b/remote_rollouts/sim_transfer_reward4_20260601/imf/success_result.json
new file mode 100644
index 0000000..d60aac3
--- /dev/null
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diff --git a/remote_rollouts/sim_transfer_reward4_20260601/imf/success_summary.json b/remote_rollouts/sim_transfer_reward4_20260601/imf/success_summary.json
new file mode 100644
index 0000000..c61a4a9
--- /dev/null
+++ b/remote_rollouts/sim_transfer_reward4_20260601/imf/success_summary.json
@@ -0,0 +1,9822 @@
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diff --git a/remote_rollouts/socket_reward4_20260601/dp_socket_reward4_front.mp4 b/remote_rollouts/socket_reward4_20260601/dp_socket_reward4_front.mp4
new file mode 100644
index 0000000..2a28241
Binary files /dev/null and b/remote_rollouts/socket_reward4_20260601/dp_socket_reward4_front.mp4 differ
diff --git a/remote_rollouts/socket_reward4_20260601/dp_socket_reward4_summary.json b/remote_rollouts/socket_reward4_20260601/dp_socket_reward4_summary.json
new file mode 100644
index 0000000..4ea9c14
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diff --git a/remote_rollouts/socket_reward4_20260601/imf_socket_reward4_front.mp4 b/remote_rollouts/socket_reward4_20260601/imf_socket_reward4_front.mp4
new file mode 100644
index 0000000..dd5aa14
Binary files /dev/null and b/remote_rollouts/socket_reward4_20260601/imf_socket_reward4_front.mp4 differ
diff --git a/remote_rollouts/socket_reward4_20260601/imf_socket_reward4_summary.json b/remote_rollouts/socket_reward4_20260601/imf_socket_reward4_summary.json
new file mode 100644
index 0000000..2b80314
--- /dev/null
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diff --git a/remote_rollouts/socket_reward5_20260601/dp_socket_reward5_front.mp4 b/remote_rollouts/socket_reward5_20260601/dp_socket_reward5_front.mp4
new file mode 100644
index 0000000..c4536fe
Binary files /dev/null and b/remote_rollouts/socket_reward5_20260601/dp_socket_reward5_front.mp4 differ
diff --git a/remote_rollouts/socket_reward5_20260601/dp_socket_reward5_summary.json b/remote_rollouts/socket_reward5_20260601/dp_socket_reward5_summary.json
new file mode 100644
index 0000000..dda1da9
--- /dev/null
+++ b/remote_rollouts/socket_reward5_20260601/dp_socket_reward5_summary.json
@@ -0,0 +1,10457 @@
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diff --git a/remote_rollouts/socket_reward5_20260601/imf_socket_reward5_front.mp4 b/remote_rollouts/socket_reward5_20260601/imf_socket_reward5_front.mp4
new file mode 100644
index 0000000..05892f1
Binary files /dev/null and b/remote_rollouts/socket_reward5_20260601/imf_socket_reward5_front.mp4 differ
diff --git a/remote_rollouts/socket_reward5_20260601/imf_socket_reward5_summary.json b/remote_rollouts/socket_reward5_20260601/imf_socket_reward5_summary.json
new file mode 100644
index 0000000..b649a53
--- /dev/null
+++ b/remote_rollouts/socket_reward5_20260601/imf_socket_reward5_summary.json
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+      "inference_time_ms": 0.5402378737926483,
+      "env_step_time_ms": 34.74731091409922,
+      "held_video_frames": 1,
+      "raw_action_min": -106.83428192138672,
+      "raw_action_max": 0.9225777983665466,
+      "raw_action_mean": -12.565808296203613
+    },
+    {
+      "step": 713,
+      "reward": 4.0,
+      "episode_max_reward_so_far": 4.0,
+      "obs_read_time_ms": 2.160734962671995,
+      "preprocess_time_ms": 4.309981595724821,
+      "inference_time_ms": 0.5374480970203876,
+      "env_step_time_ms": 34.873147029429674,
+      "held_video_frames": 1,
+      "raw_action_min": -101.72437286376953,
+      "raw_action_max": 0.9282572269439697,
+      "raw_action_mean": -12.376388549804688
+    },
+    {
+      "step": 714,
+      "reward": 4.0,
+      "episode_max_reward_so_far": 4.0,
+      "obs_read_time_ms": 2.2453288547694683,
+      "preprocess_time_ms": 4.245377145707607,
+      "inference_time_ms": 0.5301889032125473,
+      "env_step_time_ms": 34.59979500621557,
+      "held_video_frames": 1,
+      "raw_action_min": -101.77128601074219,
+      "raw_action_max": 0.9271531105041504,
+      "raw_action_mean": -12.215265274047852
+    },
+    {
+      "step": 715,
+      "reward": 5.0,
+      "episode_max_reward_so_far": 5.0,
+      "obs_read_time_ms": 2.2361851297318935,
+      "preprocess_time_ms": 4.46841586381197,
+      "inference_time_ms": 0.5372241139411926,
+      "env_step_time_ms": 35.077269189059734,
+      "held_video_frames": 1,
+      "raw_action_min": -105.65049743652344,
+      "raw_action_max": 0.9280983209609985,
+      "raw_action_mean": -12.77689266204834
+    }
+  ]
+}
\ No newline at end of file
diff --git a/roboimi/demos/vla_scripts/eval_vla.py b/roboimi/demos/vla_scripts/eval_vla.py
index fc30df2..80f882c 100644
--- a/roboimi/demos/vla_scripts/eval_vla.py
+++ b/roboimi/demos/vla_scripts/eval_vla.py
@@ -113,6 +113,7 @@ def prepare_observation(
     obs: Dict,
     camera_names: list,
     image_resize_shape: Optional[tuple[int, int]] = (224, 224),
+    task_description: Optional[str] = None,
 ) -> Dict:
     """
     将环境观测转换为 agent 格式。
@@ -139,7 +140,32 @@ def prepare_observation(
     # 转换 qpos: numpy -> tensor
     qpos = torch.from_numpy(obs['qpos']).float()
 
-    return {'qpos': qpos, 'images': images}
+    observation = {'qpos': qpos, 'images': images}
+    if 'task' in obs:
+        observation['task'] = obs['task']
+    elif task_description is not None:
+        observation['task'] = task_description
+    return observation
+
+
+def _normalize_resize_shape(shape) -> Optional[tuple[int, int]]:
+    if shape is None:
+        return None
+    normalized = tuple(int(v) for v in shape)
+    if len(normalized) != 2:
+        raise ValueError(f'image resize shape must contain exactly two values, got {normalized}')
+    return normalized
+
+
+def _resolve_eval_image_resize_shape(cfg: DictConfig) -> Optional[tuple[int, int]]:
+    image_resize_shape = cfg.get('data', {}).get('image_resize_shape', (224, 224))
+    agent_cfg = cfg.agent
+    for backbone_key in ('vision_backbone', 'condition_encoder'):
+        backbone_cfg = agent_cfg.get(backbone_key, None)
+        if backbone_cfg is not None and 'eval_image_resize_shape' in backbone_cfg:
+            image_resize_shape = backbone_cfg.get('eval_image_resize_shape')
+            break
+    return _normalize_resize_shape(image_resize_shape)
 
 
 def _resolve_policy_camera_names(cfg: DictConfig) -> list[str]:
@@ -159,6 +185,7 @@ def _new_local_policy_queues(obs_horizon: int) -> dict[str, deque]:
     return {
         'qpos': deque(maxlen=int(obs_horizon)),
         'images': deque(maxlen=int(obs_horizon)),
+        'task': deque(maxlen=int(obs_horizon)),
         'action': deque(),
     }
 
@@ -174,6 +201,8 @@ def _populate_local_policy_queues(
             camera_name: image.detach().clone()
             for camera_name, image in observation['images'].items()
         })
+    if 'task' in observation:
+        queues['task'].append(observation['task'])
 
 
 def _prepare_local_policy_batch(
@@ -202,7 +231,10 @@ def _prepare_local_policy_batch(
         ).unsqueeze(0)
         for camera_name in ordered_camera_names
     }
-    return {'qpos': batch_qpos, 'images': batch_images}
+    batch = {'qpos': batch_qpos, 'images': batch_images}
+    if queues.get('task'):
+        batch['task'] = [list(queues['task'])[-1]]
+    return batch
 
 
 def _enqueue_predicted_actions(
@@ -226,23 +258,29 @@ def _enqueue_predicted_actions(
 
 
 def _serialize_policy_batch(batch: Dict[str, torch.Tensor]) -> dict[str, Any]:
-    return {
+    serialized = {
         'qpos': batch['qpos'].detach().cpu().numpy().astype(np.float32, copy=True),
         'images': {
             camera_name: image.detach().cpu().numpy().astype(np.float32, copy=True)
             for camera_name, image in batch['images'].items()
         },
     }
+    if 'task' in batch:
+        serialized['task'] = batch['task']
+    return serialized
 
 
 def _deserialize_policy_batch(batch: dict[str, Any], device: str) -> Dict[str, torch.Tensor]:
-    return {
+    deserialized = {
         'qpos': torch.as_tensor(batch['qpos'], dtype=torch.float32, device=device),
         'images': {
             camera_name: torch.as_tensor(image, dtype=torch.float32, device=device)
             for camera_name, image in batch['images'].items()
         },
     }
+    if 'task' in batch:
+        deserialized['task'] = batch['task']
+    return deserialized
 
 
 class _LocalPolicyRunner:
@@ -986,7 +1024,7 @@ def _print_eval_config(cfg: DictConfig):
 
 def _build_episode_plans(
     num_episodes: int,
-    box_poses: Optional[list[np.ndarray]] = None,
+    box_poses: Optional[list[Any]] = None,
 ) -> list[dict[str, Any]]:
     if box_poses is None:
         return [
@@ -998,12 +1036,25 @@ def _build_episode_plans(
     return [
         {
             'episode_index': int(episode_index),
-            'box_pos': np.asarray(box_pos, dtype=np.float32).copy(),
+            'box_pos': _json_friendly(np.asarray(box_pos, dtype=np.float32).copy()),
         }
         for episode_index, box_pos in enumerate(box_poses)
     ]
 
 
+def _episode_plan_reset_state(plan: dict[str, Any], task_name: str):
+    if 'task_state' in plan:
+        return _json_friendly(plan['task_state'])
+
+    if str(task_name) == 'sim_air_insert_socket_peg':
+        return _sample_task_reset_state(str(task_name))
+
+    task_state = plan.get('box_pos')
+    if task_state is None:
+        return _sample_task_reset_state(str(task_name))
+    return np.asarray(task_state, dtype=np.float32)
+
+
 def _run_eval_episode_plans(
     cfg: DictConfig,
     episode_plans: list[dict[str, Any]],
@@ -1015,6 +1066,8 @@ def _run_eval_episode_plans(
     eval_cfg = cfg.eval
     device = str(eval_cfg.device)
     camera_names = list(eval_cfg.camera_names)
+    image_resize_shape = _resolve_eval_image_resize_shape(cfg)
+    task_description = eval_cfg.get('task_description', None)
     artifact_paths = artifact_paths or _resolve_artifact_paths(eval_cfg)
     video_recorder = _RolloutVideoRecorder(
         output_path=artifact_paths['video_mp4'],
@@ -1041,12 +1094,7 @@ def _run_eval_episode_plans(
         episode_max_rewards = []
         for plan in episode_plans:
             episode_idx = int(plan['episode_index'])
-            task_state = plan.get('box_pos')
-            if task_state is None:
-                task_state = _sample_task_reset_state(str(eval_cfg.task_name))
-            elif isinstance(task_state, np.ndarray):
-                task_state = np.asarray(task_state, dtype=np.float32)
-
+            task_state = _episode_plan_reset_state(plan, str(eval_cfg.task_name))
 
             if show_progress:
                 print(f"\n{'='*60}")
@@ -1089,7 +1137,12 @@ def _run_eval_episode_plans(
                     video_recorder.write(video_frame)
 
                     # 准备给 agent 的观测
-                    observation = prepare_observation(obs, camera_names)
+                    observation = prepare_observation(
+                        obs,
+                        camera_names,
+                        image_resize_shape=image_resize_shape,
+                        task_description=task_description,
+                    )
                     end_preprocess = time.perf_counter()
 
                     # 选择动作（本地 agent 或远端 inference server）
@@ -1497,7 +1550,9 @@ def _build_parallel_worker_payloads(
         num_episodes=int(eval_cfg.num_episodes),
         num_workers=requested_workers,
     )
-    box_poses = _plan_episode_box_poses(int(eval_cfg.num_episodes))
+    box_poses = None
+    if 'sim_transfer' in str(eval_cfg.task_name):
+        box_poses = _plan_episode_box_poses(int(eval_cfg.num_episodes))
     resolved_cfg = OmegaConf.to_container(cfg, resolve=True)
     payloads = []
     workers_dir = None
@@ -1523,7 +1578,15 @@ def _build_parallel_worker_payloads(
             'episode_plans': [
                 {
                     'episode_index': int(episode_index),
-                    'box_pos': np.asarray(box_poses[episode_index], dtype=np.float32).tolist(),
+                    **(
+                        {
+                            'box_pos': _json_friendly(
+                                np.asarray(box_poses[episode_index], dtype=np.float32).copy()
+                            ),
+                        }
+                        if box_poses is not None
+                        else {}
+                    ),
                 }
                 for episode_index in episode_indices
             ],
diff --git a/roboimi/demos/vla_scripts/realtime_front_success_rollout.py b/roboimi/demos/vla_scripts/realtime_front_success_rollout.py
new file mode 100644
index 0000000..e9574e6
--- /dev/null
+++ b/roboimi/demos/vla_scripts/realtime_front_success_rollout.py
@@ -0,0 +1,474 @@
+"""Record realtime-ish front-view rollout videos for socket insertion checkpoints.
+
+This script intentionally decouples video time from policy inference time.  During
+an inference call the environment is not advanced; the current front frame is
+written repeatedly.  Once the action is ready, exactly one environment action step
+is applied.  The resulting video therefore makes slow inference visible as
+holds/pauses, matching the user's requested visualization.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import shutil
+import sys
+import time
+from pathlib import Path
+from typing import Any, Optional
+
+# Set EGL before importing eval_vla/mujoco helpers on headless hosts.
+os.environ.setdefault("MUJOCO_GL", "egl")
+
+import numpy as np
+import torch
+from hydra import compose, initialize_config_dir
+from omegaconf import DictConfig, ListConfig, OmegaConf
+
+# Make direct script execution work from the repository root.
+sys.path.append(os.getcwd())
+
+from roboimi.demos.vla_scripts.eval_vla import (  # noqa: E402
+    ActionSmoother,
+    _close_env,
+    _get_video_frame,
+    _is_cuda_device,
+    _open_video_writer,
+    _resolve_policy_camera_names,
+    _sample_task_reset_state,
+    load_checkpoint,
+    prepare_observation,
+)
+from roboimi.envs.double_pos_ctrl_env import make_sim_env  # noqa: E402
+from roboimi.vla.eval_utils import execute_policy_action  # noqa: E402
+
+
+def _jsonable(value: Any) -> Any:
+    if isinstance(value, dict):
+        return {str(k): _jsonable(v) for k, v in value.items()}
+    if isinstance(value, (list, tuple)):
+        return [_jsonable(v) for v in value]
+    if isinstance(value, (np.integer, np.floating)):
+        return value.item()
+    if isinstance(value, np.ndarray):
+        return value.tolist()
+    if isinstance(value, Path):
+        return str(value)
+    return value
+
+
+def _parse_override_items(items: Optional[list[str]]) -> list[str]:
+    return [str(item) for item in (items or []) if str(item)]
+
+
+def _load_cfg(agent_name: str, overrides: list[str]) -> DictConfig:
+    config_dir = str((Path.cwd() / "roboimi" / "vla" / "conf").resolve())
+    with initialize_config_dir(version_base=None, config_dir=config_dir):
+        return compose(config_name="config", overrides=[f"agent={agent_name}", *overrides])
+
+
+def _listify(value: Any) -> list[str]:
+    if isinstance(value, (list, tuple, ListConfig)):
+        return [str(v) for v in value]
+    if isinstance(value, str):
+        # Be permissive for older SwanLab configs that persisted lists as strings.
+        stripped = value.strip()
+        if stripped.startswith("[") and stripped.endswith("]"):
+            inner = stripped[1:-1].strip()
+            if not inner:
+                return []
+            return [part.strip().strip("'\"") for part in inner.split(",")]
+        return [value]
+    return [str(value)]
+
+
+def _normalize_resize_shape(shape) -> Optional[tuple[int, int]]:
+    if shape is None:
+        return None
+    normalized = tuple(int(v) for v in shape)
+    if len(normalized) != 2:
+        raise ValueError(f"image resize shape must contain exactly two values, got {normalized}")
+    return normalized
+
+
+def _resolve_eval_image_resize_shape_local(cfg: DictConfig) -> Optional[tuple[int, int]]:
+    image_resize_shape = cfg.get("data", {}).get("image_resize_shape", (224, 224))
+    agent_cfg = cfg.agent
+    for backbone_key in ("vision_backbone", "condition_encoder"):
+        backbone_cfg = agent_cfg.get(backbone_key, None)
+        if backbone_cfg is not None and "eval_image_resize_shape" in backbone_cfg:
+            image_resize_shape = backbone_cfg.get("eval_image_resize_shape")
+            break
+    return _normalize_resize_shape(image_resize_shape)
+
+
+def _copy_success_artifacts(output_dir: Path, attempt_dir: Path) -> dict[str, str]:
+    final_video = output_dir / "success_front.mp4"
+    final_summary = output_dir / "success_summary.json"
+    src_video = attempt_dir / "front.mp4"
+    src_summary = attempt_dir / "summary.json"
+    if src_video.exists():
+        shutil.copy2(src_video, final_video)
+    if src_summary.exists():
+        shutil.copy2(src_summary, final_summary)
+    return {
+        "success_video": str(final_video),
+        "success_summary": str(final_summary),
+    }
+
+
+def _write_json(path: Path, payload: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", encoding="utf-8") as f:
+        json.dump(_jsonable(payload), f, ensure_ascii=False, indent=2)
+
+
+def _record_hold_frames(
+    writer,
+    frame: Optional[np.ndarray],
+    *,
+    elapsed_s: float,
+    video_fps: float,
+    min_frames: int = 1,
+) -> int:
+    if frame is None:
+        return 0
+    # Round up so an inference wait of 80 ms at 30 FPS becomes 3 held frames.
+    num_frames = max(int(min_frames), int(np.ceil(max(0.0, elapsed_s) * float(video_fps))))
+    for _ in range(num_frames):
+        writer.write(frame)
+    return num_frames
+
+
+def _run_one_attempt(
+    *,
+    cfg: DictConfig,
+    agent: torch.nn.Module,
+    attempt_dir: Path,
+    attempt_index: int,
+    args: argparse.Namespace,
+) -> dict[str, Any]:
+    eval_cfg = cfg.eval
+    camera_names = _resolve_policy_camera_names(cfg)
+    camera_names = _listify(camera_names)
+    if "front" not in camera_names:
+        raise ValueError(f"front camera is required for recording, got camera_names={camera_names}")
+
+    image_resize_shape = _resolve_eval_image_resize_shape_local(cfg)
+    task_name = str(eval_cfg.task_name)
+    task_state = _sample_task_reset_state(task_name)
+    video_path = attempt_dir / "front.mp4"
+    summary_path = attempt_dir / "summary.json"
+    attempt_dir.mkdir(parents=True, exist_ok=True)
+
+    env = None
+    writer = None
+    smoother = ActionSmoother(alpha=float(eval_cfg.get("smooth_alpha", 0.3))) if bool(eval_cfg.get("use_smoothing", False)) else None
+
+    episode_reward = 0.0
+    episode_max_reward = float("-inf")
+    inference_times_ms: list[float] = []
+    env_step_times_ms: list[float] = []
+    obs_read_times_ms: list[float] = []
+    preprocess_times_ms: list[float] = []
+    recorded_frames = 0
+    executed_steps = 0
+    reward_trace: list[float] = []
+    step_records: list[dict[str, Any]] = []
+
+    start_wall = time.perf_counter()
+    try:
+        env = make_sim_env(task_name, headless=True)
+        # Optional runtime override. The factory defaults socket insertion to 30 Hz;
+        # this script keeps that default unless the caller explicitly changes it.
+        if args.control_freq is not None and callable(getattr(env, "_initializeTime", None)):
+            env._initializeTime(float(args.control_freq))
+            if hasattr(env, "base_time"):
+                env.base_time = env.control_timestep
+
+        env.reset(task_state)
+        if callable(getattr(agent, "reset", None)):
+            agent.reset()
+        if smoother is not None:
+            smoother.reset()
+
+        obs = env._get_image_obs()
+        qpos_obs = env._get_qpos_obs()
+        obs["qpos"] = qpos_obs["qpos"]
+        frame = _get_video_frame(obs, "front")
+        if frame is None:
+            raise RuntimeError("front frame is unavailable")
+        writer = _open_video_writer(str(video_path), (int(frame.shape[1]), int(frame.shape[0])), int(args.video_fps))
+        writer.write(frame)
+        recorded_frames += 1
+
+        with torch.inference_mode():
+            for t in range(int(args.max_timesteps)):
+                iter_start = time.perf_counter()
+
+                obs_start = time.perf_counter()
+                obs = env._get_image_obs()
+                qpos_obs = env._get_qpos_obs()
+                obs["qpos"] = qpos_obs["qpos"]
+                obs_end = time.perf_counter()
+                current_frame = _get_video_frame(obs, "front")
+
+                prep_start = time.perf_counter()
+                observation = prepare_observation(
+                    obs,
+                    camera_names,
+                    image_resize_shape=image_resize_shape,
+                )
+                prep_end = time.perf_counter()
+
+                infer_start = time.perf_counter()
+                action = agent.select_action(observation)
+                if _is_cuda_device(str(eval_cfg.device)) and torch.cuda.is_available():
+                    torch.cuda.synchronize()
+                infer_end = time.perf_counter()
+                inference_s = infer_end - infer_start
+                hold_frames = _record_hold_frames(
+                    writer,
+                    current_frame,
+                    elapsed_s=inference_s,
+                    video_fps=float(args.video_fps),
+                    min_frames=int(args.min_hold_frames),
+                )
+                recorded_frames += hold_frames
+
+                raw_action = action.detach().cpu().numpy().astype(np.float32, copy=True) if isinstance(action, torch.Tensor) else np.asarray(action, dtype=np.float32).copy()
+                executed_action = raw_action.copy()
+                if smoother is not None:
+                    executed_action = smoother.smooth(executed_action)
+
+                step_start = time.perf_counter()
+                execute_policy_action(env, executed_action)
+                step_end = time.perf_counter()
+                executed_steps += 1
+
+                reward = getattr(env, "rew", None)
+                reward_value = float(reward) if reward is not None else 0.0
+                reward_trace.append(reward_value)
+                episode_reward += reward_value
+                episode_max_reward = max(episode_max_reward, reward_value)
+
+                obs_read_ms = (obs_end - obs_start) * 1000.0
+                preprocess_ms = (prep_end - prep_start) * 1000.0
+                inference_ms = inference_s * 1000.0
+                env_step_ms = (step_end - step_start) * 1000.0
+                obs_read_times_ms.append(obs_read_ms)
+                preprocess_times_ms.append(preprocess_ms)
+                inference_times_ms.append(inference_ms)
+                env_step_times_ms.append(env_step_ms)
+                step_records.append({
+                    "step": int(t),
+                    "reward": reward_value,
+                    "episode_max_reward_so_far": float(episode_max_reward),
+                    "obs_read_time_ms": obs_read_ms,
+                    "preprocess_time_ms": preprocess_ms,
+                    "inference_time_ms": inference_ms,
+                    "env_step_time_ms": env_step_ms,
+                    "held_video_frames": int(hold_frames),
+                    "raw_action_min": float(raw_action.min()),
+                    "raw_action_max": float(raw_action.max()),
+                    "raw_action_mean": float(raw_action.mean()),
+                })
+
+                if args.verbose_every > 0 and (t % int(args.verbose_every) == 0 or episode_max_reward > args.success_threshold):
+                    print(
+                        f"attempt={attempt_index} step={t} reward={reward_value:.1f} "
+                        f"max={episode_max_reward:.1f} infer={inference_ms:.1f}ms "
+                        f"hold_frames={hold_frames} video_frames={recorded_frames}",
+                        flush=True,
+                    )
+
+                if bool(args.stop_on_success) and episode_max_reward > float(args.success_threshold):
+                    # Add a short tail so the successful state remains visible.
+                    final_obs = env._get_image_obs()
+                    final_frame = _get_video_frame(final_obs, "front")
+                    tail_frames = int(round(float(args.success_tail_s) * float(args.video_fps)))
+                    for _ in range(max(0, tail_frames)):
+                        writer.write(final_frame)
+                    recorded_frames += max(0, tail_frames)
+                    break
+
+                # Safety: keep files bounded if a very slow model produces many duplicate frames.
+                if args.max_video_frames is not None and recorded_frames >= int(args.max_video_frames):
+                    print(
+                        f"attempt={attempt_index} reached max_video_frames={args.max_video_frames}; stopping attempt",
+                        flush=True,
+                    )
+                    break
+
+        wall_s = time.perf_counter() - start_wall
+        success = bool(episode_max_reward > float(args.success_threshold))
+        summary = {
+            "attempt_index": int(attempt_index),
+            "success": success,
+            "checkpoint": str(args.ckpt_path),
+            "agent": str(args.agent),
+            "task_name": task_name,
+            "task_state": task_state,
+            "video_path": str(video_path),
+            "video_camera": "front",
+            "video_fps": float(args.video_fps),
+            "recording_semantics": "Video duplicates the last observed front frame while inference runs; env.step is not called, so the simulation state is not advanced or modified during inference waits.",
+            "control_freq_hz": float(getattr(env, "control_freq", np.nan)),
+            "mujoco_model_timestep_s": float(getattr(env, "model_timestep", np.nan)),
+            "max_theoretical_physics_hz": float(1.0 / getattr(env, "model_timestep", np.nan)),
+            "executed_steps": int(executed_steps),
+            "recorded_frames": int(recorded_frames),
+            "video_duration_s": float(recorded_frames) / float(args.video_fps),
+            "wall_time_s": wall_s,
+            "episode_reward": float(episode_reward),
+            "episode_max_reward": float(episode_max_reward) if episode_max_reward != float("-inf") else None,
+            "avg_inference_time_ms": float(np.mean(inference_times_ms)) if inference_times_ms else 0.0,
+            "avg_inference_fps": float(1000.0 / np.mean(inference_times_ms)) if inference_times_ms and np.mean(inference_times_ms) > 0 else 0.0,
+            "avg_env_step_time_ms": float(np.mean(env_step_times_ms)) if env_step_times_ms else 0.0,
+            "avg_obs_read_time_ms": float(np.mean(obs_read_times_ms)) if obs_read_times_ms else 0.0,
+            "avg_preprocess_time_ms": float(np.mean(preprocess_times_ms)) if preprocess_times_ms else 0.0,
+            "inference_time_ms_min": float(np.min(inference_times_ms)) if inference_times_ms else 0.0,
+            "inference_time_ms_max": float(np.max(inference_times_ms)) if inference_times_ms else 0.0,
+            "reward_trace": reward_trace,
+            "steps": step_records,
+        }
+        _write_json(summary_path, summary)
+        print(
+            f"attempt={attempt_index} done success={success} reward={episode_reward:.1f} "
+            f"max_reward={summary['episode_max_reward']} video={video_path}",
+            flush=True,
+        )
+        return summary
+    finally:
+        if writer is not None:
+            writer.release()
+        _close_env(env)
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--ckpt-path", required=True)
+    parser.add_argument("--agent", required=True, help="Hydra agent config name, e.g. resnet_imf_attnres or resnet_transformer")
+    parser.add_argument("--output-dir", required=True)
+    parser.add_argument("--task-name", default="sim_air_insert_socket_peg")
+    parser.add_argument("--dataset-dir", default=None)
+    parser.add_argument("--camera-names", default=None, help="Comma-separated policy camera names. Defaults depend on task.")
+    parser.add_argument("--max-attempts", type=int, default=80)
+    parser.add_argument("--max-timesteps", type=int, default=750)
+    parser.add_argument("--success-threshold", type=float, default=4.0)
+    parser.add_argument("--video-fps", type=float, default=30.0)
+    parser.add_argument("--min-hold-frames", type=int, default=1)
+    parser.add_argument("--success-tail-s", type=float, default=2.0)
+    parser.add_argument("--max-video-frames", type=int, default=None)
+    parser.add_argument("--control-freq", type=float, default=None, help="Optional env control frequency override. Default uses make_sim_env default (30 Hz for socket).")
+    parser.add_argument("--device", default="cuda")
+    parser.add_argument("--seed", type=int, default=42)
+    parser.add_argument("--stop-on-success", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--verbose-every", type=int, default=50)
+    parser.add_argument("overrides", nargs="*", help="Additional Hydra overrides, e.g. agent.inference_steps=3")
+    args = parser.parse_args()
+
+    np.random.seed(int(args.seed))
+    torch.manual_seed(int(args.seed))
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(int(args.seed))
+
+    output_dir = Path(args.output_dir).expanduser().resolve()
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    default_dataset_dirs = {
+        "sim_air_insert_socket_peg": "/data/roboimi_datasets/sim_air_insert_socket_peg",
+        "sim_transfer": "/home/droid/project/diana_sim/sim_transfer",
+    }
+    default_camera_names = {
+        "sim_air_insert_socket_peg": ["l_vis", "r_vis", "front"],
+        "sim_transfer": ["r_vis", "top", "front"],
+    }
+    task_name = str(args.task_name)
+    dataset_dir = str(args.dataset_dir or default_dataset_dirs.get(task_name, ""))
+    if not dataset_dir:
+        raise ValueError(f"--dataset-dir is required for task {task_name!r}")
+    if args.camera_names is None:
+        camera_names = default_camera_names.get(task_name)
+    else:
+        camera_names = [item.strip() for item in str(args.camera_names).split(",") if item.strip()]
+    if not camera_names:
+        raise ValueError(f"--camera-names is required for task {task_name!r}")
+    camera_names_override = "[" + ",".join(camera_names) + "]"
+
+    overrides = [
+        f"data.dataset_dir={dataset_dir}",
+        f"data.camera_names={camera_names_override}",
+        f"eval.camera_names={camera_names_override}",
+        f"eval.task_name={task_name}",
+        f"eval.max_timesteps={int(args.max_timesteps)}",
+        f"eval.device={args.device}",
+        "eval.headless=true",
+        "eval.verbose_action=false",
+        *_parse_override_items(args.overrides),
+    ]
+    cfg = _load_cfg(args.agent, overrides)
+    cfg.eval.ckpt_path = str(Path(args.ckpt_path).expanduser().resolve())
+    cfg.eval.device = str(args.device)
+    cfg.eval.headless = True
+    cfg.eval.task_name = task_name
+    cfg.eval.max_timesteps = int(args.max_timesteps)
+    if cfg.get("train") is not None:
+        cfg.train.device = str(args.device)
+
+    _write_json(output_dir / "resolved_config.json", OmegaConf.to_container(cfg, resolve=True))
+    print("=" * 80)
+    print("Realtime front rollout config")
+    print(OmegaConf.to_yaml(cfg))
+    print("=" * 80, flush=True)
+
+    agent, _stats = load_checkpoint(str(cfg.eval.ckpt_path), cfg.agent, device=str(args.device))
+
+    all_summaries: list[dict[str, Any]] = []
+    for attempt_index in range(1, int(args.max_attempts) + 1):
+        attempt_dir = output_dir / f"attempt_{attempt_index:03d}"
+        summary = _run_one_attempt(
+            cfg=cfg,
+            agent=agent,
+            attempt_dir=attempt_dir,
+            attempt_index=attempt_index,
+            args=args,
+        )
+        all_summaries.append(summary)
+        _write_json(output_dir / "attempts_summary.json", {"attempts": all_summaries})
+        if summary.get("success"):
+            final_paths = _copy_success_artifacts(output_dir, attempt_dir)
+            final_summary = {
+                "success": True,
+                "winning_attempt": int(attempt_index),
+                "final_paths": final_paths,
+                "summary": summary,
+            }
+            _write_json(output_dir / "success_result.json", final_summary)
+            print(json.dumps(_jsonable(final_summary), ensure_ascii=False, indent=2), flush=True)
+            return 0
+
+    failure = {
+        "success": False,
+        "max_attempts": int(args.max_attempts),
+        "best_episode_max_reward": max(
+            [
+                (
+                    s.get("episode_max_reward")
+                    if s.get("episode_max_reward") is not None
+                    else float("-inf")
+                )
+                for s in all_summaries
+            ],
+            default=None,
+        ),
+        "attempts": all_summaries,
+    }
+    _write_json(output_dir / "failure_result.json", failure)
+    print(json.dumps(_jsonable(failure), ensure_ascii=False, indent=2), flush=True)
+    return 2
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/roboimi/demos/vla_scripts/train_vla.py b/roboimi/demos/vla_scripts/train_vla.py
index 2607fa3..3ed0ce7 100644
--- a/roboimi/demos/vla_scripts/train_vla.py
+++ b/roboimi/demos/vla_scripts/train_vla.py
@@ -118,6 +118,109 @@ def recursive_to_device(data, device):
     return data
 
 
+def build_agent_input(batch_data):
+    images = {}
+    for key, value in batch_data.items():
+        if key.startswith('observation.') and key != 'observation.state':
+            images[key.replace('observation.', '')] = value
+
+    agent_input = {
+        'images': images,
+        'qpos': batch_data['observation.state'],
+        'action': batch_data['action'],
+    }
+    if 'action_is_pad' in batch_data:
+        agent_input['action_is_pad'] = batch_data['action_is_pad']
+    if 'task' in batch_data:
+        agent_input['task'] = batch_data['task']
+    return agent_input
+
+
+def _instantiate_dataset(cfg, dataset_image_resize_shape, episode_indices=None):
+    kwargs = {'image_resize_shape': dataset_image_resize_shape}
+    if episode_indices is not None:
+        kwargs['episode_indices'] = episode_indices
+    return instantiate(cfg.data, **kwargs)
+
+
+def build_train_val_datasets(cfg, dataset_image_resize_shape):
+    val_episode_indices = cfg.train.get('val_episode_indices', None)
+    if val_episode_indices:
+        dataset = _instantiate_dataset(cfg, dataset_image_resize_shape)
+        available_episode_indices = list(getattr(dataset, 'available_episode_indices', []))
+        if not available_episode_indices:
+            raise ValueError('显式 val_episode_indices 需要数据集暴露 available_episode_indices')
+        requested_val_episode_indices = sorted(int(idx) for idx in val_episode_indices)
+        available_set = set(available_episode_indices)
+        missing = sorted(set(requested_val_episode_indices) - available_set)
+        if missing:
+            raise ValueError(
+                f'val_episode_indices {missing} 不存在于数据集可用 episodes {available_episode_indices}'
+            )
+        val_set = set(requested_val_episode_indices)
+        train_episode_indices = [
+            idx for idx in available_episode_indices
+            if idx not in val_set
+        ]
+        if not train_episode_indices:
+            raise ValueError('显式 val_episode_indices 不能覆盖全部 episodes，训练集将为空')
+
+        train_dataset = _instantiate_dataset(
+            cfg,
+            dataset_image_resize_shape,
+            episode_indices=train_episode_indices,
+        )
+        val_dataset = _instantiate_dataset(
+            cfg,
+            dataset_image_resize_shape,
+            episode_indices=requested_val_episode_indices,
+        )
+        return dataset, train_dataset, val_dataset, requested_val_episode_indices
+
+    dataset = _instantiate_dataset(cfg, dataset_image_resize_shape)
+    val_split = float(cfg.train.get('val_split', 0.1))
+    seed = int(cfg.train.get('seed', 42))
+    val_size = int(len(dataset) * val_split)
+    train_size = len(dataset) - val_size
+    if val_size > 0:
+        train_dataset, val_dataset = random_split(
+            dataset,
+            [train_size, val_size],
+            generator=torch.Generator().manual_seed(seed)
+        )
+    else:
+        train_dataset, val_dataset = dataset, None
+    return dataset, train_dataset, val_dataset, None
+
+
+def compute_action_mse_validation(agent, val_loader, device):
+    if val_loader is None:
+        return None
+
+    was_training = agent.training
+    agent.eval()
+    total_squared_error = 0.0
+    total_count = 0.0
+    with torch.no_grad():
+        for val_batch in val_loader:
+            val_batch = recursive_to_device(val_batch, device)
+            val_input = build_agent_input(val_batch)
+            pred_actions = agent.predict_action_chunk(val_input)
+            target_actions = val_input['action']
+            squared_error = (pred_actions - target_actions).pow(2)
+            action_is_pad = val_input.get('action_is_pad', None)
+            if action_is_pad is not None:
+                mask = (~action_is_pad).unsqueeze(-1).to(squared_error.dtype)
+                total_squared_error += (squared_error * mask).sum().item()
+                total_count += mask.sum().item() * squared_error.shape[-1]
+            else:
+                total_squared_error += squared_error.sum().item()
+                total_count += target_actions.numel()
+    if was_training:
+        agent.train()
+    return total_squared_error / max(total_count, 1.0)
+
+
 def resolve_resume_checkpoint(resume_ckpt, checkpoint_dir):
     """
     解析恢复训练用的 checkpoint 路径。
@@ -369,6 +472,11 @@ def _run_training(cfg: DictConfig):
     _configure_cuda_runtime(cfg)
     swanlab_module = _init_swanlab(cfg)
     try:
+        action_mse_val_freq_epochs = int(cfg.train.get('action_mse_val_freq_epochs', 0) or 0)
+        explicit_val_episode_indices = cfg.train.get('val_episode_indices', None)
+        if action_mse_val_freq_epochs > 0 and not explicit_val_episode_indices:
+            raise ValueError('action_mse_val_freq_epochs > 0 requires train.val_episode_indices')
+
         # 创建检查点目录
         run_output_dir = _resolve_run_output_dir()
         checkpoint_dir = run_output_dir / "checkpoints"
@@ -381,33 +489,35 @@ def _run_training(cfg: DictConfig):
         log.info("📦 加载数据集...")
         try:
             dataset_image_resize_shape = cfg.data.get('image_resize_shape', (224, 224))
-            vision_backbone_cfg = cfg.agent.get('vision_backbone', None)
-            if vision_backbone_cfg is not None and 'dataset_image_resize_shape' in vision_backbone_cfg:
-                dataset_image_resize_shape = vision_backbone_cfg.get('dataset_image_resize_shape')
-            dataset = instantiate(
-                cfg.data,
-                image_resize_shape=dataset_image_resize_shape,
+            for backbone_key in ('vision_backbone', 'condition_encoder'):
+                backbone_cfg = cfg.agent.get(backbone_key, None)
+                if backbone_cfg is not None and 'dataset_image_resize_shape' in backbone_cfg:
+                    dataset_image_resize_shape = backbone_cfg.get('dataset_image_resize_shape')
+                    break
+            dataset, train_dataset, val_dataset, explicit_val_episode_indices = (
+                build_train_val_datasets(cfg, dataset_image_resize_shape)
             )
             log.info(f"✅ 数据集加载成功。总样本数: {len(dataset)}")
         except Exception as e:
             log.error(f"❌ 数据集加载失败: {e}")
             raise
 
-        # 训练/验证集划分
-        val_split = float(cfg.train.get('val_split', 0.1))
-        seed = int(cfg.train.get('seed', 42))
-        val_size = int(len(dataset) * val_split)
-        train_size = len(dataset) - val_size
-        if val_size > 0:
-            train_dataset, val_dataset = random_split(
-                dataset,
-                [train_size, val_size],
-                generator=torch.Generator().manual_seed(seed)
+        if explicit_val_episode_indices is not None:
+            log.info(
+                "✅ 数据集划分: 训练集=%s, 验证集=%s (显式 held-out episodes=%s)",
+                len(train_dataset),
+                len(val_dataset),
+                explicit_val_episode_indices,
             )
-            log.info(f"✅ 数据集划分: 训练集={train_size}, 验证集={val_size} (验证比例={val_split})")
         else:
-            train_dataset, val_dataset = dataset, None
-            log.info("✅ 数据集划分: 全部用于训练, 验证集=0 (验证比例=0)")
+            val_split = float(cfg.train.get('val_split', 0.1))
+            val_size = len(val_dataset) if val_dataset is not None else 0
+            if val_size > 0:
+                log.info(
+                    f"✅ 数据集划分: 训练集={len(train_dataset)}, 验证集={val_size} (验证比例={val_split})"
+                )
+            else:
+                log.info("✅ 数据集划分: 全部用于训练, 验证集=0 (验证比例=0)")
 
         train_batch_size = int(cfg.train.batch_size)
         train_drop_last = len(train_dataset) >= train_batch_size
@@ -643,22 +753,6 @@ def _run_training(cfg: DictConfig):
         # =========================================================================
         log.info("🏋️ 开始训练循环...")
 
-        def build_agent_input(batch_data):
-            """构建 agent 输入格式"""
-            images = {}
-            # SimpleRobotDataset 返回 observation.{cam_name} 格式
-            for cam_name in cfg.data.camera_names:
-                key = f"observation.{cam_name}"
-                if key in batch_data:
-                    images[cam_name] = batch_data[key]
-
-            return {
-                'images': images,
-                'qpos': batch_data['observation.state'],  # SimpleRobotDataset 使用 observation.state
-                'action': batch_data['action'],
-                'action_is_pad': batch_data.get('action_is_pad', None)  # 传递padding mask
-            }
-
         def save_checkpoint(checkpoint_path: Path, step: int, loss_value, val_loss=None, rollout_avg_reward=None):
             agent_stats = agent.get_normalization_stats()
             torch.save({
@@ -753,6 +847,9 @@ def _run_training(cfg: DictConfig):
         steps_per_epoch = len(train_loader)
         rollout_val_freq_epochs = int(cfg.train.get('rollout_val_freq_epochs', 0) or 0)
         rollout_validation_enabled = rollout_val_freq_epochs > 0
+        action_mse_validation_enabled = (
+            explicit_val_episode_indices is not None and action_mse_val_freq_epochs > 0
+        )
         best_loss = resume_best_loss
         best_rollout_reward = resume_best_rollout_reward
         last_loss = resume_loss
@@ -911,6 +1008,26 @@ def _run_training(cfg: DictConfig):
                 and completed_epoch > 0
                 and completed_epoch % rollout_val_freq_epochs == 0
             )
+            should_run_action_mse_validation = (
+                action_mse_validation_enabled
+                and steps_per_epoch > 0
+                and completed_steps % steps_per_epoch == 0
+                and completed_epoch > 0
+                and completed_epoch % action_mse_val_freq_epochs == 0
+            )
+            if should_run_action_mse_validation:
+                action_mse = compute_action_mse_validation(agent, val_loader, cfg.train.device)
+                if action_mse is not None:
+                    log.info(
+                        f"步骤 {step}/{cfg.train.max_steps} | Epoch {completed_epoch} held-out action MSE: "
+                        f"{action_mse:.6f}"
+                    )
+                    _log_to_swanlab(
+                        swanlab_module,
+                        {'val/action_mse': action_mse},
+                        step=step,
+                    )
+
             if should_run_epoch_rollout:
                 if checkpoint_path is None:
                     checkpoint_path = checkpoint_dir / f"vla_model_step_{step}.pt"
diff --git a/roboimi/vla/agent_imf.py b/roboimi/vla/agent_imf.py
index 6dfc307..1b839fb 100644
--- a/roboimi/vla/agent_imf.py
+++ b/roboimi/vla/agent_imf.py
@@ -15,14 +15,46 @@ except ImportError:  # pragma: no cover
 
 
 class IMFVLAAgent(VLAAgent):
-    def __init__(self, *args, inference_steps: int = 1, **kwargs):
-        if inference_steps != 1:
+    def __init__(
+        self,
+        *args,
+        inference_steps: int = 1,
+        loss_type: str = 'pseudo_huber',
+        pseudo_huber_delta: float = 1.0,
+        **kwargs,
+    ):
+        inference_steps = int(inference_steps)
+        if inference_steps < 1:
             raise ValueError(
-                'IMFVLAAgent only supports one-step inference; '
-                f'inference_steps must be 1, got {inference_steps}.'
+                'IMFVLAAgent inference_steps must be >= 1, '
+                f'got {inference_steps}.'
             )
+        self._validate_loss_config(loss_type=loss_type, pseudo_huber_delta=pseudo_huber_delta)
         super().__init__(*args, inference_steps=inference_steps, **kwargs)
-        self.inference_steps = 1
+        self.inference_steps = inference_steps
+        self.loss_type = str(loss_type)
+        self.pseudo_huber_delta = float(pseudo_huber_delta)
+
+    @staticmethod
+    def _validate_loss_config(loss_type: str, pseudo_huber_delta: float) -> None:
+        if loss_type not in {'pseudo_huber', 'mse'}:
+            raise ValueError(
+                "IMFVLAAgent loss_type must be one of {'pseudo_huber', 'mse'}, "
+                f'got {loss_type!r}.'
+            )
+        if float(pseudo_huber_delta) <= 0.0:
+            raise ValueError(
+                'IMFVLAAgent pseudo_huber_delta must be > 0, '
+                f'got {pseudo_huber_delta}.'
+            )
+
+    def _velocity_loss_from_error(self, error: torch.Tensor) -> torch.Tensor:
+        if self.loss_type == 'mse':
+            return error.square()
+        if self.loss_type != 'pseudo_huber':
+            raise ValueError(f'Unsupported IMFVLAAgent loss_type: {self.loss_type!r}.')
+        delta = float(self.pseudo_huber_delta)
+        return delta**2 * (torch.sqrt(1.0 + (error / delta).square()) - 1.0)
 
     @staticmethod
     def _broadcast_batch_time(value: torch.Tensor, reference: torch.Tensor) -> torch.Tensor:
@@ -142,7 +174,7 @@ class IMFVLAAgent(VLAAgent):
         V = self._compound_velocity(u, du_dt, r, t)
         target = e - x
 
-        loss = F.mse_loss(V, target, reduction='none')
+        loss = self._velocity_loss_from_error(V - target)
         if action_is_pad is not None:
             mask = (~action_is_pad).unsqueeze(-1).to(loss.dtype)
             valid_count = mask.sum() * loss.shape[-1]
@@ -157,5 +189,26 @@ class IMFVLAAgent(VLAAgent):
         proprioception = self.normalization.normalize_qpos(proprioception)
         cond = self._build_cond(images, proprioception)
         z_t = torch.randn((batch_size, self.pred_horizon, self.action_dim), device=cond.device, dtype=cond.dtype)
-        action = self._sample_one_step(z_t, cond=cond)
+        action = z_t
+        time_grid = torch.linspace(
+            1.0,
+            0.0,
+            steps=self.inference_steps + 1,
+            device=cond.device,
+            dtype=cond.dtype,
+        )
+        for step_index in range(self.inference_steps):
+            t = torch.full(
+                (batch_size,),
+                float(time_grid[step_index].item()),
+                device=cond.device,
+                dtype=cond.dtype,
+            )
+            r = torch.full(
+                (batch_size,),
+                float(time_grid[step_index + 1].item()),
+                device=cond.device,
+                dtype=cond.dtype,
+            )
+            action = self._sample_one_step(action, r=r, t=t, cond=cond)
         return self.normalization.denormalize_action(action)
diff --git a/roboimi/vla/agent_smolvla_conditioned.py b/roboimi/vla/agent_smolvla_conditioned.py
new file mode 100644
index 0000000..b995cc2
--- /dev/null
+++ b/roboimi/vla/agent_smolvla_conditioned.py
@@ -0,0 +1,282 @@
+from __future__ import annotations
+
+from collections import deque
+from typing import Dict, Optional, Sequence
+
+import torch
+import torch.nn as nn
+
+from roboimi.vla.agent_imf import IMFVLAAgent
+from roboimi.vla.models.normalization import NormalizationModule
+
+
+class SmolVLAIMFAttnResAgent(IMFVLAAgent):
+    """IMF-AttnRes action expert conditioned by SmolVLA-style VLM prefix tokens.
+
+    Unlike ``VLAAgent`` this agent does not concatenate ResNet features and raw
+    state at every observation step.  A ``condition_encoder`` receives images,
+    normalized state, and variable task language, and returns a condition token
+    sequence `(B, S, D)` that is passed directly to the IMF head.
+    """
+
+    def __init__(
+        self,
+        condition_encoder: nn.Module,
+        action_encoder,
+        head,
+        action_dim: int,
+        obs_dim: int,
+        pred_horizon: int = 16,
+        obs_horizon: int = 2,
+        diffusion_steps: int = 100,
+        inference_steps: int = 1,
+        num_cams: int = 3,
+        camera_names: Optional[Sequence[str]] = None,
+        dataset_stats=None,
+        normalization_type: str = 'min_max',
+        num_action_steps: int = 8,
+        head_type: str = 'transformer',
+        condition_dim: int | None = None,
+        condition_sequence_length: int | None = None,
+        task_description: str | None = None,
+        loss_type: str = 'pseudo_huber',
+        pseudo_huber_delta: float = 1.0,
+        **kwargs,
+    ) -> None:
+        # Intentionally bypass VLAAgent.__init__; its condition dimensions are
+        # tied to ResNet-style visual+state concatenation.
+        nn.Module.__init__(self)
+        if inference_steps != 1:
+            raise ValueError(
+                'SmolVLAIMFAttnResAgent only supports one-step IMF inference; '
+                f'inference_steps must be 1, got {inference_steps}.'
+            )
+        if head_type != 'transformer':
+            raise ValueError(f'SmolVLAIMFAttnResAgent requires head_type="transformer", got {head_type!r}')
+        self._validate_loss_config(loss_type=loss_type, pseudo_huber_delta=pseudo_huber_delta)
+        del diffusion_steps, kwargs
+
+        self.action_dim = int(action_dim)
+        self.obs_dim = int(obs_dim)
+        self.pred_horizon = int(pred_horizon)
+        self.obs_horizon = int(obs_horizon)
+        self.num_cams = int(num_cams)
+        self.num_action_steps = int(num_action_steps)
+        self.inference_steps = 1
+        self.head_type = head_type
+        self.loss_type = str(loss_type)
+        self.pseudo_huber_delta = float(pseudo_huber_delta)
+        self.camera_names = tuple(camera_names) if camera_names is not None else None
+        if self.camera_names is not None and len(self.camera_names) != self.num_cams:
+            raise ValueError(f'camera_names length({len(self.camera_names)}) does not match num_cams({self.num_cams})')
+
+        self.normalization = NormalizationModule(stats=dataset_stats, normalization_type=normalization_type)
+        self.condition_encoder = condition_encoder
+        # Compatibility aliases used by some utilities/tests.
+        self.vision_encoder = condition_encoder
+        self.action_encoder = action_encoder
+        self.state_encoder = None
+        self.task_description = task_description
+
+        encoder_dim = getattr(condition_encoder, 'output_dim', None)
+        if encoder_dim is None:
+            encoder_dim = getattr(condition_encoder, 'joint_output_dim', None)
+        if condition_dim is None:
+            if encoder_dim is None:
+                raise ValueError('condition_dim must be provided when condition_encoder has no output_dim')
+            condition_dim = int(encoder_dim)
+        self.per_step_cond_dim = int(condition_dim)
+        self.raw_per_step_cond_dim = self.per_step_cond_dim
+
+        encoder_seq_len = getattr(condition_encoder, 'condition_sequence_length', None)
+        if condition_sequence_length is None:
+            if encoder_seq_len is None:
+                raise ValueError(
+                    'condition_sequence_length must be provided when condition_encoder has no condition_sequence_length'
+                )
+            condition_sequence_length = int(encoder_seq_len)
+        self.condition_sequence_length = int(condition_sequence_length)
+        self.condition_tokens_per_step = self.condition_sequence_length
+        self.global_cond_dim = self.per_step_cond_dim * self.condition_sequence_length
+
+        if isinstance(head, nn.Module):
+            self.noise_pred_net = head
+        else:
+            self.noise_pred_net = head(
+                input_dim=self.action_dim,
+                output_dim=self.action_dim,
+                horizon=self.pred_horizon,
+                n_obs_steps=self.condition_sequence_length,
+                cond_dim=self.per_step_cond_dim,
+            )
+        self.reset()
+
+    def _get_model_device(self) -> torch.device:
+        return next(self.parameters()).device
+
+    def _move_to_device(self, data, device: torch.device):
+        if torch.is_tensor(data):
+            return data.to(device)
+        if isinstance(data, dict):
+            return {k: self._move_to_device(v, device) for k, v in data.items()}
+        if isinstance(data, list):
+            return [self._move_to_device(v, device) for v in data]
+        if isinstance(data, tuple):
+            return tuple(self._move_to_device(v, device) for v in data)
+        return data
+
+    def _order_images(self, images: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
+        if self.camera_names is None:
+            names = tuple(sorted(images.keys()))
+            if len(names) != self.num_cams:
+                raise ValueError(f'image camera count({len(names)}) does not match num_cams({self.num_cams})')
+            return {name: images[name] for name in names}
+        missing = [name for name in self.camera_names if name not in images]
+        if missing:
+            raise ValueError(f'image condition missing required cameras. missing={missing}, expected={list(self.camera_names)}')
+        return {name: images[name] for name in self.camera_names}
+
+    def _resolve_task(self, task, batch_size: int):
+        def is_missing_task(item) -> bool:
+            return item is None or (isinstance(item, str) and item.strip().lower() in {'', 'unknown'})
+
+        def fallback_or(item):
+            if self.task_description is not None and is_missing_task(item):
+                return self.task_description
+            if item is None:
+                return ''
+            return item
+
+        if task is None:
+            task = self.task_description
+        if task is None:
+            return [''] * batch_size
+        if isinstance(task, str):
+            return [fallback_or(task)] * batch_size
+        task = list(task)
+        if len(task) != batch_size:
+            raise ValueError(f'task batch size ({len(task)}) must match batch size ({batch_size})')
+        return [fallback_or(item) for item in task]
+
+    def _build_cond(self, images: Dict[str, torch.Tensor], states: torch.Tensor, task=None) -> torch.Tensor:
+        ordered_images = self._order_images(images)
+        batch_size = states.shape[0]
+        tasks = self._resolve_task(task, batch_size=batch_size)
+        cond = self.condition_encoder(ordered_images, state=states, task=tasks)
+        if cond.ndim != 3:
+            raise RuntimeError(f'condition_encoder must return (B,S,D), got {tuple(cond.shape)}')
+        if cond.shape[0] != batch_size:
+            raise RuntimeError(f'condition batch mismatch: got {cond.shape[0]}, expected {batch_size}')
+        if cond.shape[1] != self.condition_sequence_length:
+            raise RuntimeError(
+                f'condition sequence length mismatch: got {cond.shape[1]}, expected {self.condition_sequence_length}'
+            )
+        if cond.shape[2] != self.per_step_cond_dim:
+            raise RuntimeError(f'condition dim mismatch: got {cond.shape[2]}, expected {self.per_step_cond_dim}')
+        head_dtype = next(self.noise_pred_net.parameters()).dtype
+        return cond.to(dtype=head_dtype)
+
+    def compute_loss(self, batch):
+        actions, states, images = batch['action'], batch['qpos'], batch['images']
+        action_is_pad = batch.get('action_is_pad', None)
+        batch_size = actions.shape[0]
+
+        states = self.normalization.normalize_qpos(states)
+        actions = self.normalization.normalize_action(actions)
+        cond = self._build_cond(images, states, task=batch.get('task', None))
+
+        x = actions
+        e = torch.randn_like(x)
+        t = torch.rand(batch_size, device=x.device, dtype=x.dtype)
+        r = torch.rand(batch_size, device=x.device, dtype=x.dtype)
+        t, r = torch.maximum(t, r), torch.minimum(t, r)
+
+        t_broadcast = self._broadcast_batch_time(t, x)
+        z_t = (1 - t_broadcast) * x + t_broadcast * e
+
+        v = self.fn(z_t, t, t, cond=cond)
+        u, du_dt = self._compute_u_and_du_dt(z_t, r, t, cond=cond, v=v)
+        V = self._compound_velocity(u, du_dt, r, t)
+        target = e - x
+
+        loss = self._velocity_loss_from_error(V - target)
+        if action_is_pad is not None:
+            mask = (~action_is_pad).unsqueeze(-1).to(loss.dtype)
+            valid_count = mask.sum() * loss.shape[-1]
+            loss = (loss * mask).sum() / valid_count.clamp_min(1.0)
+        else:
+            loss = loss.mean()
+        return loss
+
+    @torch.no_grad()
+    def predict_action(self, images, proprioception, task=None):
+        batch_size = proprioception.shape[0]
+        proprioception = self.normalization.normalize_qpos(proprioception)
+        cond = self._build_cond(images, proprioception, task=task)
+        z_t = torch.randn((batch_size, self.pred_horizon, self.action_dim), device=cond.device, dtype=cond.dtype)
+        action = self._sample_one_step(z_t, cond=cond)
+        return self.normalization.denormalize_action(action)
+
+    @torch.no_grad()
+    def predict_action_chunk(self, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
+        return self.predict_action(batch['images'], batch['qpos'], task=batch.get('task', None))
+
+    def reset(self):
+        self._queues = {
+            'qpos': deque(maxlen=self.obs_horizon),
+            'images': deque(maxlen=self.obs_horizon),
+            'task': deque(maxlen=self.obs_horizon),
+            'action': deque(maxlen=self.pred_horizon - self.obs_horizon + 1),
+        }
+
+    def _populate_queues(self, observation: Dict[str, torch.Tensor]) -> None:
+        if 'qpos' in observation:
+            self._queues['qpos'].append(observation['qpos'].clone())
+        if 'images' in observation:
+            ordered_images = self._order_images(observation['images'])
+            self._queues['images'].append({k: v.clone() for k, v in ordered_images.items()})
+        if 'task' in observation:
+            self._queues['task'].append(observation['task'])
+
+    def _prepare_observation_batch(self) -> Dict[str, torch.Tensor]:
+        qpos_list = list(self._queues['qpos'])
+        if not qpos_list:
+            raise ValueError('observation queue is empty.')
+        while len(qpos_list) < self.obs_horizon:
+            qpos_list.append(qpos_list[-1])
+        batch_qpos = torch.stack(qpos_list, dim=0).unsqueeze(0)
+
+        images_list = list(self._queues['images'])
+        if not images_list:
+            raise ValueError('image queue is empty.')
+        while len(images_list) < self.obs_horizon:
+            images_list.append(images_list[-1])
+        names = self.camera_names if self.camera_names is not None else tuple(sorted(images_list[0].keys()))
+        batch_images = {
+            name: torch.stack([item[name] for item in images_list], dim=0).unsqueeze(0)
+            for name in names
+        }
+        batch = {'qpos': batch_qpos, 'images': batch_images}
+        if self._queues['task']:
+            batch['task'] = [list(self._queues['task'])[-1]]
+        elif self.task_description is not None:
+            batch['task'] = [self.task_description]
+        return batch
+
+    @torch.no_grad()
+    def select_action(self, observation: Dict[str, torch.Tensor]) -> torch.Tensor:
+        device = self._get_model_device()
+        observation = self._move_to_device(observation, device)
+        self._populate_queues(observation)
+        if len(self._queues['action']) == 0:
+            batch = self._prepare_observation_batch()
+            actions = self.predict_action_chunk(batch)
+            start = self.obs_horizon - 1
+            end = start + self.num_action_steps
+            executable_actions = actions[:, start:end]
+            for i in range(executable_actions.shape[1]):
+                self._queues['action'].append(executable_actions[:, i].squeeze(0))
+        return self._queues['action'].popleft()
+
+    def get_normalization_stats(self):
+        return self.normalization.get_stats()
diff --git a/roboimi/vla/conf/agent/lewm_imf_attnres.yaml b/roboimi/vla/conf/agent/lewm_imf_attnres.yaml
index f7394e8..056a01a 100644
--- a/roboimi/vla/conf/agent/lewm_imf_attnres.yaml
+++ b/roboimi/vla/conf/agent/lewm_imf_attnres.yaml
@@ -25,6 +25,8 @@ vision_backbone:
 diffusion_steps: 100
 inference_steps: 1
 head_type: "transformer"
+loss_type: "pseudo_huber"
+pseudo_huber_delta: 1.0
 
 head:
   input_dim: ${agent.action_dim}
diff --git a/roboimi/vla/conf/agent/resnet_imf_attnres.yaml b/roboimi/vla/conf/agent/resnet_imf_attnres.yaml
index e04bfb4..b0c32a2 100644
--- a/roboimi/vla/conf/agent/resnet_imf_attnres.yaml
+++ b/roboimi/vla/conf/agent/resnet_imf_attnres.yaml
@@ -24,6 +24,8 @@ vision_backbone:
 diffusion_steps: 100
 inference_steps: 1
 head_type: "transformer"
+loss_type: "pseudo_huber"
+pseudo_huber_delta: 1.0
 
 head:
   input_dim: ${agent.action_dim}
diff --git a/roboimi/vla/conf/agent/resnet_imf_attnres_multitoken.yaml b/roboimi/vla/conf/agent/resnet_imf_attnres_multitoken.yaml
index bfb5906..e79a32d 100644
--- a/roboimi/vla/conf/agent/resnet_imf_attnres_multitoken.yaml
+++ b/roboimi/vla/conf/agent/resnet_imf_attnres_multitoken.yaml
@@ -33,6 +33,8 @@ cond_projector:
 diffusion_steps: 100
 inference_steps: 1
 head_type: "transformer"
+loss_type: "pseudo_huber"
+pseudo_huber_delta: 1.0
 
 head:
   input_dim: ${agent.action_dim}
diff --git a/roboimi/vla/conf/agent/siglip2_imf_attnres.yaml b/roboimi/vla/conf/agent/siglip2_imf_attnres.yaml
index 2694621..bef551c 100644
--- a/roboimi/vla/conf/agent/siglip2_imf_attnres.yaml
+++ b/roboimi/vla/conf/agent/siglip2_imf_attnres.yaml
@@ -28,6 +28,8 @@ cond_projector:
 diffusion_steps: 100
 inference_steps: 1
 head_type: "transformer"
+loss_type: "pseudo_huber"
+pseudo_huber_delta: 1.0
 
 head:
   input_dim: ${agent.action_dim}
diff --git a/roboimi/vla/conf/agent/smolvla_imf_attnres.yaml b/roboimi/vla/conf/agent/smolvla_imf_attnres.yaml
new file mode 100644
index 0000000..132bb42
--- /dev/null
+++ b/roboimi/vla/conf/agent/smolvla_imf_attnres.yaml
@@ -0,0 +1,48 @@
+# @package agent
+defaults:
+  - /backbone@condition_encoder: smolvla_prefix_encoder
+  - /modules@action_encoder: identity_action_encoder
+  - /head: imf_transformer1d
+  - _self_
+
+_target_: roboimi.vla.agent_smolvla_conditioned.SmolVLAIMFAttnResAgent
+
+action_dim: 16
+obs_dim: 16
+normalization_type: "min_max"
+pred_horizon: 16
+obs_horizon: 2
+num_action_steps: 8
+camera_names: ${data.camera_names}
+num_cams: ${len:${agent.camera_names}}
+
+# Optional fallback language instruction used only when a batch/observation
+# does not provide `task`. Keep null by default so this architecture remains
+# generic and dataset/eval can supply variable language.
+task_description: null
+condition_dim: 960
+condition_sequence_length: 241
+
+condition_encoder:
+  num_cameras: ${agent.num_cams}
+  camera_names: ${agent.camera_names}
+
+diffusion_steps: 100
+inference_steps: 1
+head_type: "transformer"
+loss_type: "pseudo_huber"
+pseudo_huber_delta: 1.0
+
+head:
+  input_dim: ${agent.action_dim}
+  output_dim: ${agent.action_dim}
+  horizon: ${agent.pred_horizon}
+  n_obs_steps: ${agent.condition_sequence_length}
+  cond_dim: ${agent.condition_dim}
+  causal_attn: false
+  time_as_cond: true
+  obs_as_cond: true
+  n_cond_layers: 0
+  backbone_type: attnres_full
+  n_head: 8
+  n_kv_head: 8
diff --git a/roboimi/vla/conf/backbone/smolvla_prefix_encoder.yaml b/roboimi/vla/conf/backbone/smolvla_prefix_encoder.yaml
new file mode 100644
index 0000000..ade060e
--- /dev/null
+++ b/roboimi/vla/conf/backbone/smolvla_prefix_encoder.yaml
@@ -0,0 +1,23 @@
+_target_: roboimi.vla.models.backbones.smolvla_prefix_encoder.SmolVLAPrefixEncoder
+
+model_name: HuggingFaceTB/SmolVLM2-500M-Video-Instruct
+load_vlm_weights: true
+local_files_only: false
+num_vlm_layers: 16
+freeze_vlm: true
+freeze_vision_encoder: true
+train_state_proj: true
+max_state_dim: 32
+resize_imgs_with_padding: [512, 512]
+tokenizer_max_length: 48
+pad_language_to: max_length
+# transformer: run SmolVLM text/LLM layers over the prefix.
+# embedding: use vision tokens + raw text embedding tokens directly and prune text layers.
+# null preserves the legacy run_text_model switch; set to embedding/transformer to be explicit.
+text_encoder_mode: null
+run_text_model: true
+camera_names: [r_vis, top, front]
+num_cameras: 3
+
+dataset_image_resize_shape: null
+eval_image_resize_shape: null
diff --git a/roboimi/vla/conf/config.yaml b/roboimi/vla/conf/config.yaml
index 6e43491..ab9e1ab 100644
--- a/roboimi/vla/conf/config.yaml
+++ b/roboimi/vla/conf/config.yaml
@@ -18,6 +18,8 @@ train:
   # 数据加载
   num_workers: 12     # DataLoader 工作进程数（调试时设为 0）
   val_split: 0.0      # 验证集比例；默认使用全量数据训练
+  val_episode_indices: null  # 显式按 episode 划出的验证集，例如 [100]
+  action_mse_val_freq_epochs: 0  # >0 时每隔多少个 epoch 在 held-out episode 上计算 action MSE
   seed: 42            # 随机种子（用于数据划分）
 
   # 日志和检查点
diff --git a/roboimi/vla/conf/eval/eval.yaml b/roboimi/vla/conf/eval/eval.yaml
index b2e38a2..b9b9efe 100644
--- a/roboimi/vla/conf/eval/eval.yaml
+++ b/roboimi/vla/conf/eval/eval.yaml
@@ -9,6 +9,7 @@ server_startup_timeout_s: 300.0 # parent 等待 inference server 就绪的超时
 max_timesteps: 700        # 每回合最大时间步
 device: ${train.device}   # 与训练保持一致
 task_name: "sim_transfer" # 环境任务名称
+task_description: null    # 可选语言任务描述；SmolVLM 条件编码器在环境未提供 task 时使用
 
 # ====================
 # 策略执行参数
diff --git a/roboimi/vla/data/simpe_robot_dataset.py b/roboimi/vla/data/simpe_robot_dataset.py
index c1c5a93..d5f91c1 100644
--- a/roboimi/vla/data/simpe_robot_dataset.py
+++ b/roboimi/vla/data/simpe_robot_dataset.py
@@ -4,6 +4,7 @@ from torch.utils.data import Dataset
 from typing import List, Dict, Union, Optional, Sequence
 from pathlib import Path
 from collections import OrderedDict
+import re
 
 
 class SimpleRobotDataset(Dataset):
@@ -24,6 +25,7 @@ class SimpleRobotDataset(Dataset):
         camera_names: List[str] = None,
         image_resize_shape: Optional[Sequence[int]] = (224, 224),
         max_open_files: int = 64,
+        episode_indices: Optional[Sequence[int]] = None,
     ):
         """
         Args:
@@ -48,6 +50,9 @@ class SimpleRobotDataset(Dataset):
         )
         self.max_open_files = max(1, int(max_open_files))
         self._file_cache: "OrderedDict[str, h5py.File]" = OrderedDict()
+        self.requested_episode_indices = (
+            None if episode_indices is None else tuple(sorted(int(idx) for idx in episode_indices))
+        )
 
         self.dataset_dir = Path(dataset_dir)
         if not self.dataset_dir.exists():
@@ -60,20 +65,37 @@ class SimpleRobotDataset(Dataset):
         if not self.hdf5_files:
             raise FileNotFoundError(f"在 {dataset_dir} 中未找到 HDF5 文件")
 
+        if self.requested_episode_indices is not None:
+            requested = set(self.requested_episode_indices)
+            filtered = []
+            for hdf5_path in self.hdf5_files:
+                match = re.search(r'episode_(\d+)$', hdf5_path.stem)
+                if match and int(match.group(1)) in requested:
+                    filtered.append(hdf5_path)
+            self.hdf5_files = filtered
+            if not self.hdf5_files:
+                raise FileNotFoundError(
+                    f"在 {dataset_dir} 中未找到 episode_indices={sorted(requested)} 对应的 HDF5 文件"
+                )
+
         # 构建 episode 索引（只存储元数据，不加载数据）
         self.episodes = {}
         self.frame_meta = []  # 存储 (ep_idx, frame_idx, hdf5_path)
         for ep_idx, hdf5_path in enumerate(self.hdf5_files):
             with h5py.File(hdf5_path, 'r') as f:
                 T = f['action'].shape[0]
+                dataset_episode_idx = ep_idx
+                match = re.search(r'episode_(\d+)$', hdf5_path.stem)
+                if match:
+                    dataset_episode_idx = int(match.group(1))
                 start_idx = len(self.frame_meta)
                 for t in range(T):
                     self.frame_meta.append({
-                        "ep_idx": ep_idx,
+                        "ep_idx": dataset_episode_idx,
                         "frame_idx": t,
                         "hdf5_path": hdf5_path,
                     })
-                self.episodes[ep_idx] = list(range(start_idx, len(self.frame_meta)))
+                self.episodes[dataset_episode_idx] = list(range(start_idx, len(self.frame_meta)))
 
         print(f"懒加载模式: {len(self.hdf5_files)} 个 episodes, 共 {len(self.frame_meta)} 帧")
 
@@ -227,6 +249,10 @@ class SimpleRobotDataset(Dataset):
         """获取所有相机键名 (LeRobotDataset 格式)"""
         return [f"observation.{cam_name}" for cam_name in self.camera_names]
 
+    @property
+    def available_episode_indices(self) -> List[int]:
+        return sorted(self.episodes.keys())
+
     @property
     def camera_info(self) -> dict:
         """获取相机信息"""
@@ -246,4 +272,5 @@ class SimpleRobotDataset(Dataset):
         return info
 
     def __del__(self):
-        self._close_all_files()
+        if hasattr(self, '_file_cache'):
+            self._close_all_files()
diff --git a/roboimi/vla/models/backbones/__init__.py b/roboimi/vla/models/backbones/__init__.py
index c5544b5..b2781b9 100644
--- a/roboimi/vla/models/backbones/__init__.py
+++ b/roboimi/vla/models/backbones/__init__.py
@@ -1,5 +1,11 @@
 # Backbone models
-__all__ = ["LEWMViTBackbone", "ResNetBackbone", "ResNetDiffusionBackbone", "SigLIP2DiffusionBackbone"]
+__all__ = [
+    "LEWMViTBackbone",
+    "ResNetBackbone",
+    "ResNetDiffusionBackbone",
+    "SigLIP2DiffusionBackbone",
+    "SmolVLAPrefixEncoder",
+]
 
 
 def __getattr__(name):
@@ -9,6 +15,9 @@ def __getattr__(name):
     if name == "SigLIP2DiffusionBackbone":
         from .siglip2_diffusion_backbone import SigLIP2DiffusionBackbone
         return SigLIP2DiffusionBackbone
+    if name in {"SmolVLAPrefixEncoder", "SmolVLMPrefixEncoder"}:
+        from .smolvla_prefix_encoder import SmolVLAPrefixEncoder
+        return SmolVLAPrefixEncoder
     if name in {"ResNetBackbone", "ResNetDiffusionBackbone"}:
         from .resnet_diffusion import ResNetDiffusionBackbone
         return ResNetDiffusionBackbone
diff --git a/roboimi/vla/models/backbones/smolvla_prefix_encoder.py b/roboimi/vla/models/backbones/smolvla_prefix_encoder.py
new file mode 100644
index 0000000..f203e47
--- /dev/null
+++ b/roboimi/vla/models/backbones/smolvla_prefix_encoder.py
@@ -0,0 +1,429 @@
+from __future__ import annotations
+
+import warnings
+from typing import Dict, Sequence
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from roboimi.vla.core.interfaces import VLABackbone
+
+try:  # pragma: no cover - exercised by tests via monkeypatch/fakes
+    from transformers import AutoModelForImageTextToText, AutoTokenizer
+except Exception:  # pragma: no cover
+    AutoModelForImageTextToText = None
+    AutoTokenizer = None
+
+
+def _resize_with_pad(img: torch.Tensor, width: int, height: int, pad_value: float = 0.0) -> torch.Tensor:
+    if img.ndim != 4:
+        raise ValueError(f'expected image tensor shaped (B,C,H,W), got {tuple(img.shape)}')
+    cur_height, cur_width = img.shape[2:]
+    ratio = max(cur_width / width, cur_height / height)
+    resized_height = int(cur_height / ratio)
+    resized_width = int(cur_width / ratio)
+    resized = F.interpolate(img, size=(resized_height, resized_width), mode='bilinear', align_corners=False)
+    pad_height = max(0, int(height - resized_height))
+    pad_width = max(0, int(width - resized_width))
+    return F.pad(resized, (pad_width, 0, pad_height, 0), value=pad_value)
+
+
+def _pad_vector(vector: torch.Tensor, new_dim: int) -> torch.Tensor:
+    if vector.shape[-1] == new_dim:
+        return vector
+    if vector.shape[-1] > new_dim:
+        raise ValueError(f'cannot pad vector with dim {vector.shape[-1]} to smaller dim {new_dim}')
+    shape = list(vector.shape)
+    shape[-1] = int(new_dim)
+    padded = torch.zeros(*shape, dtype=vector.dtype, device=vector.device)
+    padded[..., : vector.shape[-1]] = vector
+    return padded
+
+
+class SmolVLAPrefixEncoder(VLABackbone):
+    """SmolVLA-compatible VLM prefix encoder for RoboIMI action experts.
+
+    This module intentionally extracts only the conditioning path from SmolVLA:
+    multiview image tokens, variable language task tokens, and one projected
+    state token.  It freezes the pretrained VLM by default and returns a fixed
+    condition token sequence `(B, S, D)` that can be consumed by existing IMF /
+    transformer-style action heads.
+    """
+
+    def __init__(
+        self,
+        model_name: str = 'HuggingFaceTB/SmolVLM2-500M-Video-Instruct',
+        *,
+        model_name_or_path: str | None = None,
+        vlm: nn.Module | None = None,
+        tokenizer=None,
+        load_vlm_weights: bool = True,
+        local_files_only: bool = False,
+        num_vlm_layers: int = 16,
+        freeze_vlm: bool = True,
+        freeze_vision_encoder: bool = True,
+        train_state_proj: bool = True,
+        max_state_dim: int = 32,
+        resize_imgs_with_padding: Sequence[int] | None = (512, 512),
+        dataset_image_resize_shape: Sequence[int] | None = None,
+        eval_image_resize_shape: Sequence[int] | None = None,
+        tokenizer_max_length: int = 48,
+        pad_language_to: str = 'max_length',
+        run_text_model: bool = True,
+        text_encoder_mode: str | None = None,
+        camera_names: Sequence[str] = ('r_vis', 'top', 'front'),
+        num_cameras: int | None = None,
+    ) -> None:
+        super().__init__()
+        if model_name_or_path is not None:
+            model_name = model_name_or_path
+        self.model_name = str(model_name)
+        self.camera_names = tuple(camera_names)
+        self.num_cameras = int(num_cameras) if num_cameras is not None else len(self.camera_names)
+        if len(self.camera_names) != self.num_cameras:
+            raise ValueError(
+                f'camera_names length ({len(self.camera_names)}) must match num_cameras ({self.num_cameras})'
+            )
+        self.load_vlm_weights = bool(load_vlm_weights)
+        self.local_files_only = bool(local_files_only)
+        self.num_vlm_layers_requested = int(num_vlm_layers)
+        self.freeze_vlm = bool(freeze_vlm)
+        self.freeze_vision_encoder = bool(freeze_vision_encoder)
+        self.max_state_dim = int(max_state_dim)
+        self.resize_imgs_with_padding = self._normalize_resize_shape(resize_imgs_with_padding)
+        self.dataset_image_resize_shape = self._normalize_resize_shape(dataset_image_resize_shape)
+        self.eval_image_resize_shape = self._normalize_resize_shape(eval_image_resize_shape)
+        self.tokenizer_max_length = int(tokenizer_max_length)
+        self.pad_language_to = str(pad_language_to)
+        if text_encoder_mode is None:
+            text_encoder_mode = 'transformer' if bool(run_text_model) else 'embedding'
+        self.text_encoder_mode = str(text_encoder_mode).lower()
+        if self.text_encoder_mode not in {'embedding', 'transformer'}:
+            raise ValueError(
+                f'text_encoder_mode must be "embedding" or "transformer", got {text_encoder_mode!r}'
+            )
+        self.run_text_model = self.text_encoder_mode == 'transformer'
+        self._warned_trainable_frozen_text_model = False
+
+        if vlm is None:
+            if AutoModelForImageTextToText is None:
+                raise ImportError('transformers AutoModelForImageTextToText is required for SmolVLAPrefixEncoder')
+            if not self.load_vlm_weights:
+                raise ValueError('SmolVLAPrefixEncoder currently requires load_vlm_weights=True')
+            vlm = AutoModelForImageTextToText.from_pretrained(
+                self.model_name,
+                torch_dtype='bfloat16',
+                low_cpu_mem_usage=True,
+                local_files_only=self.local_files_only,
+            )
+        self.vlm = vlm
+
+        if tokenizer is None:
+            if AutoTokenizer is None:
+                raise ImportError('transformers AutoTokenizer is required for SmolVLAPrefixEncoder')
+            tokenizer = AutoTokenizer.from_pretrained(self.model_name, local_files_only=self.local_files_only)
+        self.tokenizer = tokenizer
+
+        text_model = self.vlm.model.text_model
+        text_layers = text_model.layers
+        if self.text_encoder_mode == 'embedding':
+            text_model.layers = nn.ModuleList()
+            if hasattr(self.vlm, 'lm_head'):
+                self.vlm.lm_head = nn.Identity()
+            if hasattr(self.vlm.model, 'lm_head'):
+                self.vlm.model.lm_head = nn.Identity()
+        elif self.num_vlm_layers_requested > 0:
+            if self.num_vlm_layers_requested > len(text_layers):
+                raise ValueError(
+                    f'num_vlm_layers ({self.num_vlm_layers_requested}) exceeds available text layers '
+                    f'({len(text_layers)})'
+                )
+            text_model.layers = text_layers[: self.num_vlm_layers_requested]
+        self.num_vlm_layers = len(self.vlm.model.text_model.layers)
+        text_config = getattr(self.vlm.config, 'text_config', None)
+        if text_config is not None and hasattr(text_config, 'num_hidden_layers'):
+            text_config.num_hidden_layers = self.num_vlm_layers
+
+        hidden_size = int(self.vlm.config.text_config.hidden_size)
+        self._output_dim = hidden_size
+        self.state_proj = nn.Linear(self.max_state_dim, hidden_size)
+        for param in self.state_proj.parameters():
+            param.requires_grad = bool(train_state_proj)
+
+        self.last_prefix_pad_mask: torch.Tensor | None = None
+        self.last_prefix_att_mask: torch.Tensor | None = None
+        self.last_attention_2d_mask: torch.Tensor | None = None
+        self.last_position_ids: torch.Tensor | None = None
+        self._configured_condition_sequence_length = self._infer_configured_condition_sequence_length()
+
+        self.set_requires_grad()
+
+    @staticmethod
+    def _normalize_resize_shape(shape: Sequence[int] | None) -> tuple[int, int] | None:
+        if shape is None:
+            return None
+        normalized = tuple(int(v) for v in shape)
+        if len(normalized) != 2:
+            raise ValueError(f'resize_imgs_with_padding must contain exactly two values, got {normalized}')
+        return normalized
+
+    @property
+    def output_dim(self) -> int:
+        return self._output_dim
+
+    @property
+    def joint_output_dim(self) -> int:
+        return self._output_dim
+
+    def _infer_configured_condition_sequence_length(self) -> int:
+        image_tokens_per_camera = 0
+        config = getattr(self.vlm, 'config', None)
+        vision_config = getattr(config, 'vision_config', None)
+        if vision_config is not None:
+            image_size = int(getattr(vision_config, 'image_size', 0) or 0)
+            patch_size = int(getattr(vision_config, 'patch_size', 0) or 0)
+            scale_factor = int(getattr(config, 'scale_factor', 1) or 1)
+            if image_size > 0 and patch_size > 0 and scale_factor > 0:
+                image_tokens_per_camera = int(((image_size // patch_size) ** 2) / (scale_factor**2))
+        return self.num_cameras * image_tokens_per_camera + self.tokenizer_max_length + 1
+
+    @property
+    def tokens_per_step(self) -> int:
+        if self.last_prefix_pad_mask is not None:
+            return int(self.last_prefix_pad_mask.shape[1])
+        return int(self._configured_condition_sequence_length)
+
+    @property
+    def condition_sequence_length(self) -> int:
+        return self.tokens_per_step
+
+    @staticmethod
+    def _freeze_module(module) -> None:
+        if module is None:
+            return
+        if hasattr(module, 'eval'):
+            module.eval()
+        parameters = getattr(module, 'parameters', None)
+        if callable(parameters):
+            for param in parameters():
+                param.requires_grad = False
+
+    def set_requires_grad(self) -> None:
+        if self.freeze_vision_encoder:
+            self._freeze_module(self.vlm.model.vision_model)
+        if self.freeze_vlm:
+            self._freeze_module(self.vlm)
+            self._freeze_module(getattr(self.vlm.model, 'vision_model', None))
+            self._freeze_module(getattr(self.vlm.model, 'connector', None))
+            self._freeze_module(getattr(self.vlm.model, 'text_model', None))
+
+    def train(self, mode: bool = True):
+        super().train(mode)
+        if self.freeze_vlm:
+            self.vlm.eval()
+        elif self.freeze_vision_encoder:
+            self.vlm.model.vision_model.eval()
+        return self
+
+    def _ordered_camera_names(self, images: Dict[str, torch.Tensor]) -> tuple[str, ...]:
+        missing = [name for name in self.camera_names if name not in images]
+        if missing:
+            raise ValueError(f'image input missing required cameras. missing={missing}, expected={list(self.camera_names)}')
+        return self.camera_names
+
+    def _batch_size_from_images(self, images: Dict[str, torch.Tensor]) -> int:
+        return int(next(iter(images.values())).shape[0])
+
+    def _normalize_tasks(self, task, batch_size: int) -> list[str]:
+        if task is None:
+            tasks = [''] * batch_size
+        elif isinstance(task, str):
+            tasks = [task] * batch_size
+        elif isinstance(task, tuple):
+            tasks = list(task)
+        elif isinstance(task, list):
+            tasks = task
+        else:
+            raise TypeError(f'task must be str/list/tuple/None, got {type(task)!r}')
+        if len(tasks) != batch_size:
+            raise ValueError(f'task batch size ({len(tasks)}) must match image batch size ({batch_size})')
+        return [item if item.endswith('\n') else f'{item}\n' for item in tasks]
+
+    def tokenize_task(self, task, batch_size: int, device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:
+        tasks = self._normalize_tasks(task, batch_size)
+        old_padding_side = getattr(self.tokenizer, 'padding_side', None)
+        if old_padding_side is not None:
+            self.tokenizer.padding_side = 'right'
+        try:
+            tokenized = self.tokenizer(
+                tasks,
+                padding=self.pad_language_to,
+                max_length=self.tokenizer_max_length,
+                return_tensors='pt',
+                truncation=True,
+            )
+        finally:
+            if old_padding_side is not None:
+                self.tokenizer.padding_side = old_padding_side
+        tokens = tokenized['input_ids'].to(device=device)
+        masks = tokenized['attention_mask'].to(device=device, dtype=torch.bool)
+        return tokens, masks
+
+    def prepare_images(self, images: Dict[str, torch.Tensor]) -> tuple[list[torch.Tensor], list[torch.Tensor]]:
+        camera_names = self._ordered_camera_names(images)
+        reference = images[camera_names[0]]
+        if reference.ndim != 5:
+            raise ValueError(f'expected image tensor shaped (B,T,C,H,W), got {tuple(reference.shape)}')
+        batch_size = reference.shape[0]
+        prepared: list[torch.Tensor] = []
+        masks: list[torch.Tensor] = []
+        for camera_name in camera_names:
+            image = images[camera_name]
+            if image.shape[:2] != reference.shape[:2] or image.shape[2] != reference.shape[2]:
+                raise ValueError(f'camera {camera_name!r} shape {tuple(image.shape)} does not match reference {tuple(reference.shape)}')
+            image = image[:, -1].contiguous().float().clamp(0.0, 1.0)
+            if self.resize_imgs_with_padding is not None:
+                image = _resize_with_pad(image, *self.resize_imgs_with_padding, pad_value=0.0)
+            image = image * 2.0 - 1.0
+            prepared.append(image)
+            masks.append(torch.ones(batch_size, dtype=torch.bool, device=image.device))
+        return prepared, masks
+
+    def prepare_state(self, state: torch.Tensor) -> torch.Tensor:
+        if state.ndim > 2:
+            state = state[:, -1, :]
+        return _pad_vector(state.float(), self.max_state_dim)
+
+    def embed_image(self, image: torch.Tensor) -> torch.Tensor:
+        if hasattr(self.vlm.model, 'get_image_features'):
+            pixel_values = image[:, None, ...]
+            pixel_attention_mask = torch.ones(
+                image.shape[0],
+                1,
+                image.shape[2],
+                image.shape[3],
+                dtype=torch.bool,
+                device=image.device,
+            )
+            with torch.set_grad_enabled(
+                torch.is_grad_enabled() and not self.freeze_vlm and not self.freeze_vision_encoder
+            ):
+                hidden = self.vlm.model.get_image_features(
+                    pixel_values=pixel_values,
+                    pixel_attention_mask=pixel_attention_mask,
+                    return_dict=True,
+                ).pooler_output
+        else:
+            vision_model = self.vlm.model.vision_model
+            with torch.set_grad_enabled(
+                torch.is_grad_enabled() and not self.freeze_vlm and not self.freeze_vision_encoder
+            ):
+                patch_attention_mask = torch.ones(
+                    image.shape[0],
+                    image.shape[2] // self.vlm.config.vision_config.patch_size,
+                    image.shape[3] // self.vlm.config.vision_config.patch_size,
+                    dtype=torch.bool,
+                    device=image.device,
+                ) if hasattr(self.vlm.config, 'vision_config') and hasattr(self.vlm.config.vision_config, 'patch_size') else None
+                hidden = vision_model(
+                    pixel_values=image.to(dtype=vision_model.dtype),
+                    patch_attention_mask=patch_attention_mask,
+                ).last_hidden_state
+                hidden = self.vlm.model.connector(hidden)
+        return hidden
+
+    def embed_language_tokens(self, tokens: torch.Tensor) -> torch.Tensor:
+        return self.vlm.model.text_model.get_input_embeddings()(tokens)
+
+    @staticmethod
+    def make_att_2d_masks(pad_masks: torch.Tensor, att_masks: torch.Tensor) -> torch.Tensor:
+        cumsum = torch.cumsum(att_masks, dim=1)
+        att_2d = cumsum[:, None, :] <= cumsum[:, :, None]
+        pad_2d = pad_masks[:, None, :] * pad_masks[:, :, None]
+        return att_2d & pad_2d
+
+    def embed_prefix(self, images: Dict[str, torch.Tensor], state: torch.Tensor, task=None) -> torch.Tensor:
+        prepared_images, image_masks = self.prepare_images(images)
+        batch_size = prepared_images[0].shape[0]
+        device = prepared_images[0].device
+
+        embs: list[torch.Tensor] = []
+        pad_masks: list[torch.Tensor] = []
+        att_masks: list[int] = []
+
+        for image, image_mask in zip(prepared_images, image_masks, strict=False):
+            img_emb = self.embed_image(image)
+            if self.text_encoder_mode == 'transformer':
+                img_emb = img_emb * torch.tensor(
+                    img_emb.shape[-1] ** 0.5,
+                    dtype=img_emb.dtype,
+                    device=img_emb.device,
+                )
+            num_img_tokens = img_emb.shape[1]
+            embs.append(img_emb)
+            pad_masks.append(image_mask[:, None].expand(batch_size, num_img_tokens))
+            att_masks += [0] * num_img_tokens
+
+        lang_tokens, lang_masks = self.tokenize_task(task, batch_size=batch_size, device=device)
+        lang_emb = self.embed_language_tokens(lang_tokens)
+        if self.text_encoder_mode == 'transformer':
+            lang_emb = lang_emb * (lang_emb.shape[-1] ** 0.5)
+        elif self.text_encoder_mode == 'embedding':
+            lang_emb = lang_emb * lang_masks.unsqueeze(-1).to(dtype=lang_emb.dtype)
+        embs.append(lang_emb)
+        pad_masks.append(lang_masks)
+        att_masks += [0] * lang_emb.shape[1]
+
+        state = self.prepare_state(state).to(device=device)
+        state_emb = self.state_proj(state).unsqueeze(1)
+        embs.append(state_emb)
+        pad_masks.append(torch.ones(batch_size, 1, dtype=torch.bool, device=device))
+        att_masks += [1]
+
+        prefix = torch.cat(embs, dim=1)
+        prefix_pad_mask = torch.cat(pad_masks, dim=1)
+        prefix_att_mask = torch.tensor(att_masks, dtype=torch.bool, device=device)[None, :].expand(batch_size, -1)
+
+        self.last_prefix_pad_mask = prefix_pad_mask
+        self.last_prefix_att_mask = prefix_att_mask
+        self.last_attention_2d_mask = self.make_att_2d_masks(prefix_pad_mask, prefix_att_mask)
+        self.last_position_ids = torch.cumsum(prefix_pad_mask, dim=1) - 1
+        return prefix
+
+    def encode_prefix(self, prefix: torch.Tensor) -> torch.Tensor:
+        if not self.run_text_model:
+            return prefix
+        if self.last_attention_2d_mask is None or self.last_position_ids is None:
+            raise RuntimeError('encode_prefix requires masks from embed_prefix')
+        text_model = self.vlm.model.text_model
+        text_dtype = getattr(text_model, 'dtype', prefix.dtype)
+        if (
+            self.freeze_vlm
+            and not self._warned_trainable_frozen_text_model
+            and any(param.requires_grad for param in text_model.parameters())
+        ):
+            warnings.warn(
+                'freeze_vlm=True but text_model has trainable parameters; temporarily disabling '
+                'their gradients while keeping gradients for trainable prefix inputs.',
+                RuntimeWarning,
+            )
+            self._warned_trainable_frozen_text_model = True
+        with torch.set_grad_enabled(torch.is_grad_enabled()):
+            outputs = text_model(
+                inputs_embeds=prefix.to(dtype=text_dtype),
+                attention_mask=self.last_attention_2d_mask[:, None, :, :],
+                position_ids=self.last_position_ids,
+                use_cache=False,
+                return_dict=True,
+            )
+        return outputs.last_hidden_state
+
+    def forward(self, images: Dict[str, torch.Tensor], state: torch.Tensor | None = None, task=None) -> torch.Tensor:
+        if state is None:
+            raise ValueError('SmolVLAPrefixEncoder.forward requires `state` for SmolVLA-compatible state token')
+        prefix = self.embed_prefix(images=images, state=state, task=task)
+        return self.encode_prefix(prefix)
+
+
+SmolVLMPrefixEncoder = SmolVLAPrefixEncoder
diff --git a/roboimi/vla/models/heads/imf_transformer1d.py b/roboimi/vla/models/heads/imf_transformer1d.py
index ae605c1..cc65a6a 100644
--- a/roboimi/vla/models/heads/imf_transformer1d.py
+++ b/roboimi/vla/models/heads/imf_transformer1d.py
@@ -47,8 +47,19 @@ class IMFTransformer1D(ModuleAttrMixin):
     ) -> None:
         super().__init__()
 
-        if n_head != 1:
-            raise AssertionError('IMFTransformer1D currently supports single-head attention only.')
+        if n_head < 1:
+            raise ValueError(f'n_head must be >= 1, got {n_head}.')
+        if n_kv_head < 1:
+            raise ValueError(f'n_kv_head must be >= 1, got {n_kv_head}.')
+        if n_emb % n_head != 0:
+            raise ValueError(f'n_emb={n_emb} must be divisible by n_head={n_head}.')
+        if n_head % n_kv_head != 0:
+            raise ValueError(f'n_head={n_head} must be divisible by n_kv_head={n_kv_head}.')
+        if backbone_type == 'attnres_full' and (n_emb // n_head) % 2 != 0:
+            raise ValueError(
+                'attnres_full uses RoPE, which requires an even per-head dimension. '
+                f'Got n_emb={n_emb}, n_head={n_head}, head_dim={n_emb // n_head}.'
+            )
         if n_obs_steps is None:
             n_obs_steps = horizon
 
diff --git a/sim_frames/air_insert_front_first.png b/sim_frames/air_insert_front_first.png
new file mode 100644
index 0000000..2141b24
Binary files /dev/null and b/sim_frames/air_insert_front_first.png differ
diff --git a/sim_frames/air_insert_front_last.png b/sim_frames/air_insert_front_last.png
new file mode 100644
index 0000000..6cd51e2
Binary files /dev/null and b/sim_frames/air_insert_front_last.png differ
diff --git a/sim_frames/transfer_front_first.png b/sim_frames/transfer_front_first.png
new file mode 100644
index 0000000..0e8861c
Binary files /dev/null and b/sim_frames/transfer_front_first.png differ
diff --git a/sim_frames/transfer_front_last.png b/sim_frames/transfer_front_last.png
new file mode 100644
index 0000000..20639af
Binary files /dev/null and b/sim_frames/transfer_front_last.png differ
diff --git a/tests/test_eval_vla_headless.py b/tests/test_eval_vla_headless.py
index 768965f..f2d0c0a 100644
--- a/tests/test_eval_vla_headless.py
+++ b/tests/test_eval_vla_headless.py
@@ -598,6 +598,42 @@ class EvalVLAHeadlessTest(unittest.TestCase):
         execute_policy_action.assert_called_once()
         self.assertEqual(fake_env.reset_calls, [sampled_task_state])
 
+    def test_parallel_socket_peg_payloads_do_not_plan_transfer_box_poses(self):
+        cfg = OmegaConf.create(
+            {
+                "agent": {},
+                "eval": {
+                    "num_episodes": 3,
+                    "num_workers": 2,
+                    "task_name": "sim_air_insert_socket_peg",
+                },
+            }
+        )
+
+        with mock.patch.object(
+            eval_vla,
+            "sample_transfer_pose",
+            side_effect=AssertionError(
+                "sim_air_insert_socket_peg parallel rollout must not sample transfer box poses"
+            ),
+        ):
+            payloads, active_workers = eval_vla._build_parallel_worker_payloads(
+                cfg,
+                {"output_dir": None},
+            )
+
+        self.assertEqual(active_workers, 2)
+        planned_episodes = [
+            episode_plan
+            for payload in payloads
+            for episode_plan in payload["episode_plans"]
+        ]
+        self.assertEqual(
+            [plan["episode_index"] for plan in planned_episodes],
+            [0, 1, 2],
+        )
+        self.assertTrue(all("box_pos" not in plan for plan in planned_episodes))
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_imf_transformer1d_external_alignment.py b/tests/test_imf_transformer1d_external_alignment.py
index c3dff34..b71977a 100644
--- a/tests/test_imf_transformer1d_external_alignment.py
+++ b/tests/test_imf_transformer1d_external_alignment.py
@@ -191,6 +191,73 @@ class IMFTransformer1DExternalAlignmentTest(unittest.TestCase):
                 self._optim_group_names(external_model, external_groups),
             )
 
+    def test_attnres_full_supports_multihead_grouped_query_attention(self):
+        local_module = _load_local_module()
+        model = local_module.IMFTransformer1D(
+            input_dim=4,
+            output_dim=4,
+            horizon=6,
+            n_obs_steps=3,
+            cond_dim=5,
+            n_layer=1,
+            n_head=4,
+            n_kv_head=2,
+            n_emb=16,
+            p_drop_emb=0.0,
+            p_drop_attn=0.0,
+            causal_attn=False,
+            time_as_cond=True,
+            n_cond_layers=0,
+            backbone_type='attnres_full',
+        )
+        model.eval()
+
+        attention = model.attnres_backbone.layers[0].fn
+        self.assertEqual(attention.n_heads, 4)
+        self.assertEqual(attention.n_kv_heads, 2)
+        self.assertEqual(attention.d_head, 4)
+
+        sample = torch.randn(2, 6, 4)
+        r = torch.tensor([0.1, 0.4], dtype=torch.float32)
+        t = torch.tensor([0.7, 0.9], dtype=torch.float32)
+        cond = torch.randn(2, 3, 5)
+        with torch.no_grad():
+            output = model(sample=sample, r=r, t=t, cond=cond)
+
+        self.assertEqual(output.shape, (2, 6, 4))
+
+    def test_attnres_full_rejects_invalid_multihead_shapes(self):
+        local_module = _load_local_module()
+        with self.assertRaisesRegex(ValueError, 'must be divisible by n_head'):
+            local_module.IMFTransformer1D(
+                input_dim=4,
+                output_dim=4,
+                horizon=6,
+                n_head=5,
+                n_emb=16,
+                backbone_type='attnres_full',
+            )
+        with self.assertRaisesRegex(ValueError, 'n_head=4 must be divisible by n_kv_head=3'):
+            local_module.IMFTransformer1D(
+                input_dim=4,
+                output_dim=4,
+                horizon=6,
+                n_head=4,
+                n_kv_head=3,
+                n_emb=16,
+                backbone_type='attnres_full',
+            )
+        with self.assertRaisesRegex(ValueError, 'requires an even per-head dimension'):
+            local_module.IMFTransformer1D(
+                input_dim=4,
+                output_dim=4,
+                horizon=6,
+                n_head=2,
+                n_kv_head=1,
+                n_emb=18,
+                backbone_type='attnres_full',
+            )
+
 
 if __name__ == '__main__':
     unittest.main()
diff --git a/tests/test_imf_vla_agent.py b/tests/test_imf_vla_agent.py
index dfccdff..f8405e1 100644
--- a/tests/test_imf_vla_agent.py
+++ b/tests/test_imf_vla_agent.py
@@ -384,7 +384,7 @@ def _make_images(batch_size, obs_horizon, per_camera_fill):
 
 
 class IMFVLAAgentTest(unittest.TestCase):
-    def _make_agent(self, pred_horizon=3, obs_horizon=2, num_action_steps=2):
+    def _make_agent(self, pred_horizon=3, obs_horizon=2, num_action_steps=2, inference_steps=1):
         agent_cls, agent_module = _load_imf_agent_class()
         head = _RecordingLinearIMFHead()
         agent = agent_cls(
@@ -397,7 +397,7 @@ class IMFVLAAgentTest(unittest.TestCase):
             pred_horizon=pred_horizon,
             obs_horizon=obs_horizon,
             diffusion_steps=10,
-            inference_steps=1,
+            inference_steps=inference_steps,
             num_cams=len(_CAMERA_NAMES),
             camera_names=_CAMERA_NAMES,
             num_action_steps=num_action_steps,
@@ -405,7 +405,7 @@ class IMFVLAAgentTest(unittest.TestCase):
         )
         return agent, head, agent_module
 
-    def test_compute_loss_matches_imf_objective_and_masks_padded_actions(self):
+    def test_compute_loss_uses_pseudo_huber_by_default_and_masks_padded_actions(self):
         agent, head, agent_module = self._make_agent(pred_horizon=3, obs_horizon=2)
         images = _make_images(
             batch_size=1,
@@ -447,7 +447,8 @@ class IMFVLAAgentTest(unittest.TestCase):
         du_dt = scale * v + 2.0
         compound_velocity = u + (t - r).view(1, 1, 1) * du_dt
         target = noise - actions
-        elementwise_loss = (compound_velocity - target) ** 2
+        error = compound_velocity - target
+        elementwise_loss = torch.sqrt(1.0 + error.square()) - 1.0
         mask = (~action_is_pad).unsqueeze(-1).to(elementwise_loss.dtype)
         expected_loss = (elementwise_loss * mask).sum() / (mask.sum() * elementwise_loss.shape[-1])
 
@@ -457,6 +458,60 @@ class IMFVLAAgentTest(unittest.TestCase):
         self.assertTrue(torch.allclose(head.calls[0]['t'], t_sample))
         self.assertTrue(torch.allclose(head.calls[0]['cond'], cond))
 
+    def test_compute_loss_can_use_mse_for_ablation(self):
+        agent_cls, agent_module = _load_imf_agent_class()
+        head = _RecordingLinearIMFHead()
+        agent = agent_cls(
+            vision_backbone=_StubVisionBackbone(),
+            state_encoder=nn.Identity(),
+            action_encoder=nn.Identity(),
+            head=head,
+            action_dim=2,
+            obs_dim=1,
+            pred_horizon=3,
+            obs_horizon=2,
+            diffusion_steps=10,
+            inference_steps=1,
+            num_cams=len(_CAMERA_NAMES),
+            camera_names=_CAMERA_NAMES,
+            num_action_steps=2,
+            head_type='transformer',
+            loss_type='mse',
+        )
+        images = _make_images(
+            batch_size=1,
+            obs_horizon=2,
+            per_camera_fill={'r_vis': 1.0, 'top': 2.0, 'front': 3.0},
+        )
+        qpos = torch.tensor([[[0.25], [0.75]]], dtype=torch.float32)
+        actions = torch.tensor(
+            [[[1.0, -1.0], [0.5, 0.25], [-0.5, 1.5]]],
+            dtype=torch.float32,
+        )
+        noise = torch.tensor(
+            [[[0.2, -0.4], [0.1, 0.3], [0.5, -0.2]]],
+            dtype=torch.float32,
+        )
+        t_sample = torch.tensor([0.8], dtype=torch.float32)
+        r_sample = torch.tensor([0.25], dtype=torch.float32)
+
+        with mock.patch.object(agent_module.torch, 'randn_like', return_value=noise), \
+             mock.patch.object(agent_module.torch, 'rand', side_effect=[t_sample, r_sample]):
+            loss = agent.compute_loss({'images': images, 'qpos': qpos, 'action': actions})
+
+        cond = torch.tensor([[[1.0, 2.0, 3.0, 0.25], [1.0, 2.0, 3.0, 0.75]]], dtype=torch.float32)
+        cond_term = cond.mean(dim=(1, 2), keepdim=True)
+        z_t = (1 - t_sample.view(1, 1, 1)) * actions + t_sample.view(1, 1, 1) * noise
+        scale = head.scale.detach()
+        u = scale * z_t + r_sample.view(1, 1, 1) + 2.0 * t_sample.view(1, 1, 1) + cond_term
+        v = scale * z_t + 3.0 * t_sample.view(1, 1, 1) + cond_term
+        du_dt = scale * v + 2.0
+        compound_velocity = u + (t_sample - r_sample).view(1, 1, 1) * du_dt
+        target = noise - actions
+        expected_loss = (compound_velocity - target).square().mean()
+
+        self.assertAlmostEqual(loss.item(), expected_loss.item(), places=6)
+
     def test_predict_action_uses_one_step_imf_sampling_and_image_conditioning(self):
         agent, head, agent_module = self._make_agent(pred_horizon=3, obs_horizon=2)
         agent.infer_scheduler = _ForbiddenScheduler()
@@ -501,6 +556,41 @@ class IMFVLAAgentTest(unittest.TestCase):
         self.assertTrue(torch.allclose(head.calls[0]['t'], torch.ones(2)))
         self.assertTrue(torch.allclose(head.calls[0]['cond'], expected_cond))
 
+    def test_predict_action_supports_two_half_step_imf_sampling(self):
+        agent, head, agent_module = self._make_agent(pred_horizon=3, obs_horizon=2, inference_steps=2)
+        agent.infer_scheduler = _ForbiddenScheduler()
+
+        images = _make_images(
+            batch_size=1,
+            obs_horizon=2,
+            per_camera_fill={'r_vis': 10.0, 'top': 20.0, 'front': 30.0},
+        )
+        qpos = torch.tensor([[[1.0], [2.0]]], dtype=torch.float32)
+        initial_noise = torch.tensor([[[1.0, -1.0], [0.0, 2.0], [3.0, -2.0]]], dtype=torch.float32)
+
+        with mock.patch.object(agent_module.torch, 'randn', return_value=initial_noise):
+            predicted_actions = agent.predict_action(images, qpos)
+
+        expected_cond = torch.tensor(
+            [[[10.0, 20.0, 30.0, 1.0], [10.0, 20.0, 30.0, 2.0]]],
+            dtype=torch.float32,
+        )
+        cond_term = expected_cond.mean(dim=(1, 2), keepdim=True)
+        first_step = 0.75 * initial_noise - 1.25 - 0.5 * cond_term
+        expected_actions = 0.75 * first_step - 0.5 - 0.5 * cond_term
+
+        self.assertEqual(predicted_actions.shape, (1, 3, 2))
+        self.assertTrue(torch.allclose(predicted_actions, expected_actions))
+        self.assertEqual(len(head.calls), 2)
+        self.assertTrue(torch.allclose(head.calls[0]['r'], torch.full((1,), 0.5)))
+        self.assertTrue(torch.allclose(head.calls[0]['t'], torch.ones(1)))
+        self.assertTrue(torch.allclose(head.calls[0]['sample'], initial_noise))
+        self.assertTrue(torch.allclose(head.calls[0]['cond'], expected_cond))
+        self.assertTrue(torch.allclose(head.calls[1]['r'], torch.zeros(1)))
+        self.assertTrue(torch.allclose(head.calls[1]['t'], torch.full((1,), 0.5)))
+        self.assertTrue(torch.allclose(head.calls[1]['sample'], first_step))
+        self.assertTrue(torch.allclose(head.calls[1]['cond'], expected_cond))
+
     def test_select_action_only_regenerates_when_action_queue_is_empty(self):
         agent, _head, _agent_module = self._make_agent(pred_horizon=4, obs_horizon=2, num_action_steps=2)
         observation = {
@@ -723,6 +813,8 @@ class IMFVLAAgentTest(unittest.TestCase):
         self.assertTrue(cfg.agent.head.time_as_cond)
         self.assertFalse(cfg.agent.head.causal_attn)
         self.assertEqual(cfg.agent.inference_steps, 1)
+        self.assertEqual(cfg.agent.loss_type, 'pseudo_huber')
+        self.assertEqual(cfg.agent.pseudo_huber_delta, 1.0)
         self.assertEqual(list(cfg.agent.camera_names), list(_CAMERA_NAMES))
 
         with _stub_optional_modules(include_imf_head=True):
diff --git a/tests/test_simple_robot_dataset_image_loading.py b/tests/test_simple_robot_dataset_image_loading.py
index b305275..2ab6ac9 100644
--- a/tests/test_simple_robot_dataset_image_loading.py
+++ b/tests/test_simple_robot_dataset_image_loading.py
@@ -12,10 +12,13 @@ from roboimi.vla.data.simpe_robot_dataset import SimpleRobotDataset
 
 
 class SimpleRobotDatasetImageLoadingTest(unittest.TestCase):
-    def _write_episode(self, dataset_dir: Path) -> None:
-        episode_path = dataset_dir / "episode_0.hdf5"
+    def _write_episode(self, dataset_dir: Path, episode_idx: int = 0, action_offset: float = 0.0) -> None:
+        episode_path = dataset_dir / f"episode_{episode_idx}.hdf5"
         with h5py.File(episode_path, "w") as root:
-            root.create_dataset("action", data=np.arange(8, dtype=np.float32).reshape(4, 2))
+            root.create_dataset(
+                "action",
+                data=(np.arange(8, dtype=np.float32).reshape(4, 2) + action_offset),
+            )
             root.create_dataset(
                 "observations/qpos",
                 data=np.arange(16, dtype=np.float32).reshape(4, 4),
@@ -79,3 +82,22 @@ class SimpleRobotDatasetImageLoadingTest(unittest.TestCase):
 
         fake_cv2.resize.assert_not_called()
         self.assertEqual(tuple(sample["observation.front"].shape), (2, 3, 8, 8))
+
+    def test_dataset_can_filter_by_episode_indices_and_expose_available_episode_indices(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            dataset_dir = Path(tmpdir)
+            self._write_episode(dataset_dir, episode_idx=3, action_offset=0.0)
+            self._write_episode(dataset_dir, episode_idx=7, action_offset=100.0)
+
+            dataset = SimpleRobotDataset(
+                dataset_dir,
+                obs_horizon=2,
+                pred_horizon=3,
+                camera_names=["front"],
+                episode_indices=[7],
+            )
+            sample = dataset[0]
+
+        self.assertEqual(dataset.available_episode_indices, [7])
+        self.assertEqual(len(dataset), 4)
+        self.assertEqual(sample["action"][0, 0].item(), 100.0)
diff --git a/tests/test_smolvla_imf_agent.py b/tests/test_smolvla_imf_agent.py
new file mode 100644
index 0000000..a2f2d95
--- /dev/null
+++ b/tests/test_smolvla_imf_agent.py
@@ -0,0 +1,341 @@
+import contextlib
+import importlib
+import importlib.machinery
+import sys
+import types
+import unittest
+from pathlib import Path
+from unittest import mock
+
+import torch
+from hydra import compose, initialize_config_dir
+from hydra.core.global_hydra import GlobalHydra
+from hydra.utils import instantiate
+from omegaconf import OmegaConf
+from torch import nn
+
+
+_REPO_ROOT = Path(__file__).resolve().parents[1]
+_CONFIG_DIR = str((_REPO_ROOT / 'roboimi/vla/conf').resolve())
+_CAMERA_NAMES = ('r_vis', 'top', 'front')
+_MISSING = object()
+
+
+class _RecordingHead(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.scale = nn.Parameter(torch.tensor(0.5))
+        self.calls = []
+
+    @staticmethod
+    def _broadcast(value, reference):
+        while value.ndim < reference.ndim:
+            value = value.unsqueeze(-1)
+        return value
+
+    def forward(self, sample, r, t, cond=None):
+        self.calls.append({
+            'sample': sample.detach().clone(),
+            'r': r.detach().clone(),
+            't': t.detach().clone(),
+            'cond': None if cond is None else cond.detach().clone(),
+        })
+        cond_term = 0.0 if cond is None else cond.mean(dim=(1, 2), keepdim=True)
+        return self.scale * sample + self._broadcast(r, sample) + 2.0 * self._broadcast(t, sample) + cond_term
+
+
+class _TaskAwareConditionEncoder(nn.Module):
+    output_dim = 4
+    condition_sequence_length = 3
+    tokens_per_step = 3
+    joint_output_dim = 4
+    camera_names = _CAMERA_NAMES
+    num_cameras = 3
+
+    def __init__(self, **kwargs):
+        super().__init__()
+        self.constructor_kwargs = dict(kwargs)
+        self.bias = nn.Parameter(torch.tensor(0.0))
+        self.calls = []
+
+    def forward(self, images, state, task=None):
+        self.calls.append({'task': task, 'state': state.detach().clone(), 'image_keys': tuple(images.keys())})
+        batch_size = state.shape[0]
+        state_last = state[:, -1, 0]
+        if isinstance(task, str):
+            task_lengths = torch.full((batch_size,), float(len(task)), dtype=state.dtype, device=state.device)
+        else:
+            task_lengths = torch.tensor([float(len(item)) for item in task], dtype=state.dtype, device=state.device)
+        image_marker = images['r_vis'][:, -1].mean(dim=(1, 2, 3))
+        token0 = torch.stack([state_last, task_lengths, image_marker, torch.ones_like(state_last)], dim=-1)
+        token1 = token0 + 1.0
+        token2 = token0 + 2.0
+        return torch.stack([token0, token1, token2], dim=1) + self.bias
+
+
+class _BF16TaskAwareConditionEncoder(_TaskAwareConditionEncoder):
+    def forward(self, images, state, task=None):
+        return super().forward(images, state, task=task).to(dtype=torch.bfloat16)
+
+
+class _StubIMFHead(nn.Module):
+    def __init__(self, input_dim, output_dim, horizon, n_obs_steps, cond_dim, **kwargs):
+        super().__init__()
+        self.constructor_kwargs = {
+            'input_dim': input_dim,
+            'output_dim': output_dim,
+            'horizon': horizon,
+            'n_obs_steps': n_obs_steps,
+            'cond_dim': cond_dim,
+            **kwargs,
+        }
+        self.proj = nn.Linear(input_dim, output_dim)
+        self.cond_obs_emb = nn.Linear(cond_dim, max(cond_dim, 1))
+
+    def forward(self, sample, r, t, cond=None):
+        return torch.zeros_like(sample)
+
+    def get_optim_groups(self, weight_decay):
+        return [
+            {'params': [self.proj.weight], 'weight_decay': weight_decay},
+            {'params': [self.proj.bias, self.cond_obs_emb.weight, self.cond_obs_emb.bias], 'weight_decay': 0.0},
+        ]
+
+
+@contextlib.contextmanager
+def _stub_optional_modules(include_head=False, include_condition_encoder=False):
+    previous = {}
+
+    def inject(name, module):
+        if name not in previous:
+            previous[name] = sys.modules.get(name, _MISSING)
+        sys.modules[name] = module
+
+    diffusers_module = types.ModuleType('diffusers')
+    schedulers_module = types.ModuleType('diffusers.schedulers')
+    ddpm_module = types.ModuleType('diffusers.schedulers.scheduling_ddpm')
+    ddim_module = types.ModuleType('diffusers.schedulers.scheduling_ddim')
+
+    class _FakeScheduler:
+        def __init__(self, num_train_timesteps=100, **kwargs):
+            self.config = types.SimpleNamespace(num_train_timesteps=num_train_timesteps)
+
+    ddpm_module.DDPMScheduler = _FakeScheduler
+    ddim_module.DDIMScheduler = _FakeScheduler
+    diffusers_module.DDPMScheduler = _FakeScheduler
+    diffusers_module.DDIMScheduler = _FakeScheduler
+    diffusers_module.schedulers = schedulers_module
+
+    try:
+        inject('diffusers', diffusers_module)
+        inject('diffusers.schedulers', schedulers_module)
+        inject('diffusers.schedulers.scheduling_ddpm', ddpm_module)
+        inject('diffusers.schedulers.scheduling_ddim', ddim_module)
+        if include_head:
+            import roboimi.vla.models.heads as heads_package
+            head_module = types.ModuleType('roboimi.vla.models.heads.imf_transformer1d')
+            head_module.IMFTransformer1D = _StubIMFHead
+            inject('roboimi.vla.models.heads.imf_transformer1d', head_module)
+            setattr(heads_package, 'imf_transformer1d', head_module)
+        if include_condition_encoder:
+            module = types.ModuleType('tests.fake_smolvla_condition_encoder')
+            module.TaskAwareConditionEncoder = _TaskAwareConditionEncoder
+            module.BF16TaskAwareConditionEncoder = _BF16TaskAwareConditionEncoder
+            inject('tests.fake_smolvla_condition_encoder', module)
+        yield
+    finally:
+        for name, old in reversed(list(previous.items())):
+            if old is _MISSING:
+                sys.modules.pop(name, None)
+            else:
+                sys.modules[name] = old
+
+
+def _compose_cfg(overrides=None):
+    if not OmegaConf.has_resolver('len'):
+        OmegaConf.register_new_resolver('len', lambda x: len(x))
+    GlobalHydra.instance().clear()
+    with initialize_config_dir(version_base=None, config_dir=_CONFIG_DIR):
+        return compose(config_name='config', overrides=list(overrides or []))
+
+
+class SmolVLAIMFAgentTest(unittest.TestCase):
+    def test_compute_loss_uses_pseudo_huber_and_passes_variable_task_to_condition_encoder(self):
+        from roboimi.vla.agent_smolvla_conditioned import SmolVLAIMFAttnResAgent
+
+        condition_encoder = _BF16TaskAwareConditionEncoder()
+        head = _RecordingHead()
+        agent = SmolVLAIMFAttnResAgent(
+            condition_encoder=condition_encoder,
+            action_encoder=nn.Identity(),
+            head=head,
+            action_dim=2,
+            obs_dim=1,
+            pred_horizon=3,
+            obs_horizon=2,
+            diffusion_steps=10,
+            inference_steps=1,
+            num_cams=3,
+            camera_names=_CAMERA_NAMES,
+            num_action_steps=2,
+            head_type='transformer',
+        )
+        images = {
+            'r_vis': torch.full((2, 2, 1, 2, 2), 1.0),
+            'top': torch.full((2, 2, 1, 2, 2), 2.0),
+            'front': torch.full((2, 2, 1, 2, 2), 3.0),
+        }
+        qpos = torch.tensor([[[1.0], [2.0]], [[3.0], [4.0]]])
+        actions = torch.zeros(2, 3, 2)
+        tasks = ['short', 'longer task']
+        noise = torch.tensor(
+            [
+                [[0.2, -0.4], [0.1, 0.3], [0.5, -0.2]],
+                [[-0.1, 0.25], [0.4, -0.5], [0.2, 0.6]],
+            ],
+            dtype=torch.float32,
+        )
+        t_sample = torch.full((2,), 0.75, dtype=torch.float32)
+        r_sample = torch.full((2,), 0.25, dtype=torch.float32)
+        with mock.patch('roboimi.vla.agent_imf.torch.randn_like', return_value=noise), \
+             mock.patch(
+                 'roboimi.vla.agent_imf.torch.rand',
+                 side_effect=[t_sample, r_sample],
+             ):
+            loss = agent.compute_loss({'images': images, 'qpos': qpos, 'action': actions, 'task': tasks})
+        self.assertTrue(torch.isfinite(loss))
+        self.assertEqual(condition_encoder.calls[-1]['task'], tasks)
+        self.assertEqual(head.calls[-1]['cond'].shape, (2, 3, 4))
+        self.assertTrue(torch.allclose(head.calls[-1]['cond'][:, 0, 1], torch.tensor([5.0, 11.0])))
+        cond = head.calls[0]['cond'].float()
+        cond_term = cond.mean(dim=(1, 2), keepdim=True)
+        z_t = (1 - t_sample.view(2, 1, 1)) * actions + t_sample.view(2, 1, 1) * noise
+        scale = head.scale.detach()
+        u = scale * z_t + r_sample.view(2, 1, 1) + 2.0 * t_sample.view(2, 1, 1) + cond_term
+        v = scale * z_t + 3.0 * t_sample.view(2, 1, 1) + cond_term
+        du_dt = scale * v + 2.0
+        compound_velocity = u + (t_sample - r_sample).view(2, 1, 1) * du_dt
+        expected_loss = (torch.sqrt(1.0 + (compound_velocity - noise).square()) - 1.0).mean()
+        mse_loss = (compound_velocity - noise).square().mean()
+        self.assertAlmostEqual(loss.item(), expected_loss.item(), places=4)
+        self.assertLess(loss.item(), mse_loss.item())
+
+        with mock.patch('roboimi.vla.agent_imf.torch.randn', return_value=torch.zeros(2, 3, 2)):
+            pred = agent.predict_action(images, qpos, task=tasks)
+        self.assertEqual(pred.shape, (2, 3, 2))
+        self.assertEqual(condition_encoder.calls[-1]['task'], tasks)
+
+    def test_condition_tokens_are_cast_to_action_head_dtype(self):
+        from roboimi.vla.agent_smolvla_conditioned import SmolVLAIMFAttnResAgent
+
+        condition_encoder = _TaskAwareConditionEncoder()
+        head = _RecordingHead()
+        agent = SmolVLAIMFAttnResAgent(
+            condition_encoder=condition_encoder,
+            action_encoder=nn.Identity(),
+            head=head,
+            action_dim=2,
+            obs_dim=1,
+            pred_horizon=3,
+            obs_horizon=2,
+            diffusion_steps=10,
+            inference_steps=1,
+            num_cams=3,
+            camera_names=_CAMERA_NAMES,
+            num_action_steps=2,
+            head_type='transformer',
+        )
+        images = {
+            'r_vis': torch.full((1, 2, 1, 2, 2), 1.0),
+            'top': torch.full((1, 2, 1, 2, 2), 2.0),
+            'front': torch.full((1, 2, 1, 2, 2), 3.0),
+        }
+        qpos = torch.tensor([[[1.0], [2.0]]], dtype=torch.float32)
+
+        cond = agent._build_cond(images, qpos, task=['pick'])
+
+        self.assertEqual(cond.dtype, head.scale.dtype)
+
+    def test_unknown_dataset_task_uses_configured_task_description(self):
+        from roboimi.vla.agent_smolvla_conditioned import SmolVLAIMFAttnResAgent
+
+        condition_encoder = _TaskAwareConditionEncoder()
+        head = _RecordingHead()
+        agent = SmolVLAIMFAttnResAgent(
+            condition_encoder=condition_encoder,
+            action_encoder=nn.Identity(),
+            head=head,
+            action_dim=2,
+            obs_dim=1,
+            pred_horizon=3,
+            obs_horizon=2,
+            diffusion_steps=10,
+            inference_steps=1,
+            num_cams=3,
+            camera_names=_CAMERA_NAMES,
+            num_action_steps=2,
+            head_type='transformer',
+            task_description='insert the peg into the socket',
+        )
+        images = {
+            'r_vis': torch.full((2, 2, 1, 2, 2), 1.0),
+            'top': torch.full((2, 2, 1, 2, 2), 2.0),
+            'front': torch.full((2, 2, 1, 2, 2), 3.0),
+        }
+        qpos = torch.tensor([[[1.0], [2.0]], [[3.0], [4.0]]], dtype=torch.float32)
+
+        agent._build_cond(images, qpos, task=['unknown', ''])
+
+        self.assertEqual(
+            condition_encoder.calls[-1]['task'],
+            ['insert the peg into the socket', 'insert the peg into the socket'],
+        )
+
+    def test_hydra_config_instantiates_smolvla_imf_attnres_with_condition_encoder_contract(self):
+        cfg = _compose_cfg(overrides=[
+            'agent=smolvla_imf_attnres',
+            'agent.condition_encoder._target_=tests.fake_smolvla_condition_encoder.TaskAwareConditionEncoder',
+            'agent.condition_dim=4',
+            'agent.condition_sequence_length=3',
+            'agent.head.n_layer=1',
+            'agent.head.n_emb=16',
+        ])
+        self.assertEqual(cfg.agent._target_, 'roboimi.vla.agent_smolvla_conditioned.SmolVLAIMFAttnResAgent')
+        self.assertEqual(cfg.agent.head.cond_dim, cfg.agent.condition_dim)
+        self.assertEqual(cfg.agent.head.n_obs_steps, cfg.agent.condition_sequence_length)
+
+        with _stub_optional_modules(include_head=True, include_condition_encoder=True):
+            agent = instantiate(cfg.agent)
+
+        self.assertEqual(agent.per_step_cond_dim, 4)
+        self.assertEqual(agent.condition_sequence_length, 3)
+        self.assertIsInstance(agent.noise_pred_net, _StubIMFHead)
+        self.assertEqual(agent.noise_pred_net.constructor_kwargs['cond_dim'], 4)
+        self.assertEqual(agent.noise_pred_net.constructor_kwargs['n_obs_steps'], 3)
+        self.assertEqual(agent.loss_type, 'pseudo_huber')
+        self.assertEqual(agent.pseudo_huber_delta, 1.0)
+
+    def test_hydra_config_exposes_smolvla_pretrained_vlm_defaults(self):
+        cfg = _compose_cfg(overrides=[
+            'agent=smolvla_imf_attnres',
+        ])
+
+        self.assertEqual(cfg.agent.condition_encoder.model_name, 'HuggingFaceTB/SmolVLM2-500M-Video-Instruct')
+        self.assertTrue(cfg.agent.condition_encoder.load_vlm_weights)
+        self.assertEqual(cfg.agent.condition_encoder.num_vlm_layers, 16)
+        self.assertTrue(cfg.agent.condition_encoder.freeze_vlm)
+        self.assertTrue(cfg.agent.condition_encoder.freeze_vision_encoder)
+        self.assertTrue(cfg.agent.condition_encoder.run_text_model)
+        self.assertEqual(cfg.agent.condition_encoder.max_state_dim, 32)
+        self.assertIsNone(cfg.agent.condition_encoder.dataset_image_resize_shape)
+        self.assertIsNone(cfg.agent.condition_encoder.eval_image_resize_shape)
+        self.assertEqual(cfg.agent.condition_dim, 960)
+        self.assertEqual(cfg.agent.condition_sequence_length, 241)
+        self.assertEqual(cfg.agent.head.cond_dim, 960)
+        self.assertEqual(cfg.agent.head.n_obs_steps, 241)
+        self.assertEqual(cfg.agent.loss_type, 'pseudo_huber')
+        self.assertEqual(cfg.agent.pseudo_huber_delta, 1.0)
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/test_smolvla_prefix_encoder.py b/tests/test_smolvla_prefix_encoder.py
new file mode 100644
index 0000000..ad43c73
--- /dev/null
+++ b/tests/test_smolvla_prefix_encoder.py
@@ -0,0 +1,382 @@
+import types
+import unittest
+from unittest import mock
+
+import torch
+from torch import nn
+
+
+class _FakeVisionOutput:
+    def __init__(self, last_hidden_state):
+        self.last_hidden_state = last_hidden_state
+
+
+class _FakeVisionModel(nn.Module):
+    def __init__(self, hidden_size=4):
+        super().__init__()
+        self.dtype = torch.float32
+        self.scale = nn.Parameter(torch.tensor(1.0))
+        self.calls = []
+        self.hidden_size = hidden_size
+
+    def forward(self, pixel_values=None, patch_attention_mask=None):
+        self.calls.append({
+            'pixel_values': pixel_values.detach().clone(),
+            'patch_attention_mask': patch_attention_mask,
+        })
+        pooled = pixel_values.mean(dim=(2, 3)) * self.scale
+        tokens = torch.stack([pooled, pooled + 1.0], dim=1)
+        return _FakeVisionOutput(tokens)
+
+
+class _FakeConnector(nn.Module):
+    def __init__(self, in_dim=3, out_dim=4):
+        super().__init__()
+        self.proj = nn.Linear(in_dim, out_dim, bias=False)
+        with torch.no_grad():
+            self.proj.weight.copy_(
+                torch.tensor(
+                    [
+                        [1.0, 0.0, 0.0],
+                        [0.0, 1.0, 0.0],
+                        [0.0, 0.0, 1.0],
+                        [1.0, 1.0, 1.0],
+                    ]
+                )
+            )
+
+    def forward(self, x):
+        return self.proj(x)
+
+
+class _FakeTextModel(nn.Module):
+    def __init__(self, vocab_size=32, hidden_size=4, num_layers=6):
+        super().__init__()
+        self.embed = nn.Embedding(vocab_size, hidden_size)
+        self.layers = nn.ModuleList([nn.Linear(hidden_size, hidden_size) for _ in range(num_layers)])
+        self.norm = nn.Identity()
+        self.forward_calls = []
+        with torch.no_grad():
+            for idx in range(vocab_size):
+                self.embed.weight[idx].fill_(float(idx))
+
+    def get_input_embeddings(self):
+        return self.embed
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        position_ids=None,
+        past_key_values=None,
+        inputs_embeds=None,
+        use_cache=None,
+        return_dict=True,
+        cache_position=None,
+        **kwargs,
+    ):
+        del input_ids, past_key_values, use_cache, cache_position, kwargs
+        self.forward_calls.append({
+            'attention_mask': None if attention_mask is None else attention_mask.detach().clone(),
+            'position_ids': None if position_ids is None else position_ids.detach().clone(),
+            'inputs_embeds': inputs_embeds.detach().clone(),
+        })
+        hidden = inputs_embeds
+        for layer in self.layers:
+            hidden = layer(hidden)
+        hidden = self.norm(hidden)
+        if return_dict:
+            return types.SimpleNamespace(last_hidden_state=hidden)
+        return (hidden,)
+
+
+class _FakeVLM(nn.Module):
+    def __init__(self):
+        super().__init__()
+        text_config = types.SimpleNamespace(hidden_size=4, head_dim=2, num_attention_heads=2, num_key_value_heads=1)
+        self.config = types.SimpleNamespace(text_config=text_config)
+        self.model = types.SimpleNamespace(
+            vision_model=_FakeVisionModel(hidden_size=4),
+            connector=_FakeConnector(in_dim=3, out_dim=4),
+            text_model=_FakeTextModel(hidden_size=4, num_layers=6),
+        )
+        self.lm_head = nn.Linear(4, 32)
+
+
+class _FakeTokenizer:
+    fake_image_token_id = 29
+    global_image_token_id = 30
+
+    def __init__(self):
+        self.padding_side = 'left'
+        self.calls = []
+
+    def __call__(self, text, *, padding, max_length, return_tensors, truncation):
+        self.calls.append({
+            'text': list(text),
+            'padding': padding,
+            'max_length': max_length,
+            'return_tensors': return_tensors,
+            'truncation': truncation,
+            'padding_side': self.padding_side,
+        })
+        batch = len(text)
+        ids = torch.zeros(batch, max_length, dtype=torch.long)
+        mask = torch.zeros(batch, max_length, dtype=torch.bool)
+        for row, item in enumerate(text):
+            del item
+            ids[row, :3] = torch.tensor([1, 2, 3])
+            mask[row, :3] = True
+        return {'input_ids': ids, 'attention_mask': mask}
+
+
+class SmolVLAPrefixEncoderTest(unittest.TestCase):
+    def test_loads_pretrained_vlm_with_local_files_only_crops_layers_and_freezes_vlm(self):
+        from roboimi.vla.models.backbones.smolvla_prefix_encoder import SmolVLAPrefixEncoder
+
+        fake_vlm = _FakeVLM()
+        fake_tokenizer = _FakeTokenizer()
+        with mock.patch(
+            'roboimi.vla.models.backbones.smolvla_prefix_encoder.AutoModelForImageTextToText.from_pretrained',
+            return_value=fake_vlm,
+        ) as model_loader, mock.patch(
+            'roboimi.vla.models.backbones.smolvla_prefix_encoder.AutoTokenizer.from_pretrained',
+            return_value=fake_tokenizer,
+        ) as tokenizer_loader:
+            encoder = SmolVLAPrefixEncoder(
+                model_name='HuggingFaceTB/SmolVLM2-500M-Video-Instruct',
+                local_files_only=True,
+                num_vlm_layers=2,
+                freeze_vlm=True,
+                max_state_dim=5,
+                tokenizer_max_length=4,
+                camera_names=('r_vis', 'top'),
+                resize_imgs_with_padding=None,
+            )
+
+        model_loader.assert_called_once()
+        self.assertEqual(model_loader.call_args.kwargs['local_files_only'], True)
+        self.assertEqual(model_loader.call_args.kwargs['torch_dtype'], 'bfloat16')
+        tokenizer_loader.assert_called_once_with(
+            'HuggingFaceTB/SmolVLM2-500M-Video-Instruct',
+            local_files_only=True,
+        )
+        self.assertEqual(len(encoder.vlm.model.text_model.layers), 2)
+        self.assertTrue(all(not p.requires_grad for p in encoder.vlm.parameters()))
+        self.assertFalse(encoder.vlm.training)
+        encoder.train()
+        self.assertFalse(encoder.vlm.training)
+
+    def test_accepts_train_eval_resize_compatibility_fields(self):
+        from roboimi.vla.models.backbones.smolvla_prefix_encoder import SmolVLAPrefixEncoder
+
+        encoder = SmolVLAPrefixEncoder(
+            vlm=_FakeVLM(),
+            tokenizer=_FakeTokenizer(),
+            num_vlm_layers=3,
+            camera_names=('r_vis', 'top'),
+            resize_imgs_with_padding=None,
+            dataset_image_resize_shape=None,
+            eval_image_resize_shape=(640, 480),
+        )
+
+        self.assertIsNone(encoder.dataset_image_resize_shape)
+        self.assertEqual(encoder.eval_image_resize_shape, (640, 480))
+
+    def test_embed_prefix_uses_variable_tasks_camera_order_last_state_and_smolvla_masks(self):
+        from roboimi.vla.models.backbones.smolvla_prefix_encoder import SmolVLAPrefixEncoder
+
+        fake_vlm = _FakeVLM()
+        fake_tokenizer = _FakeTokenizer()
+        encoder = SmolVLAPrefixEncoder(
+            vlm=fake_vlm,
+            tokenizer=fake_tokenizer,
+            num_vlm_layers=3,
+            freeze_vlm=True,
+            train_state_proj=True,
+            max_state_dim=5,
+            tokenizer_max_length=4,
+            camera_names=('r_vis', 'top'),
+            resize_imgs_with_padding=None,
+        )
+        with torch.no_grad():
+            encoder.state_proj.weight.zero_()
+            encoder.state_proj.bias.zero_()
+            encoder.state_proj.weight[:, :4] = torch.eye(4)
+
+        images = {
+            'top': torch.full((2, 2, 3, 2, 2), 0.75),
+            'r_vis': torch.full((2, 2, 3, 2, 2), 0.25),
+        }
+        state = torch.tensor(
+            [
+                [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
+                [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]],
+            ]
+        )
+        tasks = ['pick red cube', 'open drawer']
+        out = encoder.embed_prefix(images=images, state=state, task=tasks)
+
+        # 2 cameras * 2 image tokens + 4 language tokens + 1 state token
+        self.assertEqual(out.shape, (2, 9, 4))
+        self.assertEqual(encoder.output_dim, 4)
+        self.assertEqual(encoder.tokens_per_step, 9)
+        self.assertEqual(encoder.condition_sequence_length, 9)
+
+        self.assertEqual(fake_tokenizer.calls[-1]['text'], ['pick red cube\n', 'open drawer\n'])
+        self.assertEqual(fake_tokenizer.calls[-1]['padding'], 'max_length')
+        self.assertEqual(fake_tokenizer.calls[-1]['max_length'], 4)
+        self.assertEqual(fake_tokenizer.calls[-1]['padding_side'], 'right')
+
+        # Camera order is r_vis then top, and pixels are mapped [0,1] -> [-1,1].
+        first_camera_pixels = fake_vlm.model.vision_model.calls[0]['pixel_values']
+        second_camera_pixels = fake_vlm.model.vision_model.calls[1]['pixel_values']
+        self.assertTrue(torch.allclose(first_camera_pixels, torch.full((2, 3, 2, 2), -0.5)))
+        self.assertTrue(torch.allclose(second_camera_pixels, torch.full((2, 3, 2, 2), 0.5)))
+
+        # Last token is padded last state projected from [4,5,6,0,0] and [10,11,12,0,0].
+        self.assertTrue(torch.allclose(out[0, -1], torch.tensor([4.0, 5.0, 6.0, 0.0])))
+        self.assertTrue(torch.allclose(out[1, -1], torch.tensor([10.0, 11.0, 12.0, 0.0])))
+
+        pad_mask, att_mask = encoder.last_prefix_pad_mask, encoder.last_prefix_att_mask
+        self.assertEqual(pad_mask.shape, (2, 9))
+        self.assertEqual(att_mask.shape, (2, 9))
+        self.assertTrue(torch.all(att_mask[:, :8] == 0))
+        self.assertTrue(torch.all(att_mask[:, -1] == 1))
+
+    def test_forward_can_run_cropped_frozen_text_model_over_prefix_tokens(self):
+        from roboimi.vla.models.backbones.smolvla_prefix_encoder import SmolVLAPrefixEncoder
+
+        fake_vlm = _FakeVLM()
+        fake_tokenizer = _FakeTokenizer()
+        encoder = SmolVLAPrefixEncoder(
+            vlm=fake_vlm,
+            tokenizer=fake_tokenizer,
+            num_vlm_layers=2,
+            freeze_vlm=True,
+            max_state_dim=5,
+            tokenizer_max_length=4,
+            camera_names=('r_vis',),
+            resize_imgs_with_padding=None,
+            run_text_model=True,
+        )
+        images = {
+            'r_vis': torch.full((2, 1, 3, 2, 2), 0.25),
+        }
+        state = torch.tensor([[[1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0]]])
+
+        out = encoder(images, state=state, task=['pick', 'place'])
+
+        # 1 camera * 2 image tokens + 4 language tokens + 1 state token.
+        self.assertEqual(out.shape, (2, 7, 4))
+        self.assertEqual(len(fake_vlm.model.text_model.layers), 2)
+        self.assertEqual(len(fake_vlm.model.text_model.forward_calls), 1)
+        text_call = fake_vlm.model.text_model.forward_calls[-1]
+        self.assertEqual(tuple(text_call['attention_mask'].shape), (2, 1, 7, 7))
+        self.assertEqual(tuple(text_call['position_ids'].shape), (2, 7))
+        self.assertTrue(torch.all(out != 0))
+
+    def test_embedding_text_encoder_mode_prunes_text_layers_and_skips_text_forward(self):
+        from roboimi.vla.models.backbones.smolvla_prefix_encoder import SmolVLAPrefixEncoder
+
+        fake_vlm = _FakeVLM()
+        encoder = SmolVLAPrefixEncoder(
+            vlm=fake_vlm,
+            tokenizer=_FakeTokenizer(),
+            num_vlm_layers=4,
+            freeze_vlm=True,
+            max_state_dim=5,
+            tokenizer_max_length=4,
+            camera_names=('r_vis',),
+            resize_imgs_with_padding=None,
+            text_encoder_mode='embedding',
+        )
+        with torch.no_grad():
+            encoder.state_proj.weight.zero_()
+            encoder.state_proj.bias.zero_()
+            encoder.state_proj.weight[:, :4] = torch.eye(4)
+
+        images = {
+            'r_vis': torch.full((2, 1, 3, 2, 2), 0.25),
+        }
+        state = torch.tensor([[[1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0]]])
+
+        out = encoder(images, state=state, task=['pick', 'place'])
+
+        # 1 camera * 2 image tokens + 4 language embedding tokens + 1 state token.
+        self.assertEqual(out.shape, (2, 7, 4))
+        self.assertEqual(encoder.text_encoder_mode, 'embedding')
+        self.assertFalse(encoder.run_text_model)
+        self.assertEqual(len(fake_vlm.model.text_model.layers), 0)
+        self.assertEqual(len(fake_vlm.model.text_model.forward_calls), 0)
+        self.assertIsInstance(fake_vlm.lm_head, nn.Identity)
+
+        # Embedding mode returns raw prefix embeddings rather than transformer-scaled inputs.
+        # For pixel value 0.25 -> prepared -0.5, connector token 0 is [-0.5,-0.5,-0.5,-1.5].
+        self.assertTrue(torch.allclose(out[0, 0], torch.tensor([-0.5, -0.5, -0.5, -1.5])))
+        self.assertTrue(torch.allclose(out[0, 2], torch.full((4,), 1.0)))
+        self.assertTrue(torch.allclose(out[0, -1], torch.tensor([1.0, 2.0, 3.0, 0.0])))
+
+    def test_text_encoder_mode_rejects_invalid_value(self):
+        from roboimi.vla.models.backbones.smolvla_prefix_encoder import SmolVLAPrefixEncoder
+
+        with self.assertRaisesRegex(ValueError, 'text_encoder_mode'):
+            SmolVLAPrefixEncoder(
+                vlm=_FakeVLM(),
+                tokenizer=_FakeTokenizer(),
+                camera_names=('r_vis',),
+                resize_imgs_with_padding=None,
+                text_encoder_mode='full',
+            )
+
+    def test_frozen_vlm_still_backpropagates_through_text_model_to_state_projection(self):
+        from roboimi.vla.models.backbones.smolvla_prefix_encoder import SmolVLAPrefixEncoder
+
+        fake_vlm = _FakeVLM()
+        encoder = SmolVLAPrefixEncoder(
+            vlm=fake_vlm,
+            tokenizer=_FakeTokenizer(),
+            num_vlm_layers=3,
+            freeze_vlm=True,
+            train_state_proj=True,
+            max_state_dim=5,
+            tokenizer_max_length=4,
+            camera_names=('r_vis',),
+            resize_imgs_with_padding=None,
+            run_text_model=True,
+        )
+        images = {
+            'r_vis': torch.full((2, 1, 3, 2, 2), 0.25),
+        }
+        state = torch.tensor([[[1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0]]])
+
+        out = encoder(images, state=state, task=['pick', 'place'])
+        out[:, -1].sum().backward()
+
+        self.assertIsNotNone(encoder.state_proj.weight.grad)
+        self.assertGreater(float(encoder.state_proj.weight.grad.abs().sum()), 0.0)
+        self.assertTrue(all(param.grad is None for param in fake_vlm.parameters()))
+
+    def test_embed_prefix_rejects_missing_camera_and_task_batch_mismatch(self):
+        from roboimi.vla.models.backbones.smolvla_prefix_encoder import SmolVLAPrefixEncoder
+
+        encoder = SmolVLAPrefixEncoder(
+            vlm=_FakeVLM(),
+            tokenizer=_FakeTokenizer(),
+            num_vlm_layers=3,
+            camera_names=('r_vis', 'top'),
+            resize_imgs_with_padding=None,
+            max_state_dim=5,
+            tokenizer_max_length=4,
+        )
+        images = {'r_vis': torch.rand(2, 1, 3, 2, 2)}
+        state = torch.rand(2, 1, 3)
+        with self.assertRaisesRegex(ValueError, 'missing.*top'):
+            encoder.embed_prefix(images=images, state=state, task=['a', 'b'])
+        images['top'] = torch.rand(2, 1, 3, 2, 2)
+        with self.assertRaisesRegex(ValueError, 'task batch'):
+            encoder.embed_prefix(images=images, state=state, task=['only one'])
+
+
+if __name__ == '__main__':
+    unittest.main()
diff --git a/tests/test_train_vla_rollout_validation.py b/tests/test_train_vla_rollout_validation.py
index 2afd791..91523b1 100644
--- a/tests/test_train_vla_rollout_validation.py
+++ b/tests/test_train_vla_rollout_validation.py
@@ -18,6 +18,17 @@ class _FakeDataset:
         return 4
 
 
+class _FakeEpisodeDataset(_FakeDataset):
+    def __init__(self, episode_indices=None):
+        self.available_episode_indices = [100, 101]
+        self.episode_indices = None if episode_indices is None else list(episode_indices)
+
+    def __len__(self):
+        if self.episode_indices is None:
+            return 8
+        return 4 * len(self.episode_indices)
+
+
 class _FakeLoader:
     def __init__(self, batch, length=1):
         self._batches = [batch] * length
@@ -90,6 +101,9 @@ class _FakeAgent(nn.Module):
     def get_normalization_stats(self):
         return {}
 
+    def predict_action_chunk(self, agent_input):
+        return agent_input['action']
+
 
 class _SequentialLossAgent(nn.Module):
     def __init__(self, losses):
@@ -340,6 +354,175 @@ class TrainVLARolloutValidationTest(unittest.TestCase):
         self.assertIn('image_resize_shape', captured_dataset_kwargs)
         self.assertIsNone(captured_dataset_kwargs['image_resize_shape'])
 
+    def test_explicit_val_episode_indices_split_train_and_val_datasets(self):
+        cfg = OmegaConf.create(
+            {
+                'agent': {
+                    'normalization_type': 'min_max',
+                },
+                'data': {
+                    'dataset_dir': 'unused',
+                    'camera_names': ['front'],
+                },
+                'train': {
+                    'batch_size': 2,
+                    'lr': 1e-4,
+                    'max_steps': 0,
+                    'device': 'cpu',
+                    'disable_cudnn': False,
+                    'num_workers': 0,
+                    'val_split': 0.0,
+                    'val_episode_indices': [100],
+                    'action_mse_val_freq_epochs': 1,
+                    'seed': 42,
+                    'log_freq': 1,
+                    'save_freq': 10,
+                    'use_swanlab': False,
+                    'rollout_val_freq_epochs': 0,
+                    'rollout_validate_on_checkpoint': False,
+                    'rollout_num_episodes': 1,
+                    'warmup_steps': 1,
+                    'scheduler_type': 'constant',
+                    'min_lr': 1e-6,
+                    'weight_decay': 1e-5,
+                    'grad_clip': 1.0,
+                    'pretrained_ckpt': None,
+                    'resume_ckpt': None,
+                },
+                'eval': {
+                    'ckpt_path': 'unused.pt',
+                    'num_episodes': 1,
+                    'headless': True,
+                    'device': 'cpu',
+                    'verbose_action': False,
+                },
+                'experiment': {},
+            }
+        )
+        instantiate_kwargs = []
+
+        def fake_instantiate(config_node, **kwargs):
+            if config_node is cfg.data:
+                instantiate_kwargs.append(dict(kwargs))
+                return _FakeEpisodeDataset(kwargs.get('episode_indices'))
+            if config_node is cfg.agent:
+                return _FakeAgent()
+            raise AssertionError(f'unexpected instantiate config: {config_node!r}')
+
+        def fake_dataloader(_dataset, *, shuffle, **_kwargs):
+            del shuffle, _kwargs
+            return _FakeLoader(
+                {
+                    'observation.front': torch.zeros(1, 3, 2, 2),
+                    'observation.state': torch.zeros(1, 4),
+                    'action': torch.zeros(1, 2),
+                    'action_is_pad': torch.zeros(1, 1, dtype=torch.bool),
+                },
+                length=1,
+            )
+
+        with tempfile.TemporaryDirectory() as tempdir:
+            previous_cwd = os.getcwd()
+            try:
+                os.chdir(tempdir)
+                with mock.patch.object(train_vla, 'instantiate', side_effect=fake_instantiate), \
+                     mock.patch.object(train_vla, 'DataLoader', side_effect=fake_dataloader), \
+                     mock.patch.object(train_vla, 'build_training_optimizer', return_value=_FakeOptimizer(cfg.train.lr)), \
+                     mock.patch.object(train_vla, 'get_lr_schedule_with_warmup', return_value=_FakeScheduler()), \
+                     mock.patch.object(train_vla, 'tqdm', side_effect=lambda iterable, **kwargs: _FakeProgressBar(iterable)), \
+                     mock.patch.object(train_vla, '_init_swanlab', return_value=None), \
+                     mock.patch.object(train_vla, '_finish_swanlab', return_value=None), \
+                     mock.patch.object(train_vla.torch, 'save', return_value=None):
+                    train_vla._run_training(cfg)
+            finally:
+                os.chdir(previous_cwd)
+
+        episode_indices_calls = [kwargs.get('episode_indices') for kwargs in instantiate_kwargs]
+        self.assertEqual(episode_indices_calls, [None, [101], [100]])
+
+    def test_action_mse_requires_explicit_val_episode_indices(self):
+        cfg = OmegaConf.create(
+            {
+                'agent': {
+                    'normalization_type': 'min_max',
+                },
+                'data': {
+                    'dataset_dir': 'unused',
+                    'camera_names': ['front'],
+                },
+                'train': {
+                    'batch_size': 2,
+                    'lr': 1e-4,
+                    'max_steps': 0,
+                    'device': 'cpu',
+                    'disable_cudnn': False,
+                    'num_workers': 0,
+                    'val_split': 0.25,
+                    'val_episode_indices': None,
+                    'action_mse_val_freq_epochs': 1,
+                    'seed': 42,
+                    'log_freq': 1,
+                    'save_freq': 10,
+                    'use_swanlab': False,
+                    'rollout_val_freq_epochs': 0,
+                    'rollout_validate_on_checkpoint': False,
+                    'rollout_num_episodes': 1,
+                    'warmup_steps': 1,
+                    'scheduler_type': 'constant',
+                    'min_lr': 1e-6,
+                    'weight_decay': 1e-5,
+                    'grad_clip': 1.0,
+                    'pretrained_ckpt': None,
+                    'resume_ckpt': None,
+                },
+                'eval': {
+                    'ckpt_path': 'unused.pt',
+                    'num_episodes': 1,
+                    'headless': True,
+                    'device': 'cpu',
+                    'verbose_action': False,
+                },
+                'experiment': {},
+            }
+        )
+
+        def fake_instantiate(config_node, **_kwargs):
+            if config_node is cfg.data:
+                return _FakeDataset()
+            if config_node is cfg.agent:
+                return _FakeAgent()
+            raise AssertionError(f'unexpected instantiate config: {config_node!r}')
+
+        with tempfile.TemporaryDirectory() as tempdir:
+            previous_cwd = os.getcwd()
+            try:
+                os.chdir(tempdir)
+                with mock.patch.object(train_vla, 'instantiate', side_effect=fake_instantiate), \
+                     mock.patch.object(train_vla, 'build_training_optimizer', return_value=_FakeOptimizer(cfg.train.lr)), \
+                     mock.patch.object(train_vla, 'get_lr_schedule_with_warmup', return_value=_FakeScheduler()), \
+                     mock.patch.object(train_vla, '_init_swanlab', return_value=None), \
+                     mock.patch.object(train_vla, '_finish_swanlab', return_value=None):
+                    with self.assertRaisesRegex(ValueError, 'val_episode_indices'):
+                        train_vla._run_training(cfg)
+            finally:
+                os.chdir(previous_cwd)
+
+    def test_compute_action_mse_validation_masks_padding(self):
+        agent = _FakeAgent()
+        val_loader = _FakeLoader(
+            {
+                'observation.front': torch.zeros(1, 2, 3, 2, 2),
+                'observation.state': torch.zeros(1, 2, 4),
+                'action': torch.tensor([[[1.0, 1.0], [5.0, 5.0]]]),
+                'action_is_pad': torch.tensor([[False, True]]),
+            },
+            length=1,
+        )
+
+        mse = train_vla.compute_action_mse_validation(agent, val_loader, device='cpu')
+
+        self.assertEqual(mse, 0.0)
+
     def test_eval_main_delegates_to_plain_run_eval_helper(self):
         cfg = OmegaConf.create(
             {
diff --git a/tests/test_train_vla_swanlab_logging.py b/tests/test_train_vla_swanlab_logging.py
index 3918e9b..7fdab6e 100644
--- a/tests/test_train_vla_swanlab_logging.py
+++ b/tests/test_train_vla_swanlab_logging.py
@@ -113,6 +113,20 @@ class FakeAgent(nn.Module):
     def get_normalization_stats(self):
         return {}
 
+    def predict_action_chunk(self, agent_input):
+        return agent_input['action']
+
+
+class FakeEpisodeDataset(FakeDataset):
+    def __init__(self, episode_indices=None):
+        self.available_episode_indices = [100, 101]
+        self.episode_indices = None if episode_indices is None else list(episode_indices)
+
+    def __len__(self):
+        if self.episode_indices is None:
+            return 8
+        return 4 * len(self.episode_indices)
+
 
 class FakeSwanLab:
     def __init__(self, init_error=None, log_errors=None, finish_error=None, image_errors=None):
@@ -339,6 +353,8 @@ class TrainVLASwanLabLoggingTest(unittest.TestCase):
                 batch_size=2,
                 num_workers=0,
                 val_split=0.25,
+                val_episode_indices=None,
+                action_mse_val_freq_epochs=0,
                 seed=0,
                 lr=1e-3,
                 max_steps=2,
@@ -442,6 +458,8 @@ class TrainVLASwanLabLoggingTest(unittest.TestCase):
                         'batch_size': 2,
                         'num_workers': 0,
                         'val_split': 0.25,
+                        'val_episode_indices': None,
+                        'action_mse_val_freq_epochs': 0,
                         'seed': 0,
                         'lr': 1e-3,
                         'max_steps': 2,
@@ -539,6 +557,58 @@ class TrainVLASwanLabLoggingTest(unittest.TestCase):
 
         self.assertEqual(fake_swanlab.finish_calls, 1)
 
+    def test_run_training_logs_action_mse_only_for_explicit_val_episode_validation(self):
+        module = self._load_train_vla_module()
+        run_training = self._get_run_training(module)
+        cfg = self._make_cfg()
+        cfg.train.val_split = 0.0
+        cfg.train.val_episode_indices = [100]
+        cfg.train.action_mse_val_freq_epochs = 1
+        cfg.train.max_steps = 4
+        cfg.train.save_freq = 100
+        cfg.train.rollout_val_freq_epochs = 0
+        agent = FakeAgent()
+        fake_swanlab = FakeSwanLab()
+        real_import_module = importlib.import_module
+
+        def fake_instantiate(config_node, **kwargs):
+            if config_node is cfg.data:
+                return FakeEpisodeDataset(kwargs.get('episode_indices'))
+            if config_node is cfg.agent:
+                return agent
+            raise AssertionError(f'unexpected instantiate config: {config_node!r}')
+
+        def fake_import_module(name, package=None):
+            if name == 'swanlab':
+                return fake_swanlab
+            return real_import_module(name, package)
+
+        def loader_factory(_dataset, *, shuffle, **_kwargs):
+            batch = {
+                'observation.front': torch.zeros(1, 2, 3, 2, 2),
+                'observation.state': torch.zeros(1, 2, 4),
+                'action': torch.zeros(1, 2, 2),
+                'action_is_pad': torch.zeros(1, 2, dtype=torch.bool),
+            }
+            return FakeLoader(batch)
+
+        with tempfile.TemporaryDirectory() as tempdir:
+            previous_cwd = os.getcwd()
+            try:
+                os.chdir(tempdir)
+                with mock.patch.object(module, 'instantiate', side_effect=fake_instantiate), \
+                     mock.patch.object(module, 'DataLoader', side_effect=loader_factory), \
+                     mock.patch.object(module, 'get_lr_schedule_with_warmup', return_value=FakeScheduler()), \
+                     mock.patch.object(module, 'tqdm', side_effect=lambda iterable, **kwargs: FakeProgressBar(iterable)), \
+                     mock.patch.object(module.torch, 'save', return_value=None), \
+                     mock.patch.object(module.importlib, 'import_module', side_effect=fake_import_module):
+                    run_training(cfg)
+            finally:
+                os.chdir(previous_cwd)
+
+        logged_keys = set().union(*(payload.keys() for payload, _step in fake_swanlab.log_calls))
+        self.assertIn('val/action_mse', logged_keys)
+
     def test_run_training_warns_and_continues_when_swanlab_log_and_finish_fail(self):
         module = self._load_train_vla_module()
         run_training = self._get_run_training(module)
diff --git a/workspace/citation_pool.json b/workspace/citation_pool.json
new file mode 100644
index 0000000..4726ef4
--- /dev/null
+++ b/workspace/citation_pool.json
@@ -0,0 +1,194 @@
+{
+  "papers": [
+    {
+      "bibtex_key": "chi2023diffusion",
+      "title": "Diffusion Policy: Visuomotor Policy Learning via Action Diffusion",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "ho2020denoising",
+      "title": "Denoising Diffusion Probabilistic Models",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "peebles2023scalable",
+      "title": "Scalable Diffusion Models with Transformers",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "lipman2023flow",
+      "title": "Flow Matching for Generative Modeling",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "liu2022flow",
+      "title": "Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "geng2025mean",
+      "title": "Mean Flows for One-step Generative Modeling",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "geng2025improved",
+      "title": "Improved Mean Flows: On the Challenges of Fastforward Generative Models",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "brohan2022rt",
+      "title": "RT-1: Robotics Transformer for Real-World Control at Scale",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "brohan2023rt",
+      "title": "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "kim2024openvla",
+      "title": "OpenVLA: An Open-Source Vision-Language-Action Model",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "shukor2025smolvla",
+      "title": "SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "black2024pi0",
+      "title": "$\\\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "pertsch2025fast",
+      "title": "FAST: Efficient Action Tokenization for Vision-Language-Action Models",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "zhao2023learning",
+      "title": "Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "he2016deep",
+      "title": "Deep Residual Learning for Image Recognition",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "vaswani2017attention",
+      "title": "Attention Is All You Need",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    },
+    {
+      "bibtex_key": "kimi2026attention",
+      "title": "Attention Residuals",
+      "year": null,
+      "abstract": "Verified from arXiv/direct web sources during drafting.",
+      "authors": [
+        {
+          "name": "see refs.bib"
+        }
+      ]
+    }
+  ],
+  "min_cite_paper_count": 15,
+  "n_total": 17,
+  "note": "Manual citation pool constructed after Exa discovery; Semantic Scholar key was not visible in shell and unauthenticated S2 was rate-limited."
+}
\ No newline at end of file
diff --git a/workspace/deduped_candidates.json b/workspace/deduped_candidates.json
new file mode 100644
index 0000000..901457c
--- /dev/null
+++ b/workspace/deduped_candidates.json
@@ -0,0 +1,721 @@
+{
+  "candidates": [
+    {
+      "title": "Visuomotor policy learning via action diffusion - ACM Digital Library",
+      "snippet": "Diffusion policy: : Visuomotor policy learning via action diffusion: International Journal of Robotics Research: Vol 4\n[...]\n, No 1\n[...]\nother-periodical;requested\n[...]\n:string:\n[...]\nPublication Websites;subPage:\n[...]\n:Basic Abstract\n[...]\n;page:string:Article/Chapter View;ctype:string:Journal Content;group\n[...]\n:acm-\n[...]\ntype>other-periodical;website:website:\n[...]\n-site;\n[...]\n:issue:\n[...]\n\\:10.5\n[...]\n55/rbrs.20\n[...]\n.44.issue-10-11;csubtype:string:Periodical;taxonomy:taxonomy:acm-pubtype;pageGroup:string:Publication Pages\"> skip to main content\n\n \n\n \n\nContents\n[...]\nThis paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 15 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion mode",
+      "source_url": "https://dl.acm.org/doi/10.1177/02783649241273668",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://dl.acm.org/doi/10.1177/02783649241273668",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Visuomotor Policy Learning via Action Diffusion - arXiv",
+      "snippet": "# Diffusion Policy: Visuomotor Policy Learning via Action Diffusion\n[...]\nThis paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 15 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details is available diffusion-policy.cs.columbia.edu\n[...]\nIn this work, we s",
+      "source_url": "https://arxiv.org/abs/2303.04137",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2303.04137",
+      "_exa_published_date": "2023-03-07T00:00:00.000Z"
+    },
+    {
+      "title": "[2303.04137v5] Diffusion Policy: Visuomotor Policy Learning via Action Diffusion",
+      "snippet": "[2303.04137v5] Diffusion Policy: Visuomotor Policy Learning via Action Diffusion\n[...]\n# Title:Diffusion Policy: Visuomotor Policy Learning via Action Diffusion\n[...]\n> Abstract:This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 12 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models.",
+      "source_url": "http://arxiv.org/abs/2303.04137v5",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "http://arxiv.org/abs/2303.04137v5",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[2303.04137] Diffusion Policy - ar5iv - arXiv",
+      "snippet": "# Diffusion Policy: Visuomotor Policy Learning via Action Diffusion\n[...]\nThis paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 12 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details will be publicly available.\n[...]\nIn this work, we seek to address thi",
+      "source_url": "https://ar5iv.labs.arxiv.org/html/2303.04137",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://ar5iv.labs.arxiv.org/html/2303.04137",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[2006.11239v2] Denoising Diffusion Probabilistic Models",
+      "snippet": "[2006.11239\n[...]\n] Denoising Diffusion Probabilistic Models\n[...]\n# Title:Denoising Diffusion Probabilistic Models\n[...]\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n[...]\n> Abstract:We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at this https URL\n\n \n\nhttps://doi.org/10.48550/arXiv.2006.11239\n\n \n\narXiv-issued DOI via DataCite",
+      "source_url": "https://arxiv.org/abs/2006.11239v2",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2006.11239v2",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[2006.11239] Denoising Diffusion Probabilistic Models - arXiv",
+      "snippet": "[2006.11239] Denoising Diffusion Probabilistic Models\n[...]\n# Denoising Diffusion Probabilistic Models\n[...]\nJonathan Ho UC Berkeley jonathanho@berkeley.edu &Ajay Jain UC Berkeley ajayj@berkeley.edu &Pieter Abbeel UC Berkeley pabbeel@cs.berkeley.edu\n[...]\nWe present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion.\n[...]\nThis paper presents progress in diffusion probabilistic models [53]. A diffusion probabilistic model (which we will call a “diffusion model” for brevity) is a parameterized Markov chain trained using variational inference to produce samples matching the data after finite time. Transitions of this chain are learned to reverse a diffusion process, which is a Markov chain that gradually adds noise to the data in the opposite direction of s",
+      "source_url": "https://arxiv.org/abs/2006.11239",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2006.11239",
+      "_exa_published_date": "2020-06-19T00:00:00.000Z"
+    },
+    {
+      "title": "Denoising Diffusion Probabilistic Models",
+      "snippet": "Denoising Diffusion Probabilistic Models \n\nAuthorFeedback Bibtex MetaReview Paper Review Supplemental\n\n## Abstract\n[...]\nWe present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.\n\n \n\nDo not remove: This comment is monitored to verify that the site is working properly",
+      "source_url": "https://papers.nips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://papers.nips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html",
+      "_exa_published_date": null
+    },
+    {
+      "title": "",
+      "snippet": "Denoising Diffusion Probabilistic Models\n[...]\njonathanho@berkeley.edu\n[...]\nAjay Jain\n[...]\najayj@berkeley.edu\n[...]\nPieter Abbeel\n[...]\npabbeel@cs.berkeley.edu\n[...]\nWe present high quality image synthesis results using diffusion probabilistic models,\n[...]\na class of latent variable models inspired by considerations from nonequilibrium\n[...]\nthermodynamics. Our best results are obtained by training on a weighted variational\n[...]\nbound designed according to a novel connection between diffusion probabilistic\n[...]\nmodels and denoising score matching with Langevin dynamics, and our models nat\u0002urally admit a progressive lossy decompression scheme that can be interpreted as a\n[...]\ngeneralization of autoregressive decoding. On the unconditional CIFAR10 dataset,\n[...]\nwe obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On\n[...]\n256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our imple\u0002mentation is available at https://github.com/hojonathanho/diffusion.\n[...]\nThis paper presents progress in diffusion probabilistic models [50]. A diffusion probabilistic model\n[...]\n(which we will call a “diffusion model” for brevity) is a parameterized Markov chain trained using\n[...]\nvariational inference to produce samples matching the data after finite time. Transitions of this chain\n[...]\nare learned to reverse a diffusion process, which is a Markov chain that gradually adds noise to the\n[...]\ndata in the opposite direction of sampling until signal",
+      "source_url": "https://arxiv.org/pdf/2006.11239v1",
+      "discovered_for": [
+        "rw.diffusion_policy",
+        "rw.mean_flow",
+        "rw.vla",
+        "rw.attnres",
+        "method.training"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2006.11239v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] Denoising Diffusion Probabilistic Models - arXiv",
+      "snippet": "Denoising Diffusion Probabilistic Models\n[...]\njonathanho@berkeley.edu\n[...]\nAjay Jain\n[...]\najayj@berkeley.edu\n[...]\nPieter Abbeel\n[...]\npabbeel@cs.berkeley.edu\n[...]\nWe present high quality image synthesis results using diffusion probabilistic models,\n[...]\na class of latent variable models inspired by considerations from nonequilibrium\n[...]\nthermodynamics. Our best results are obtained by training on a weighted variational\n[...]\nbound designed according to a novel connection between diffusion probabilistic\n[...]\nmodels and denoising score matching with Langevin dynamics, and our models nat\u0002urally admit a progressive lossy decompression scheme that can be interpreted as a\n[...]\ngeneralization of autoregressive decoding. On the unconditional CIFAR10 dataset,\n[...]\nwe obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On\n[...]\n256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our imple\u0002mentation is available at https://github.com/hojonathanho/diffusion.\n[...]\nThis paper presents progress in diffusion probabilistic models [53]. A diffusion probabilistic model\n[...]\n(which we will call a “diffusion model” for brevity) is a parameterized Markov chain trained using\n[...]\nvariational inference to produce samples matching the data after finite time. Transitions of this chain\n[...]\nare learned to reverse a diffusion process, which is a Markov chain that gradually adds noise to the\n[...]\ndata in the opposite direction of sampling until signal",
+      "source_url": "https://arxiv.org/pdf/2006.11239",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2006.11239",
+      "_exa_published_date": "2020-12-16T00:00:00.000Z"
+    },
+    {
+      "title": "[2212.09748] Scalable Diffusion Models with Transformers - arXiv",
+      "snippet": "William Peebles* UC Berkeley Saining Xie New York University\n[...]\nWe explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops—through increased transformer depth/width or increased number of input tokens—consistently have lower FID. In addition to possessing good scalability properties, our largest DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512 $\\times$ 512 and 256 $\\times$ 256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.\n[...]\n, or Di\n[...]\nfor short. Di\n[...]\nadhere to the best practices of Vision Transformers (ViTs) [10], which have been shown to scale more effectively for visual recognition than\n[...]\nconvolutional networks (e.g., Res\n[...]\n[15]).\n[...]\nMore specifically, we study the scaling behavior of transformers with respect to network complexity vs. sample quality. We show that by constructing and benchmarking the DiT design space under the Latent Diffusion Models (LDMs) [48] framework, where diffusion models are trained within a VAE’s latent space, we can successfully replace the U-Net backbone with a transformer. We further show that DiTs are scalable architectures for diff",
+      "source_url": "https://arxiv.org/abs/2212.09748",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2212.09748",
+      "_exa_published_date": "2022-12-19T00:00:00.000Z"
+    },
+    {
+      "title": "Scalable Diffusion Models with Transformers | IEEE Conference Publication | IEEE Xplore",
+      "snippet": "Scalable Diffusion Models with Transformers | IEEE Conference Publication | IEEE Xplore\n\n \n\n \n\n \n\n### IEEE Account",
+      "source_url": "https://ieeexplore.ieee.org/document/10377858/",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://ieeexplore.ieee.org/document/10377858/",
+      "_exa_published_date": "2025-05-14T00:00:00.000Z"
+    },
+    {
+      "title": "[2212.09748v1] Scalable Diffusion Models with Transformers",
+      "snippet": "[2212.09748v1] Scalable Diffusion Models with Transformers\n[...]\n# Title:Scalable Diffusion Models with Transformers\n[...]\nAuthors: William Peebles, Saining Xie\n[...]\n> Abstract:We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops -- through increased transformer depth/width or increased number of input tokens -- consistently have lower FID. In addition to possessing good scalability properties, our largest DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512x512 and 256x256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.",
+      "source_url": "http://arxiv.org/abs/2212.09748v1",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "http://arxiv.org/abs/2212.09748v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] Scalable Diffusion Models with Transformers | Semantic Scholar",
+      "snippet": "[PDF] Scalable Diffusion Models with Transformers | Semantic Scholar \n\nNavigate Paper Download (opens in a new tab) Share\n[...]\n```\n@article{Peebles2022ScalableDM,\n  title={Scalable Diffusion Models with Transformers},\n  author={William S. Peebles and Saining Xie},\n  journal={2023 IEEE/CVF International Conference on Computer Vision (ICCV)},\n  year={2022},\n  pages={4172-4182},\n  url={https://api.semanticscholar.org/CorpusID:254854389}\n}\n```",
+      "source_url": "https://www.semanticscholar.org/reader/736973165f98105fec3729b7db414ae4d80fcbeb",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://www.semanticscholar.org/reader/736973165f98105fec3729b7db414ae4d80fcbeb",
+      "_exa_published_date": "2022-12-19T14:39:50.000Z"
+    },
+    {
+      "title": "Scalable Diffusion Models with Transformers - IEEE Xplore",
+      "snippet": "Scalable Diffusion Models with Transformers | IEEE Conference Publication | IEEE Xplore\n**\n\n### IEEE Account\n* Change Username/Password\n* Update Address\n### Purchase Details\n* Payment Options\n* Order History\n* View Purchased Documents\n### Profile Information\n* Communications Preferences\n* Profession and Education\n* Technical Interests\n### Need Help?\n* **US & Canada:**+1 800 678 4333\n* **Worldwide:**+1 732 981 0060\n* Contact & Support\n* About IEEE*Xplore*\n* Contact Us\n* Help\n* Accessibility\n* Terms of Use\n* Nondiscrimination Policy\n* Sitemap\n* Privacy & Opting Out of Cookies\nA not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.\n© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.\n**",
+      "source_url": "https://ieeexplore.ieee.org/iel7/10376473/10376477/10377858.pdf",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://ieeexplore.ieee.org/iel7/10376473/10376477/10377858.pdf",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] Flow Matching for Generative Modeling - arXiv",
+      "snippet": "FLOW MATCHING FOR GENERATIVE MODELING\n[...]\nYaron Lipman1,2 Ricky T. Q. Chen1 Heli Ben-Hamu2 Maximilian Nickel1 Matt Le1\n[...]\nWe introduce a new paradigm for generative modeling built on Continuous\n[...]\n(CNFs), allowing us to train CNFs at unprecedented scale.\n[...]\nSpecifically, we present the notion of Flow Matching (FM), a simulation-free\n[...]\napproach for training CNFs based on regressing vector fields of fixed conditional\n[...]\nprobability paths. Flow Matching is compatible with a general family of Gaussian\n[...]\nprobability paths for transforming between noise and data samples—which\n[...]\nsubsumes existing diffusion paths as specific instances. Interestingly, we find\n[...]\nthat employing FM with diffusion paths results in a more robust and stable\n[...]\nalternative for training diffusion models. Furthermore, Flow Matching opens\n[...]\nthe door to training CNFs with other, non-diffusion probability paths. An\n[...]\ninstance of particular interest is using Optimal Transport (OT) displacement\n[...]\ninterpolation to define the conditional probability paths. These paths are more\n[...]\nefficient than diffusion paths, provide faster training and sampling, and result in\n[...]\nbetter generalization. Training CNFs using Flow Matching on ImageNet leads\n[...]\nto consistently better performance than alternative diffusion-based methods in\n[...]\nterms of both likelihood and sample quality, and allows fast and reliable sample\n[...]\ngeneration using off-the-shelf numerical ODE solvers.\n",
+      "source_url": "https://arxiv.org/pdf/2210.02747",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2210.02747",
+      "_exa_published_date": "2023-02-08T00:00:00.000Z"
+    },
+    {
+      "title": "[2210.02747] Flow Matching for Generative Modeling - arXiv",
+      "snippet": "[221\n[...]\n] Flow Matching for Generative Modeling\n[...]\n# Flow Matching for Generative Modeling\n[...]\nYaron Lipman1,2 Ricky T. Q. Chen1 Heli Ben-Hamu2 Maximilian Nickel1 Matt Le1 1Meta AI (FAIR) 2Weizmann Institute of Science\n[...]\nWe introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian probability paths for transforming between noise and data samples—which subsumes existing diffusion paths as specific instances. Interestingly, we find that employing FM with diffusion paths results in a more robust and stable alternative for training diffusion models. Furthermore, Flow Matching opens the door to training CNFs with other, non-diffusion probability paths. An instance of particular interest is using Optimal Transport (OT) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. Training CNFs using Flow Matching on ImageNet leads to consistently better performance than alternative diffusion-based methods in terms of both likelihood and sample quality, and allows fast and reliable sample generation using off-the-s",
+      "source_url": "https://arxiv.org/abs/2210.02747",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2210.02747",
+      "_exa_published_date": "2022-10-06T00:00:00.000Z"
+    },
+    {
+      "title": "[PDF] Flow Matching for Generative Modeling | Semantic Scholar",
+      "snippet": "```\n@article{Lipman2022FlowMF,\n  title={Flow Matching for Generative Modeling},\n  author={Yaron Lipman and Ricky T. Q. Chen and Heli Ben-Hamu and Maximilian Nickel and Matt Le},\n  journal={ArXiv},\n  year={2022},\n  volume={abs/2210.02747},\n  url={https://api.semanticscholar.org/CorpusID:252734897}\n}\n```",
+      "source_url": "https://www.semanticscholar.org/reader/af68f10ab5078bfc519caae377c90ee6d9c504e9",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://www.semanticscholar.org/reader/af68f10ab5078bfc519caae377c90ee6d9c504e9",
+      "_exa_published_date": "2022-10-06T03:39:18.000Z"
+    },
+    {
+      "title": "Flow Matching for Generative Modeling - OpenReview",
+      "snippet": "Flow Matching for Generative Modeling | OpenReview\n\n## Flow Matching for Generative Modeling\n\nICLR 2023 notable top 25%Readers: Everyone\n\nKeywords: continuous normalizing flows, generative models\n\nAbstract: We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian probability paths for transforming between noise and data samples---which subsumes existing diffusion paths as specific instances. Interestingly, we find that employing FM with diffusion paths results in a more robust and stable alternative for training diffusion models. Furthermore, Flow Matching opens the door to training CNFs with other, non-diffusion probability paths. An instance of particular interest is using Optimal Transport (OT) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. Training CNFs using Flow Matching on ImageNet leads to consistently better performance than alternative diffusion-based methods in terms of both likelihood and sample quality, and allows fast and reliable sample generation using off-the-shelf numerical ODE solve",
+      "source_url": "https://openreview.net/forum?id=PqvMRDCJT9t",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=PqvMRDCJT9t",
+      "_exa_published_date": "2022-09-29T08:12:00.000Z"
+    },
+    {
+      "title": "[2505.13447] Mean Flows for One-step Generative Modeling - arXiv",
+      "snippet": "# Mean Flows for One-step Generative Modeling\n[...]\nZhengyang Geng1 Mingyang Deng2 Xingjian Bai2 J. Zico Kolter1 Kaiming He2 1CMU 2MIT Work partly done when visiting MIT.\n[...]\nWe propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantaneous velocity modeled by Flow Matching methods. A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training. Our method, termed the MeanFlow model, is self-contained and requires no pre-training, distillation, or curriculum learning. MeanFlow demonstrates strong empirical performance: it achieves an FID of 3.43 with a single function evaluation (1-NFE) on ImageNet 256 $\\times$ 256 trained from scratch, significantly outperforming previous state-of-the-art one-step diffusion/flow models. Our study substantially narrows the gap between one-step diffusion/flow models and their multi-step predecessors, and we hope it will motivate future research to revisit the foundations of these powerful models.\n[...]\nIn this work, we propose a principled and effective framework, termed MeanFlow, for one-step generation. The core idea is to introduce a new ground-truth field representing the average velocity, in contrast to the instantaneous velocity typically modeled in Flow Matching. Average velocity is defined as the ratio of displacement to a time interval, with displacement give",
+      "source_url": "https://arxiv.org/abs/2505.13447",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2505.13447",
+      "_exa_published_date": "2025-05-19T00:00:00.000Z"
+    },
+    {
+      "title": "Mean Flows for One-step Generative Modeling",
+      "snippet": "# Mean Flows for One-step Generative Modeling\n[...]\nZhengyang Geng1 Mingyang Deng2 Xingjian Bai2 J. Zico Kolter1 Kaiming He2 1CMU 2MIT Work partly done when visiting MIT.\n[...]\nWe propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantaneous velocity modeled by Flow Matching methods. A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training. Our method, termed the MeanFlow model, is self-contained and requires no pre-training, distillation, or curriculum learning. MeanFlow demonstrates strong empirical performance: it achieves an FID of 3.43 with a single function evaluation (1-NFE) on ImageNet 256 $\\times$ 256 trained from scratch, significantly outperforming previous state-of-the-art one-step diffusion/flow models. Our study substantially narrows the gap between one-step diffusion/flow models and their multi-step predecessors, and we hope it will motivate future research to revisit the foundations of these powerful models.\n[...]\nIn this work, we propose a principled and effective framework, termed MeanFlow, for one-step generation. The core idea is to introduce a new ground-truth field representing the average velocity, in contrast to the instantaneous velocity typically modeled in Flow Matching. Average velocity is defined as the ratio of displacement to a time interval, with displacement give",
+      "source_url": "https://arxiv.org/html/2505.13447",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/html/2505.13447",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Mean Flows for One-step Generative Modeling - OpenReview",
+      "snippet": "Mean Flows for One-step Generative Modeling | OpenReview\n\n## Mean Flows for One-step Generative Modeling\n\n### Zhengyang Geng, Mingyang Deng, Xingjian Bai, J Zico Kolter, Kaiming He\n\nNeurIPS 2025 oralEveryone Revisions BibTeX CC BY 4.0\n\nKeywords: Generative Models\n\nAbstract: We propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantaneous velocity modeled by Flow Matching methods. A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training. Our method, termed the \\textit{MeanFlow} model, is self-contained and requires no pre-training, distillation, or curriculum learning. MeanFlow demonstrates strong empirical performance: it achieves an FID of 3.43 with a single function evaluation (1-NFE) on ImageNet 256$\\times$256 trained from scratch, significantly outperforming previous state-of-the-art one-step diffusion/flow models. Our study substantially narrows the gap between one-step diffusion/flow models and their multi-step predecessors, and we hope it will motivate future research to revisit the foundations of these powerful models.\n\nPrimary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)\n\nSubmission Number: 754\n\nLoading",
+      "source_url": "https://openreview.net/forum?id=uWj4s7rMnR",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=uWj4s7rMnR",
+      "_exa_published_date": "2025-10-29T14:53:17.000Z"
+    },
+    {
+      "title": "[PDF] Mean Flows for One-step Generative Modeling | Semantic Scholar",
+      "snippet": "[PDF] Mean Flows for One-step Generative Modeling | Semantic Scholar\n[...]\n```\n@article{Geng2025MeanFF,\n  title={Mean Flows for One-step Generative Modeling},\n  author={Zhengyang Geng and Mingyang Deng and Xingjian Bai and J. Zico Kolter and Kaiming He},\n  journal={ArXiv},\n  year={2025},\n  volume={abs/2505.13447},\n  url={https://api.semanticscholar.org/CorpusID:278769814}\n}\n```",
+      "source_url": "https://www.semanticscholar.org/reader/19df654b0d0f634a451564346a09af8bd348dac0",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://www.semanticscholar.org/reader/19df654b0d0f634a451564346a09af8bd348dac0",
+      "_exa_published_date": "2025-05-19T06:36:24.000Z"
+    },
+    {
+      "title": "Improved Mean Flows: On the Challenges of Fastforward Generative ...",
+      "snippet": "# Improved Mean Flows: On the Challenges of Fastforward Generative Models\n[...]\nZhengyang Geng1,2,3, Yiyang Lu4,2,∗ Zongze Wu3 Eli Shechtman3 J. Zico Kolter1 Kaiming He2 1CMU 2MIT 3Adobe 4THU Equal contribution. Part of this work was done when Z. Geng was interning at Adobe and MIT, and when Y. Lu was interning at MIT.\n[...]\nMeanFlow (MF) has recently been established as a framework for one-step generative modeling. However, its “fastforward” nature introduces key challenges in both the training objective and the guidance mechanism. First, the original MF’s training target depends not only on the underlying ground-truth fields but also on the network itself. To address this issue, we recast the objective as a loss on the instantaneous velocity $v$ , re-parameterized by a network that predicts the average velocity $u$ . Our reformulation yields a more standard regression problem and improves the training stability. Second, the original MF fixes the classifier-free guidance scale during training, which sacrifices flexibility. We tackle this issue by formulating guidance as explicit conditioning variables, thereby retaining flexibility at test time. The diverse conditions are processed through in-context conditioning, which reduces model size and benefits performance. Overall, our improved MeanFlow (iMF) method, trained entirely from scratch, achieves 1.72 FID with a single function evaluation (1-NFE) on ImageNet 256 $\\times$ 256. iMF substantially outperforms prior methods of t",
+      "source_url": "https://arxiv.org/abs/2512.02012",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2512.02012",
+      "_exa_published_date": "2025-12-01T00:00:00.000Z"
+    },
+    {
+      "title": "[2511.19065] Understanding, Accelerating, and Improving MeanFlow Training",
+      "snippet": "Accelerating, and Improving MeanFlow Training\n[...]\n# Title:Understanding, Accelerating, and Improving MeanFlow Training\n\nAuthors: Jin-Young Kim, Hyojun Go, Lea Bogensperger, Julius Erbach, Nikolai Kalischek, Federico Tombari, Konrad Schindler, Dominik Narnhofer\n\nView PDF HTML (experimental)\n[...]\n> Abstract:MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the underlying training dynamics remain unclear. We analyze the interaction between the two velocities and find: (i) well-established instantaneous velocity is a prerequisite for learning average velocity; (ii) learning of instantaneous velocity benefits from average velocity when the temporal gap is small, but degrades as the gap increases; and (iii) task-affinity analysis indicates that smooth learning of large-gap average velocities, essential for one-step generation, depends on the prior formation of accurate instantaneous and small-gap average velocities. Guided by these observations, we design an effective training scheme that accelerates the formation of instantaneous velocity, then shifts emphasis from short- to long-interval average velocity. Our enhanced MeanFlow training yields faster convergence and significantly better few-step generation: With the same DiT-XL backbone, our method reaches an impressive FID of 2.87 on 1-NFE ImageNet 256x256, compared to 3.43 for the conventional MeanFlow baseline. Alternatively, our method matche",
+      "source_url": "https://arxiv.org/abs/2511.19065",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2511.19065",
+      "_exa_published_date": null
+    },
+    {
+      "title": "RT-1: Robotics Transformer for Real-World Control at Scale",
+      "snippet": "By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project’s website and videos can be found at robotics-transformer1.github.io\n[...]\nThe second challenge lies in the design of the model itself. Effective robotic multi-task learning requires a high capacity model, and Transformer (Vaswani et al., 2017) models excel in this regard, particularly when it is necessary to learn many tasks conditioned, as in our case, on language instruct",
+      "source_url": "https://arxiv.org/html/2212.06817",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2212.06817",
+      "_exa_published_date": null
+    },
+    {
+      "title": "RT-1: Robotics Transformer for Real-World Control at Scale - arXiv",
+      "snippet": "By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project’s website and videos can be found at robotics-transformer1.github.io\n[...]\nThe second challenge lies in the design of the model itself. Effective robotic multi-task learning requires a high capacity model, and Transformer (Vaswani et al., 2017) models excel in this regard, particularly when it is necessary to learn many tasks conditioned, as in our case, on language instruct",
+      "source_url": "https://arxiv.org/abs/2212.06817",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2212.06817",
+      "_exa_published_date": "2022-12-13T00:00:00.000Z"
+    },
+    {
+      "title": "Bringing the RT-1-X Foundation Model to a SCARA robot",
+      "snippet": "Traditional robotic systems require specific training data for each task, environment, and robot form. While recent advancements in machine learning have enabled models to generalize across new tasks and environments, the challenge of adapting these models to entirely new settings remains largely unexplored. This study addresses this by investigating the generalization capabilities of the RT-1-X robotic foundation model to a type of robot unseen during its training: a SCARA robot from UMI-RTX.\n[...]\nRecent breakthroughs in machine learning and artificial intelligence suggest that training on large, diverse datasets can lead to highly\n[...]\nwhich often exceed\n[...]\nperformance of models developed for\n[...]\ndatasets tailored to\n[...]\nAs a result,\n[...]\nfield has been exploring more\n[...]\ncan adapt to\n[...]\n. Recent advancements like transformer\n[...]\n’s RT-1 [brohan_rt-1_2022], which demonstrate the potential for\n[...]\nGoogle’s RT-1 model is an impressive work, tested on a collection of real-world robotic experiences, where in different institutes a fleet of robots were performing 700 tasks [brohan_rt-1_2022]. The robots in the training set, such as the Franka, Kuka iiwa, UR5 and the EveryDay robot, can move their end-effector in a spherical working-space. None of the robots in the dataset is of the SCARA (Selective Compliance Assembly Robot Arm) type. With a SCARA robot the movement of z-axis is decoupled from the movement in the x-y plane, which gives a SCARA robot an kidney ",
+      "source_url": "https://arxiv.org/html/2409.03299v1",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2409.03299v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control | OpenReview",
+      "snippet": "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control | OpenReview\n[...]\n## RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control\n[...]\nTL;DR: Vision-language models, trained on Internet-scale data, can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning.\n[...]\nAbstract: We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robotic trajectory data and Internet-scale vision-language tasks, such as visual question answering. In contrast to other approaches, we propose a simple, general recipe to achieve this goal: in order to fit both natural language responses and robotic actions into the same format, we express the actions as text tokens and incorporate them directly into the training set of the model in the same way as natural language tokens. We refer to such category of models as vision-language-action models (VLA) and instantiate an example of such a model, which we call RT-2. Our extensive evaluation (6k evaluation trials) shows th",
+      "source_url": "https://openreview.net/forum?id=XMQgwiJ7KSX",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=XMQgwiJ7KSX",
+      "_exa_published_date": "2023-08-30T15:38:01.000Z"
+    },
+    {
+      "title": "[PDF] RT-2: Vision-Language-Action Models Transfer Web Knowledge to ...",
+      "snippet": "Transfer Web Knowledge\n[...]\n# RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control\n[...]\nWe study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robotic trajectory data and Internet-scale vision-language tasks, such as visual question answering. In contrast to other approaches, we propose a simple, general recipe to achieve this goal: in order to fit both natural language responses and robotic actions into the same format, we express the actions as text tokens and incorporate them directly into the training set of the model in the same way as natural language tokens. We refer to such category of models as vision-language-action models (VLA) and instantiate an example of such a model, which we call RT-2. Our extensive evaluation (6k evaluation trials) shows that our approach leads to performant robotic policies and enables RT-2 to obtain a range of emergent capabilities from Internet-scale training. This includes significantly improved generalization to novel objects, the ability to interpret commands not present in the robot t",
+      "source_url": "https://arxiv.org/pdf/2307.15818",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2307.15818",
+      "_exa_published_date": "2023-07-28T00:00:00.000Z"
+    },
+    {
+      "title": "[2307.15818] RT-2: Vision-Language-Action Models Transfer Web ...",
+      "snippet": "Transfer Web Knowledge\n[...]\n# RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control\n[...]\nWe study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robotic trajectory data and Internet-scale vision-language tasks, such as visual question answering. In contrast to other approaches, we propose a simple, general recipe to achieve this goal: in order to fit both natural language responses and robotic actions into the same format, we express the actions as text tokens and incorporate them directly into the training set of the model in the same way as natural language tokens. We refer to such category of models as vision-language-action models (VLA) and instantiate an example of such a model, which we call RT-2. Our extensive evaluation (6k evaluation trials) shows that our approach leads to performant robotic policies and enables RT-2 to obtain a range of emergent capabilities from Internet-scale training. This includes significantly improved generalization to novel objects, the ability to interpret commands not present in the robot t",
+      "source_url": "https://arxiv.org/abs/2307.15818",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2307.15818",
+      "_exa_published_date": "2023-07-28T00:00:00.000Z"
+    },
+    {
+      "title": "OpenVLA: An Open-Source Vision-Language-Action Model - arXiv",
+      "snippet": "OpenVLA: An Open-Source Vision-Language-Action Model\n[...]\n# OpenVLA: An Open-Source Vision-Language-Action Model\n[...]\nLarge policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rather than training new behaviors from scratch, we can fine-tune such vision-language-action (VLA) models to obtain robust, generalizable policies for visuomotor control. Yet, widespread adoption of VLAs for robotics has been challenging as 1) existing VLAs are largely closed and inaccessible to the public, and 2) prior work fails to explore methods for efficiently fine-tuning VLAs for new tasks, a key component for adoption. Addressing these challenges, we introduce OpenVLA, a 7B-parameter open-source VLA trained on a diverse collection of 970k real-world robot demonstrations. OpenVLA builds on a Llama 2 language model combined with a visual encoder that fuses pretrained features from DINOv2 and SigLIP. As a product of the added data diversity and new model components, OpenVLA demonstrates strong results for generalist manipulation, outperforming closed models such as RT-2-X (55B) by 16.5% in absolute task success rate across 29 tasks and multiple robot embodiments, with 7x fewer parameters. We further show that we can effectively fine-tune OpenVLA for new settings, with especially strong generalization results in multi-task environments involving multiple objects and strong language",
+      "source_url": "https://arxiv.org/abs/2406.09246",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2406.09246",
+      "_exa_published_date": "2024-06-13T00:00:00.000Z"
+    },
+    {
+      "title": "[PDF] OpenVLA: An Open-Source Vision-Language-Action Model | Semantic Scholar",
+      "snippet": "[PDF] OpenVLA: An Open-Source Vision-Language-Action Model | Semantic Scholar \n\nNavigate Paper Download (opens in a new tab) Share\n[...]\n```\n@article{Kim2024OpenVLAAO,\n  title={OpenVLA: An Open-Source Vision-Language-Action Model},\n  author={Moo Jin Kim and Karl Pertsch and Siddharth Karamcheti and Ted Xiao and Ashwin Balakrishna and Suraj Nair and Rafael Rafailov and Ethan Paul Foster and Grace Lam and Pannag R. Sanketi and Quan Vuong and Thomas Kollar and Benjamin Burchfiel and Russ Tedrake and Dorsa Sadigh and Sergey Levine and Percy Liang and Chelsea Finn},\n  journal={ArXiv},\n  year={2024},\n  volume={abs/2406.09246},\n  url={https://api.semanticscholar.org/CorpusID:270440391}\n}\n```",
+      "source_url": "https://www.semanticscholar.org/reader/8f9ceb5ffad8e7a066dfc9d9aaa5153b714740ee",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://www.semanticscholar.org/reader/8f9ceb5ffad8e7a066dfc9d9aaa5153b714740ee",
+      "_exa_published_date": "2024-06-13T09:02:12.000Z"
+    },
+    {
+      "title": "OpenVLA: An Open-Source Vision-Language-Action Model",
+      "snippet": "OpenVLA: An Open-Source Vision-Language-Action Model | OpenReview\n\n## OpenVLA: An Open-Source Vision-Language-Action Model\n\n### Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan P Foster, Pannag R Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, Chelsea Finn \n\nCoRL 2024everyonesince 05 Sept 2024\">Everyone Revisions BibTeX CC BY 4.0\n\nKeywords: Vision-Language-Action Models, Generalist Policies, Large-scale Robot Learning, Robotic Manipulation, Robotics, Vision-Language Models\n\nTL;DR: We introduce OpenVLA, a state-of-the-art, open-source 7B-parameter VLA model that obtains strong performance for cross-embodiment robot control out-of-the-box and can be easily adapted to new robot setups via parameter-efficient fine-tuning.\n\nAbstract: Large policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rather than training new behaviors from scratch, we can fine-tune such vision-language-action (VLA) models to obtain robust, generalizable policies for visuomotor control. Yet, widespread adoption of VLAs for robotics has been challenging as 1) existing VLAs are largely closed and inaccessible to the public, and 2) prior work fails to explore methods for efficiently fine-tuning VLAs for new tasks, a key component for adoption. Addressing these challeng",
+      "source_url": "https://openreview.net/forum?id=ZMnD6QZAE6",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=ZMnD6QZAE6",
+      "_exa_published_date": "2024-09-05T00:00:00.000Z"
+    },
+    {
+      "title": "[2406.09246v3] OpenVLA: An Open-Source Vision-Language-Action Model",
+      "snippet": "[2406.09246v3] OpenVLA: An Open-Source Vision-Language-Action Model\n[...]\n# Title:OpenVLA: An Open-Source Vision-Language-Action Model\n[...]\n> Abstract:Large policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rather than training new behaviors from scratch, we can fine-tune such vision-language-action (VLA) models to obtain robust, generalizable policies for visuomotor control. Yet, widespread adoption of VLAs for robotics has been challenging as 1) existing VLAs are largely closed and inaccessible to the public, and 2) prior work fails to explore methods for efficiently fine-tuning VLAs for new tasks, a key component for adoption. Addressing these challenges, we introduce OpenVLA, a 7B-parameter open-source VLA trained on a diverse collection of 970k real-world robot demonstrations. OpenVLA builds on a Llama 2 language model combined with a visual encoder that fuses pretrained features from DINOv2 and SigLIP. As a product of the added data diversity and new model components, OpenVLA demonstrates strong results for generalist manipulation, outperforming closed models such as RT-2-X (55B) by 16.5% in absolute task success rate across 29 tasks and multiple robot embodiments, with 7x fewer parameters. We further show that we can effectively fine-tune OpenVLA for new settings, with especially strong generalization results in multi-task environments involving mult",
+      "source_url": "https://arxiv.org/abs/2406.09246v3",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2406.09246v3",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Revision History for OpenVLA: An Open-Source... - OpenReview",
+      "snippet": "Revisions | OpenReview\n\nLoading",
+      "source_url": "https://openreview.net/revisions?id=ZMnD6QZAE6",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/revisions?id=ZMnD6QZAE6",
+      "_exa_published_date": null
+    },
+    {
+      "title": "A Vision-Language-Action Model for Affordable and Efficient Robotics",
+      "snippet": "SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\n# SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\nVision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into vision-language-action (VLA) models that enable natural language-driven perception and control. However, existing VLAs are typically massive–often with billions of parameters–leading to high training costs and limited real-world deployability. Moreover, they rely on academic and industrial datasets, overlooking the growing availability of community-collected data from affordable robotic platforms. In this work, we present SmolVLA, a small, efficient, and community-driven VLA that drastically reduces both training and inference costs, while retaining competitive performance. SmolVLA is designed to be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. To further improve responsiveness, we introduce an asynchronous inference stack decoupling perception and action prediction from action execution, allowing higher control rates with chunked action generation. Despite its compact size, SmolVLA achieves performance comparable to VLAs that are 10 $\\times$ larger. We evaluate SmolVLA on a range of both simulated as well as real-world robotic bench",
+      "source_url": "https://arxiv.org/abs/2506.01844",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2506.01844",
+      "_exa_published_date": "2025-06-02T00:00:00.000Z"
+    },
+    {
+      "title": "[PDF] SmolVLA: A Vision-Language-Action Model for Affordable ... - arXiv",
+      "snippet": "SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\n# SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\nVision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into vision-language-action (VLA) models that enable natural language-driven perception and control. However, existing VLAs are typically massive–often with billions of parameters–leading to high training costs and limited real-world deployability. Moreover, they rely on academic and industrial datasets, overlooking the growing availability of community-collected data from affordable robotic platforms. In this work, we present SmolVLA, a small, efficient, and community-driven VLA that drastically reduces both training and inference costs, while retaining competitive performance. SmolVLA is designed to be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. To further improve responsiveness, we introduce an asynchronous inference stack decoupling perception and action prediction from action execution, allowing higher control rates with chunked action generation. Despite its compact size, SmolVLA achieves performance comparable to VLAs that are 10 $\\times$ larger. We evaluate SmolVLA on a range of both simulated as well as real-world robotic bench",
+      "source_url": "https://arxiv.org/pdf/2506.01844",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2506.01844",
+      "_exa_published_date": "2025-06-02T00:00:00.000Z"
+    },
+    {
+      "title": "SmolVLA: A vision-language-action model for affordable and ... - arXiv",
+      "snippet": "SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\n# SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\nVision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into vision-language-action (VLA) models that enable natural language-driven perception and control. However, existing VLAs are typically massive–often with billions of parameters–leading to high training costs and limited real-world deployability. Moreover, they rely on academic and industrial datasets, overlooking the growing availability of community-collected data from affordable robotic platforms. In this work, we present SmolVLA, a small, efficient, and community-driven VLA that drastically reduces both training and inference costs, while retaining competitive performance. SmolVLA is designed to be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. To further improve responsiveness, we introduce an asynchronous inference stack decoupling perception and action prediction from action execution, allowing higher control rates with chunked action generation. Despite its compact size, SmolVLA achieves performance comparable to VLAs that are 10 $\\times$ larger. We evaluate SmolVLA on a range of both simulated as well as real-world robotic bench",
+      "source_url": "https://arxiv.org/html/2506.01844v1",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2506.01844v1",
+      "_exa_published_date": "2025-06-02T00:00:00.000Z"
+    },
+    {
+      "title": "[PDF] SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics | Semantic Scholar",
+      "snippet": "[PDF] SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics | Semantic Scholar \n\nNavigate Paper Download (opens in a new tab) Share\n[...]\n```\n@article{Shukor2025SmolVLAAV,\n  title={SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},\n  author={Mustafa Shukor and Dana Aubakirova and Francesco Capuano and Pepijn Kooijmans and Steven Palma and Adil Zouitine and Michel Aractingi and Caroline Pascal and Martino Russi and Andr{\\'e}s Marafioti and Simon Alibert and Matthieu Cord and Thomas Wolf and R{\\'e}mi Cad{\\`e}ne},\n  journal={ArXiv},\n  year={2025},\n  volume={abs/2506.01844},\n  url={https://api.semanticscholar.org/CorpusID:279119427}\n}\n```",
+      "source_url": "https://www.semanticscholar.org/reader/6ab4d113676d00e74b55e918fee4c7affaa8652f",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://www.semanticscholar.org/reader/6ab4d113676d00e74b55e918fee4c7affaa8652f",
+      "_exa_published_date": "2025-06-02T13:54:14.000Z"
+    },
+    {
+      "title": "Lite VLA: Efficient Vision-Language-Action Control on CPU-Bound Edge Robots",
+      "snippet": "By leveraging NF4 quantization and the llama-cpp runtime, the proposed LiteVLA implementation pioneers the CPU-only deployment path, achieving functional asynchronous visuomotor control on the low-cost Raspberry Pi 4. This represents a novel deployment strategy not demonstrated by prior GPU-centric VLA frameworks such as SmolVLA by Shukor et al. [22], whose work focused primarily on static robotic arms. Beyond proving technical feasibility, this work establishes a scalable methodology for deploying generalist robot intelligence under strict computational budgets.\n[...]\nParameter-efficient adaptation. We fine-tune a compact SmolVLM backbone using LoRA (rank 8, $\\alpha{=}8$ , dropout 0.1) to specialize visuomotor translation under tight memory/compute budgets (Alg. 1; Sec. III, pp. 2–3).\n[...]\n. 4).\n[...]\nLarge-scale multimodal systems such as PaLM-E, SayCan, and RT-2 have shown that unified language-conditioned reasoning enables robots to follow natural language commands and execute complex manipulation tasks. However, these approaches rely heavily on cloud-based computation and high-end GPUs, making them impractical for resource-limited or field-deployed robots. SMolVLA by Shukor et al. [22] introduced a small and efficient vision-language-action framework designed for community-driven robotic experimentation. It demonstrated that compact multimodal transformers could achieve competitive visuomotor reasoning performance while running on consumer-grade GPUs or CPUs. Nonetheles",
+      "source_url": "https://arxiv.org/html/2511.05642",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2511.05642",
+      "_exa_published_date": null
+    },
+    {
+      "title": "A Vision-Language-Action Flow Model for General Robot Control",
+      "snippet": "𝜋₀: A Vision-Language-Action Flow Model for General Robot Control\n[...]\n# $\\pi_{0}$ : A Vision-Language-Action Flow Model for General Robot Control\n[...]\nholds tremendous promise to unlock\n[...]\nfull potential of flexible, general, and dexterous\n[...]\nsystems, as well as to address some of\n[...]\ndeepest questions in artificial intelligence\n[...]\nHowever, bringing robot learning to\n[...]\nlevel of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how\n[...]\ncan design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of\n[...]\nability to perform tasks via direct prompting, follow language instructions from people and from a high-level VLM policy, and\n[...]\nability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes.\n[...]\nIn this paper, we present a prototype model and learning framework, which we call $\\pi_",
+      "source_url": "https://arxiv.org/abs/2410.24164",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2410.24164",
+      "_exa_published_date": "2024-10-31T00:00:00.000Z"
+    },
+    {
+      "title": "π: A Vision-Language-Action Flow Model for General Robot Control | OpenReview",
+      "snippet": "π: A Vision-Language-Action Flow Model for General Robot Control | OpenReview\n[...]\n## π: A Vision-Language-Action Flow Model for General Robot Control\n[...]\nAbstract: Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how we can design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people and from a high-level VLM policy, and its ability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes.",
+      "source_url": "https://openreview.net/forum?id=38a45ho9Nq",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=38a45ho9Nq",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Efficient Action Tokenization for Vision-Language-Action Models",
+      "snippet": "Autoregressive sequence models, such as Transformer-based vision-language action (VLA) policies, can be tremendously effective for capturing complex and generalizable robotic behaviors. However, such models require us to choose a tokenization of our continuous action signals, which determines how the discrete symbols predicted by the model map to continuous robot actions. We find that current approaches for robot action tokenization, based on simple per-dimension, per-timestep binning schemes, typically perform poorly when learning dexterous skills from high-frequency robot data. To address this challenge, we propose a new compression-based tokenization scheme for robot actions, based on the discrete cosine transform. Our tokenization approach, Frequency-space Action Sequence Tokenization (FAST), enables us to train autoregressive VLAs for highly dexterous and high-frequency tasks where standard discretization methods fail completely. Based on FAST, we release FAST+, a universal robot action tokenizer, trained on 1M real robot action trajectories. It can be used as a black-box tokenizer for a wide range of robot action sequences, with diverse action spaces and control frequencies. Finally, we show that, when combined with the $\\bm{\\pi_{0}}$ VLA, our method can scale to training on 10k hours of robot data and match the performance of diffusion VLAs, while reducing training time by up to 5x.\n[...]\nFigure 4: Overview of the FAST action tokenization pipeline. Given a normalized c",
+      "source_url": "https://arxiv.org/abs/2501.09747",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2501.09747",
+      "_exa_published_date": "2025-01-16T00:00:00.000Z"
+    },
+    {
+      "title": "FAST: Efficient Action Tokenization for Vision-Language ... - arXiv",
+      "snippet": "Autoregressive sequence models, such as Transformer-based vision-language action (VLA) policies, can be tremendously effective for capturing complex and generalizable robotic behaviors. However, such models require us to choose a tokenization of our continuous action signals, which determines how the discrete symbols predicted by the model map to continuous robot actions. We find that current approaches for robot action tokenization, based on simple per-dimension, per-timestep binning schemes, typically perform poorly when learning dexterous skills from high-frequency robot data. To address this challenge, we propose a new compression-based tokenization scheme for robot actions, based on the discrete cosine transform. Our tokenization approach, Frequency-space Action Sequence Tokenization (FAST), enables us to train autoregressive VLAs for highly dexterous and high-frequency tasks where standard discretization methods fail completely. Based on FAST, we release FAST+, a universal robot action tokenizer, trained on 1M real robot action trajectories. It can be used as a black-box tokenizer for a wide range of robot action sequences, with diverse action spaces and control frequencies. Finally, we show that, when combined with the $\\bm{\\pi_{0}}$ VLA, our method can scale to training on 10k hours of robot data and match the performance of diffusion VLAs, while reducing training time by up to 5x.\n[...]\nFigure 4: Overview of the FAST action tokenization pipeline. Given a normalized c",
+      "source_url": "https://arxiv.org/html/2501.09747v1",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2501.09747v1",
+      "_exa_published_date": "2025-01-16T00:00:00.000Z"
+    },
+    {
+      "title": "ActionCodec: What Makes for Good Action Tokenizers",
+      "snippet": "without any robotics\n[...]\nintroduce ActionCodec, a robust action tokenizer that integrates the\n[...]\n. Moreover, ActionCodec leverages Residual Vector Quantization (RVQ) (Lee et al., 2022) post-training to refine reconstruction fidelity and incorporates embodiment-specific soft prompts to facilitate knowledge transfer across diverse robotic platforms\n[...]\nthat VLA\n[...]\nActionCodec, without any additional architectural modifications\n[...]\nefficiency, success rates\n[...]\n. ActionCodec achieves SOTA performance in both\n[...]\nenvironments, providing a systematic\n[...]\nfor the future of VQ-\n[...]\nOur contributions are\n[...]\nas follows:\n[...]\nTokenization Schemes\n[...]\n20\n[...]\nsuffers from low training efficiency and ignores the\n[...]\nparallel decoding (\n[...]\n(Goy\n[...]\n., 2025),\n[...]\nfundamental inefficiencies of heuristic binning. Other\n[...]\nrepresent actions as strings for direct VLM prediction (Hancock et\n[...]\nhowever, this approach\n[...]\nno significant performance benefits while greatly increasing the token budget and extending latency to several seconds, limiting\n[...]\nPertsch et al., 2025) introduces Byte-Pair Encoding (BPE) on frequency-domain signals; however, its reliance on fixed geometric priors limits its capacity for cross-embodiment knowledge transfer. Data-driven schemes, particularly those based on Vector Quantization (VQ) (Wang et al., 2025b; Belkhale and Sadigh, 2024; Mete et al., 2024; Lee et al., 2024), offer a more flexible alternative by learning disc",
+      "source_url": "https://arxiv.org/abs/2602.15397",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2602.15397",
+      "_exa_published_date": null
+    },
+    {
+      "title": "OAT: Ordered Action Tokenization",
+      "snippet": "action tokenization\n[...]\nTo bridge this gap, we introduce Ordered Action Tokenization (OAT), a learned action tokenizer that discretizes continuous action chunks into highly compressed and causally ordered token sequences. OAT employs transformer-based register tokens to aggregate temporal information, finite scalar quantization (FSQ) to construct a discrete bottleneck, and nested dropout to explicitly induce ordering that aligns the latent space with autoregressive generation. The resulting tokenization ensures that any token prefix corresponds to a plausible action chunk. Beyond improved modelability, the ordered structure learned by OAT enables a key capability absent from prior approaches: prefix-based decoding. Autoregressive policies may terminate generation early and still produce valid actions, yielding a natural trade-off between computation and action fidelity. As additional tokens are generated, decoded actions are progressively refined.\n[...]\nAn alternative line of work explores frequency-domain compression, for instance Frequency-space Action Sequence Tokenization (FAST) [49], which employs the Discrete Cosine Transform (DCT) to decompose action chunks into frequency coefficients, followed by Byte Pair Encoding (BPE) [18]. FAST achieves high information density (P.1), and crucially, its low-frequency components first then high-frequency components ordering (P.3) improves downstream autoregressive policies: early token predictions capture the overall trajectory s",
+      "source_url": "https://arxiv.org/html/2602.04215",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2602.04215",
+      "_exa_published_date": null
+    },
+    {
+      "title": "PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding",
+      "snippet": "the above challenges, we present a novel parallel decoding framework for the mainstream VLA model with action chunking, called Parallel\n[...]\nfor VLA (PD-VLA). Fig. 1 illustrates the\n[...]\nconcept of our parallel decoding approach. Our\n[...]\naction decoding as a system of\n[...]\nsolved through parallel fixed-point iteration methods, e.g., Jacobi fix-point iteration method [32]. This approach preserves\n[...]\nimproving decoding speed\n[...]\nthat we only accelerate the decoding process\n[...]\nVLA inference.\n[...]\n, our method enables friendly\n[...]\ntraining-free acceleration without redesign and modification of models (\n[...]\n, our method\n[...]\nsynergy with existing acceleration\n[...]\nVarious acceleration strategies, including quantization [21] and token pruning [5], have been effectively applied to LLMs, yet they often fail to meet the stringent real-time requirements of action generation. Efforts to enhance efficiency have led to architectural modifications in VLA models, such as DeeR-VLA [43], which dynamically adjusts inference depth, and QAIL [33], which integrates quantization-aware training. Further innovations, like RoboMamba [25] and TinyVLA [41], replace traditional attention mechanisms or focus on developing lightweight models from the ground up, frequently necessitating model re-training and additional data collection. Meanwhile, VLA-Cache [42] selectively caches static tokens and recomputes only dynamic or task-relevant ones. FAST [34] proposes a compression-based toke",
+      "source_url": "https://arxiv.org/html/2503.02310v2",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2503.02310v2",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware | OpenReview",
+      "snippet": "Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware | OpenReview\n[...]\n## Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware\n[...]\nAbstract: Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback. Performing these tasks typically requires high-end robots, accurate sensors, or careful calibration, which can be expensive and difficult to set up. Can learning enable low-cost and imprecise hardware to perform these fine manipulation tasks? We present a low-cost system that performs end-to-end imitation learning directly from real demonstrations, collected with a custom teleoperation interface. Imitation learning, however, presents its own challenges, particularly in high-precision domains: errors in the policy can compound over time, and human demonstrations can be non-stationary. To address these challenges, we develop a simple yet novel algorithm, Action Chunking with Transformers (ACT), which learns a generative model over action sequences. ACT allows the robot to learn 6 difficult tasks in the real world, such as opening a translucent condiment cup and slotting a battery with 80-90% success, with only 10 minutes worth of demonstrations.",
+      "source_url": "https://openreview.net/forum?id=e8Eu1lqLaf",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=e8Eu1lqLaf",
+      "_exa_published_date": "2023-07-09T06:52:40.000Z"
+    },
+    {
+      "title": "Learning Fine-Grained Bimanual Manipulation with Low-Cost ... - arXiv",
+      "snippet": "Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback. Performing these tasks typically requires high-end robots, accurate sensors, or careful calibration, which can be expensive and difficult to set up. Can learning enable low-cost and imprecise hardware to perform these fine manipulation tasks? We present a low-cost system that performs end-to-end imitation learning directly from real demonstrations, collected with a custom teleoperation interface. Imitation learning, however, presents its own challenges, particularly in high-precision domains: errors in the policy can compound over time, and human demonstrations can be non-stationary. To address these challenges, we develop a simple yet novel algorithm, Action Chunking with Transformers (ACT), which learns a generative model over action sequences. ACT allows the robot to learn 6 difficult tasks in the real world, such as opening a translucent condiment cup and slotting a battery with 80-90% success, with only 10 minutes worth of demonstrations. Project website: tonyzhaozh.github.io/aloha\n[...]\nImitation learning algorithm. Tasks that require precision and visual feedback present a significant challenge for imitation learning, even with high-quality demonstrations. Small errors in the predicted action can incur large differences in the state, exacerbating the “co",
+      "source_url": "https://arxiv.org/abs/2304.13705",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2304.13705",
+      "_exa_published_date": "2023-04-23T00:00:00.000Z"
+    },
+    {
+      "title": "Learning Bimanual Manipulation via Action Chunking and Inter-Arm Coordination with Transformers",
+      "snippet": "coordinated biman\n[...]\n. To address the\n[...]\narms, particularly for synchronized actions. Therefore, we propose a novel imitation learning architecture that predicts cooperative actions. We differentiate the architecture for both arms and add an intermediate encoder layer, Inter-Arm Coordinated transformer Encoder (IACE),\n[...]\nInter-Arm Coordinated transformer Encoder (IACE), that can adjust the synchronization and timing of potential bimanual movements against the encoders corresponding to each arm. Our overall model\n[...]\na local Transformer encoder for each\n[...]\narm trajectory, the IACE to facilitate learning biman\n[...]\nactions, and a Transformer decoder to\n[...]\nthe action chunk. We compare two types of Transformer decoders: split decoders and single decoders.\n[...]\nWe build our proposed models on the ACT model to design different encoder and decoder structures. In particular, we propose a new design called the inter-arm coordinated transformer Encoder (IACE), which helps synchronize and time the movements of both arms.\n[...]\npropose basic architectures that consist of encoders\n[...]\narm, designed to leverage the biman\n[...]\nfeatures the IACE, allowing the individual robot arms to learn their trajectories while simultaneously considering the state\n[...]\nThe model should focus on the corresponding wrist camera and joint values to determine the appropriate trajectory for each robot arm. Each arm is supported by its local encoder. Global information is also integrated t",
+      "source_url": "https://arxiv.org/html/2503.13916",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2503.13916",
+      "_exa_published_date": null
+    },
+    {
+      "title": "ALPHA-𝛼 and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System",
+      "snippet": "Autonomous manipulation in everyday tasks requires flexible action generation to handle complex, diverse real-world environments, such as objects with varying hardness and softness. Imitation Learning (IL) enables robots to learn complex tasks from expert demonstrations. However, a lot of existing methods rely on position/unilateral control, leaving challenges in tasks that require force information/control, like carefully grasping fragile or varying-hardness objects. As the need for diverse controls increases, there are demand for low-cost bimanual robots that consider various motor inputs. To address these challenges, we introduce Bilateral Control-Based Imitation Learning via Action Chunking with Transformers(Bi-ACT) and”A” ”L”ow-cost ”P”hysical ”Ha”rdware Considering Diverse Motor Control Modes for Research in Everyday Bimanual Robotic Manipulation (ALPHA- $\\alpha$ ). Bi-ACT leverages bilateral control to utilize both position and force information, enhancing the robot’s adaptability to object characteristics such as hardness, shape, and weight. The concept of ALPHA- $\\alpha$ is affordability, ease of use, repairability, ease of assembly, and diverse control modes (position, velocity, torque), allowing researchers/developers to freely build control systems using ALPHA- $\\alpha$ . In our experiments, we conducted a detailed analysis of Bi-ACT in unimanual manipulation tasks, confirming its superior performance and adaptability compared to Bi-ACT without force control. Base",
+      "source_url": "https://arxiv.org/html/2411.09942",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2411.09942",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] Learning Fine-Grained Bimanual Manipulation with Low-Cost ...",
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+      "source_url": "https://openreview.net/pdf/4abc35d9793e56c5b73634eaf903e2495311fbcf.pdf",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/pdf/4abc35d9793e56c5b73634eaf903e2495311fbcf.pdf",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[1512.03385] Deep Residual Learning for Image Recognition - arXiv",
+      "snippet": "[1512.03385] Deep Residual Learning for Image Recognition\n[...]\n# Deep Residual Learning for Image Recognition\n[...]\nKaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com\n[...]\nDeeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8 $\\times$ deeper than VGG nets [41] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.\n[...]\nreferenced mapping. To\n[...]\nextreme, if\n[...]\nwould be easier\n[...]\nresidual to zero than\n[...]\nnonlinear layers.\n[...]\non ImageNet [36\n[...]\nOn the ImageNet classification dataset [36], we obtain excellent results by extremely deep residual nets. Our 152-layer residual net is the deepest network ever presented on ImageNet, while still having lower complexity than VGG nets [41]. Our ensem",
+      "source_url": "https://arxiv.org/abs/1512.03385",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://arxiv.org/abs/1512.03385",
+      "_exa_published_date": "2015-12-10T00:00:00.000Z"
+    },
+    {
+      "title": "[1512.03385] Deep Residual Learning for Image Recognition",
+      "snippet": "[1512.03385] Deep Residual Learning for Image Recognition\n[...]\n# Deep Residual Learning for Image Recognition\n[...]\nKaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com\n[...]\nDeeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8 $\\times$ deeper than VGG nets [41] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.\n[...]\nreferenced mapping. To\n[...]\nextreme, if\n[...]\nwould be easier\n[...]\nresidual to zero than\n[...]\nnonlinear layers.\n[...]\non ImageNet [36\n[...]\nOn the ImageNet classification dataset [36], we obtain excellent results by extremely deep residual nets. Our 152-layer residual net is the deepest network ever presented on ImageNet, while still having lower complexity than VGG nets [41]. Our ensem",
+      "source_url": "https://arxiv.org/abs/1512.03385v1",
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+      "_exa_id": "https://arxiv.org/abs/1512.03385v1",
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+    },
+    {
+      "title": "[PDF] Deep Residual Learning for Image Recognition - People | MIT CSAIL",
+      "snippet": "Deep Residual Learning\nfor Image Recognition\nKaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\nwork done at\nMicrosoft Research Asia\n[...]\nResNet @ ILSVRC & COCO 2015 Competitions\n[...]\n1st places in all five main tracks\n[...]\n• ImageNet Classification: “Ultra-deep” 152-layer nets \n• ImageNet Detection: 16% better than 2nd\n[...]\n• ImageNet Localization: 27% better than 2nd\n[...]\n• COCO Detection: 11% better than 2nd\n[...]\n• COCO Segmentation: 12% better than 2nd\n[...]\n*improvements are relative numbers\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nRevolution of Depth\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiang",
+      "source_url": "https://pdfs.semanticscholar.org/1cea/9b1931b9e87641708fec43d03f2a58f4d2b0.pdf",
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+      "_exa_id": "https://pdfs.semanticscholar.org/1cea/9b1931b9e87641708fec43d03f2a58f4d2b0.pdf",
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+    },
+    {
+      "title": "Deep Residual Learning for Image Recognition: A Survey - MDPI",
+      "snippet": "Deep Residual Learning for Image Recognition: A Survey\n[...]\n# Deep Residual Learning for Image Recognition: A Survey\n[...]\nMuhammad Shafiq\n[...]\n1,* and\n\nZhaoquan Gu\n[...]\n2,3,*\n[...]\nCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China\n[...]\nDepartment of New Networks, Peng Cheng Laboratory, Shenzhen 518055, China\n[...]\nDepartment of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China\n[...]\nAppl. Sci. 2022, 12(18), 8972; https://doi.org/10.3390/app12188972\n[...]\nDeep Residual Networks have recently been shown to significantly improve the performance of neural networks trained on ImageNet, with results beating all previous methods on this dataset by large margins in the image classification task. However, the meaning of these impressive numbers and their implications for future research are not fully understood yet. In this survey, we will try to explain what Deep Residual Networks are, how they achieve their excellent results, and why their successful implementation in practice represents a significant advance over existing techniques. We also discuss some open questions related to residual learning as well as possible applications of Deep Residual Networks beyond ImageNet. Finally, we discuss some issues that still need to be resolved before deep residual learning can be applied on more complex problems.\n[...]\ndeep residual learning for image recognition\n[...]\ndeep residual learning;\n[...]\nDeep resid",
+      "source_url": "https://www.mdpi.com/2076-3417/12/18/8972",
+      "discovered_for": [
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+      "_exa_id": "https://www.mdpi.com/2076-3417/12/18/8972",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[1603.05027] Identity Mappings in Deep Residual Networks - arXiv",
+      "snippet": "Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun\n[...]\nDeep residual networks [1] have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62% error) and CIFAR-100, and a 200-layer ResNet on Image\n[...]\n. Code is available at: https://github.com/KaimingHe/resnet-1k-layers.\n[...]\n- [1] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. (2016)",
+      "source_url": "https://arxiv.org/abs/1603.05027",
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+      ],
+      "_exa_id": "https://arxiv.org/abs/1603.05027",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[2603.15031] Attention Residuals - arXiv",
+      "snippet": "Untitled Document\n\n$0$ $5$ $10$ $15$ $20$\n\nUntitled Document\n$0$ $5$ $10$ $15$ $20$\nBETA",
+      "source_url": "https://arxiv.org/abs/2603.15031",
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+      "_exa_id": "https://arxiv.org/abs/2603.15031",
+      "_exa_published_date": "2026-03-16T00:00:00.000Z"
+    },
+    {
+      "title": "SiameseNorm: Breaking the Barrier to Reconciling Pre/Post-Norm",
+      "snippet": "In this paper, we propose SiameseNorm, an elegant two\n[...]\nstream residual architecture that unifies\n[...]\nmaintain two residual streams\n[...]\nshared parameters:\n[...]\nthe advantages of\n[...]\nnegligible computational overhead\n[...]\nboosts accuracy from\n[...]\n128\n[...]\n639.6\n[...]\nwhere the product denotes an ordered composition of Jacobians from layer N−1N-1 down to i+1i+1. Notably, the term 𝐈\\mathbf{I} corresponds to the skip connection, which preserves an explicit identity gradient path. This allows gradients to flow through the network without explicit attenuation, facilitating the training of large scale models. However, it implicitly allows the representation magnitudes to grow unbounded. As noted previously, Pre-Norm exhibits insufficient effective depth, an issue that likely stems from a structural mismatch: As shown in Figure˜2(a), the main path accumulates residual updates without re-normalization, causing hidden state magnitudes to grow with depth (peri-ln). Consequently, deeper blocks encounter a scaling imbalance: they must influence an increasingly high-magnitude main path while being restricted to normalized, fixed-scale inputs. This growing disparity effectively dilutes the relative contribution of deeper layers, thereby limiting the effective depth of the model.\n[...]\nBy maintaining a clean identity path, Pre-Norm ensures stable gradient propagation. However, this comes at the cost of unbounded magnitude growth. As illustrated in Figure 2(a), while the input ",
+      "source_url": "https://arxiv.org/html/2602.08064v1",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://arxiv.org/html/2602.08064v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] Attention Residuals - arXiv",
+      "snippet": "Untitled Document\n\n$0$ $5$ $10$ $15$ $20$\n\nUntitled Document\n$0$ $5$ $10$ $15$ $20$\nBETA",
+      "source_url": "https://arxiv.org/pdf/2603.15031",
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+      "_exa_id": "https://arxiv.org/pdf/2603.15031",
+      "_exa_published_date": "2026-03-16T00:00:00.000Z"
+    },
+    {
+      "title": "[2409.19606] Hyper-Connections - arXiv",
+      "snippet": "We present hyper-connections, a simple yet effective method that can serve as an alternative to residual connections. This approach specifically addresses common drawbacks observed in residual connection variants, such as the seesaw effect between gradient vanishing and representation collapse. Theoretically, hyper-connections allow the network to adjust the strength of connections between features at different depths and dynamically rearrange layers. We conduct experiments focusing on the pre-training of large language models, including dense and sparse models, where hyper-connections show significant performance improvements over residual connections. Additional experiments conducted on vision tasks also demonstrate similar improvements. We anticipate that this method will be broadly applicable and beneficial across a wide range of AI problems.\n[...]\nDeep learning has achieved tremendous success across various domains, where residual connections (He et al., 2016) have been instrumental in contemporary neural network architectures, including transformers and CNNs. Residual connections help mitigate the problem of gradient vanishing, enabling the effective training of very deep networks. However, it is important to acknowledge that residual connections are not infallible solutions and still present limitations that remain unresolved.\n[...]\nDriven by the limitations of residual connections, an important question arises: Can neural networks autonomously learn the optimal streng",
+      "source_url": "https://arxiv.org/abs/2409.19606",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2409.19606",
+      "_exa_published_date": "2024-09-29T00:00:00.000Z"
+    },
+    {
+      "title": "Hyper-Connections - OpenReview",
+      "snippet": "Hyper-Connections | OpenReview\n\n## Hyper-Connections\n\n### Defa Zhu, Hongzhi Huang, Zihao Huang, Yutao Zeng, Yunyao Mao, Banggu Wu, Qiyang Min, Xun Zhou\n\nICLR 2025 Postereveryonesince 04 Oct 2024\">Everyone Revisions BibTeX CC BY 4.0\n\nKeywords: Network Architecture, Residual Connections, LLMs, Pre-training\n\nAbstract: We present hyper-connections, a simple yet effective method that can serve as an alternative to residual connections. This approach specifically addresses common drawbacks observed in residual connection variants, such as the seesaw effect between gradient vanishing and representation collapse. Theoretically, hyper-connections allow the network to adjust the strength of connections between features at different depths and dynamically rearrange layers. We conduct experiments focusing on the pre-training of large language models, including dense and sparse models, where hyper-connections show significant performance improvements over residual connections. Additional experiments conducted on vision tasks also demonstrate similar improvements. We anticipate that this method will be broadly applicable and beneficial across a wide range of AI problems.\n\nPrimary Area: foundation or frontier models, including LLMs\n\nCode Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.\n\nSubmission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Con",
+      "source_url": "https://openreview.net/forum?id=9FqARW7dwB",
+      "discovered_for": [
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+      ],
+      "_exa_id": "https://openreview.net/forum?id=9FqARW7dwB",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Birkhoff-Exact Hyper-Connections: Exact Spectral Stability for Deep Residual Networks | OpenReview",
+      "snippet": "## Birkhoff-Exact Hyper-Connections: Exact Spectral Stability for Deep Residual Networks\n[...]\nKeywords: doubly stochastic matrices, spectral stability, deep residual networks, Birkhoff-von Neumann theorem, hyper-connections, token mixing, extreme depth training, quantization robustness\n[...]\nTL;DR: We propose BE-HC, which uses the Birkhoff-von Neumann theorem to construct exactly doubly stochastic mixing matrices as convex combinations of permutation matrices, enabling stable training at 1000+ layers where prior methods fail.\n[...]\nAbstract: Learnable information routing in deep networks faces the *depth-stability-efficiency trilemma*: architectures that scale to extreme depths often sacrifice efficiency; efficient approaches lack stability guarantees. Prior work uses iterative Sinkhorn-Knopp normalization to approximate doubly stochastic mixing matrices, but residual errors destabilize training beyond several hundred layers. We propose **Birkhoff-Exact Hyper-Connections (BE-HC)**, which leverages the Birkhoff-von Neumann theorem to construct *exactly* doubly stochastic matrices as convex combinations of permutation matrices. This guarantees spectral radius $\\rho = 1$ exactly—not approximately—enabling stable training at unprecedented depths. **Key results:** (1) *Extreme depth:* BE-HC trains stably at **1000 layers**, achieving 35.71% accuracy where ReZero and other baselines fail to converge. (2) *Long context:* BE-HC handles **8K tokens** on a single V100 GPU (22.56% vali",
+      "source_url": "https://openreview.net/forum?id=jpIjkN1B1Q",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=jpIjkN1B1Q",
+      "_exa_published_date": "2026-03-02T22:01:32.000Z"
+    },
+    {
+      "title": "Ablate and Rescue: A Causal Analysis of Residual Stream Hyper-Connections",
+      "snippet": "Multi-stream transformer architectures have recently been proposed as a promising direction for managing representation collapse and the vanishing gradient problem for residual connections, yet their internal mechanisms remain unexplored. In particular, the recently introduced Manifold-Constrained Hyper-Connections (mHC) architecture posits multiple residual streams with constrained interaction, but lacks in-depth mechanistic analysis. We present the first open-source mHC language model (https://huggingface.co/wgpeng/mhc-780m) and analyze the multiple-stream architecture with a suite of representation-level metrics and causal interventions to probe how parallel streams encode and utilize information. Specifically, we introduce a systematic stream ablation-and-rescue framework that enables direct causal comparison of residual streams during inference. Through targeted pairwise interventions and controlled recovery experiments, we distinguish functional redundancy from asymmetric utilization and reveal how information is distributed across streams beyond what is observable from representational similarity alone.\n[...]\nHyper-Connections extend the standard transformer residual architecture by allowing multiple residual streams per layer, dynamically mixed through learned routing matrices (He et al., 2015; Zhu et al., 2025). Manifold-Constrained Hyper-Connections (mHC) further refines this framework by imposing geometric constraints on inter-stream mixing (Xie et al., 2026).\n[...",
+      "source_url": "https://www.arxiv.org/pdf/2603.14833",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://www.arxiv.org/pdf/2603.14833",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[1711.05101] Decoupled Weight Decay Regularization - arXiv",
+      "snippet": "[1711.05\n[...]\npled Weight Decay Regularization\n[...]\nIlya Loshchilov & Frank Hutter University of Freiburg Freiburg, Germany, {ilya,fh}@cs.uni-freiburg.de\n[...]\nL2 regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is not the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L2 regularization (often calling it “weight decay” in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by decoupling the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam’s generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at https://github.com/loshchil/AdamW-and-SGDW\n[...]\nThe main contribution of this paper is to improve regularization in Adam by decoupling ",
+      "source_url": "https://arxiv.org/abs/1711.05101",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://arxiv.org/abs/1711.05101",
+      "_exa_published_date": "2017-11-14T00:00:00.000Z"
+    },
+    {
+      "title": "[PDF] Decoupled Weight Decay Regularization - arXiv",
+      "snippet": "DECOUPLED WEIGHT DECAY REGULARIZATION\n[...]\nIlya Loshchilov & Frank Hutter\n[...]\nL2 regularization and weight decay regularization are equivalent for standard\n[...]\nstochastic gradient descent (when rescaled by the learning rate), but as we demon\u0002strate this is not the case for adaptive gradient algorithms, such as Adam. While\n[...]\nexpose), we propose a simple modification to recover the original formulation of\n[...]\nweight decay regularization by decoupling the weight decay from the optimization\n[...]\nsteps taken w.r.t. the loss function. We provide empirical evidence that our pro\u0002posed modification (i) decouples the optimal choice of weight decay factor from\n[...]\nthe setting of the learning rate for both standard SGD and Adam and (ii) substan\u0002tially improves Adam’s generalization performance, allowing it to compete with\n[...]\nSGD with momentum on image classification datasets (on which it was previously\n[...]\ntypically outperformed by the latter). Our proposed decoupled weight decay has\n[...]\ncommunity has implemented\n[...]\nit in TensorFlow and PyTorch; the complete source code for our experiments is\n[...]\navailable at https://github.com/loshchil/AdamW-and-SGDW\n[...]\nThe main contribution of this paper is to improve regularization in Adam by decoupling the weight\n[...]\ndecay from the gradient-based update. In a comprehensive analysis, we show that Adam generalizes\n[...]\nsubstantially better with decoupled weight decay than with L2 regularization, achieving 15% relative\n[.",
+      "source_url": "https://arxiv.org/pdf/1711.05101",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/1711.05101",
+      "_exa_published_date": "2019-01-04T00:00:00.000Z"
+    },
+    {
+      "title": "Decoupled Weight Decay Regularization - OpenReview",
+      "snippet": "Decoupled Weight Decay Regularization | OpenReview\n\n## Decoupled Weight Decay Regularization\n\nICLR 2019 Conference Blind SubmissionReaders: Everyone\n\nAbstract: L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it ``weight decay'' in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at \\url{https://github.com/loshchil/AdamW-and-SGDW}\n\nKeywords: optimization, regularization, weight decay, Adam\n\nCode: ",
+      "source_url": "https://openreview.net/forum?id=Bkg6RiCqY7",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=Bkg6RiCqY7",
+      "_exa_published_date": "2018-09-27T08:45:35.000Z"
+    },
+    {
+      "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
+      "snippet": "PyTorch: An Imperative Style, High-Performance Deep Learning Library\n[...]\nDeep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks.",
+      "source_url": "https://papers.nips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://papers.nips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] An Imperative Style, High-Performance Deep Learning Library - NIPS",
+      "snippet": "PyTorch: An Imperative Style, High-Performance Deep Learning Library\n\n| | Adam | Paszke | | Sam | Gross | | | Francisco | Massa | | |\n[...]\nAbstract Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.\n[...]\nWith the increased interest in deep learning in recent years, there has been an explosion of machine learning tools. Many popular frameworks such as Caffe [1], CNTK [2], TensorFlow [3], and Theano [4], construct a static dataflow graph that represents the computation and which can then be applied repeatedly to batches of data. This approach provides visibility into the whole comput",
+      "source_url": "https://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[1912.01703v1] PyTorch: An Imperative Style, High-Performance Deep Learning Library",
+      "snippet": "[1912.01703v1] PyTorch: An Imperative Style, High-Performance Deep Learning Library\n[...]\n# Title:PyTorch: An Imperative Style, High-Performance Deep Learning Library\n[...]\n> Abstract:Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.",
+      "source_url": "https://arxiv.org/abs/1912.01703v1",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://arxiv.org/abs/1912.01703v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "PyTorch: An Imperative Style, High-Performance Deep Learning ...",
+      "snippet": "[1912.01703] PyTorch: An Imperative Style, High-Performance Deep Learning Library\n[...]\n# PyTorch: An Imperative Style, High-Performance Deep Learning Library\n[...]\nAdam Paszke University of Warsaw adam.paszke@gmail.com Sam Gross Facebook AI Research sgross@fb.com Francisco Massa Facebook AI Research fmassa@fb.com Adam Lerer Facebook AI Research alerer@fb.com James Bradbury Google jekbradbury@gmail.com Gregory Chanan Facebook AI Research gchanan@fb.com Trevor Killeen Self Employed killeent@cs.washington.edu Zeming Lin Facebook AI Research zlin@fb.com Natalia Gimelshein NVIDIA ngimelshein@nvidia.com Luca Antiga Orobix luca.antiga@orobix.com Alban Desmaison Oxford University alban@robots.ox.ac.uk Andreas Köpf Xamla andreas.koepf@xamla.com Edward Yang Facebook AI Research ezyang@fb.com Zach DeVito Facebook AI Research zdevito@cs.stanford.edu Martin Raison Nabla martinraison@gmail.com Alykhan Tejani Twitter atejani@twitter.com Sasank Chilamkurthy Qure.ai sasankchilamkurthy@gmail.com Benoit Steiner Facebook AI Research benoitsteiner@fb.com Lu Fang Facebook lufang@fb.com Junjie Bai Facebook jbai@fb.com Soumith Chintala Facebook AI Research soumith@gmail.com\n[...]\nDeep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popu",
+      "source_url": "https://arxiv.org/abs/1912.01703",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://arxiv.org/abs/1912.01703",
+      "_exa_published_date": "2019-12-03T00:00:00.000Z"
+    }
+  ],
+  "n_before_dedup": 90,
+  "n_after_dedup": 71,
+  "n_removed": 19
+}
\ No newline at end of file
diff --git a/workspace/drafts/corl_2026.sty b/workspace/drafts/corl_2026.sty
new file mode 100644
index 0000000..1180459
--- /dev/null
+++ b/workspace/drafts/corl_2026.sty
@@ -0,0 +1,472 @@
+% File: corl_2026.sty 
+%
+% Latex templates for the Conference on Robot Learning (CoRL)
+%
+% This template is heavily inspired by the NeurIPS, ICML, ICLR and IEEE Transactions latex templates.
+% Hence we would like to thank: Roman Garnett and the previous mantainers of the NIPS style, Percy Liang and the previous mantainers of the ICML style, Hugo Larochelle for the ICLR style, and Michael Shell for the IEEE Transactions style.
+%
+% History:
+% 2017/04/16 - First revision by Roberto Calandra (roberto.calandra@berkeley.edu).
+%	       Main changes:
+%	       - The abstract is more compact compared to NeurIPS/ICML
+%	       - References are by default using natbib with squared numbers (e.g., [1])
+%	       - DOI fields from the bibtex are automatically converted to hyperlinks 
+%	       to the corresponding page
+%	       - acknowledgments are now a command, and the corresponding subsubsection is
+%	       automatically included only in the final version
+% 2017/06/12 - Modified to use corlabbrvnat.bst, which order the reference by order of appearance in the paper
+% 2017/06/13 - fixed typo
+% 2018/05/09 - Slightly modified for CoRL 2018 by Jun Morimoto (xmorimo@atr.jp)
+% 2019/01/28 - Slightly modified for CoRL 2019 by Jun Nakanishi (jnakanis@meijo-u.ac.jp)
+% 2020/02/02 - Slightly modified for CoRL 2020 by Cynthia Matuszek (cmat@umbc.edu)
+% 2020/08/19 - Added preprint option by Roberto Calandra (rcalandra@fb.com)
+% 2021/05/06 - Slightly modified for CoRL 2021 by Gerhard Neumann (gerhard.neumann@kit.edu)
+% 2022/03/09 - Slightly modified for CoRL 2022 by Minas Liarokapis (minas.liarokapis@auckland.ac.nz)
+% 2022/03/06 - Slightly modified for CoRL 2023 by Marc Toussaint (toussaint@tu-berlin.de)
+% 2024/03/26 - Slightly modified for CoRL 2024 by David Held (dheld@andrew.cmu.edu)
+% 2026/01/10 - Slightly modified for CoRL 2026 by Yoonchang Sung (yoonchang.sung@ntu.edu.sg)
+%
+% TODO: nohyperref is not working at the moment
+% 
+
+\NeedsTeXFormat{LaTeX2e}
+
+% Content to be changed from year to year
+\ProvidesPackage{corl_2026}[2026/08/15 CORL2026 submission/preprint/camera-ready style file]
+\newcommand{\@conferenceordinal}{10th}
+\newcommand{\@conferenceyear}{2026}
+\newcommand{\@conferencelocation}{Austin TX, USA}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Accepted options: [final,preprint,nonatbib,nohyperref]
+
+% Declare the final option, which creates camera-ready copy
+\newif\if@conferencefinal\@conferencefinalfalse
+\DeclareOption{final}{
+  \@conferencefinaltrue
+}
+
+% Declare the preprint option, which creates a camera-ready copy without the corl footnote
+\newif\if@preprinttype\@preprinttypefalse
+\DeclareOption{preprint}{
+  \@preprinttypetrue
+}
+
+% The natbib package is loaded by default. Declaring the nonatbib option, does not load natbib in case of package clash (users can pass options to natbib via \PassOptionsToPackage)
+\newif\if@natbib\@natbibtrue
+\DeclareOption{nonatbib}{
+  \@natbibfalse
+}
+
+% The hyperref package is loaded by default. Declaring the  nohyperref option, does not load the hyperref.
+\DeclareOption{nohyperref}{%
+  \gdef\nohyperref{1}
+}
+
+% Activate the options
+\ProcessOptions\relax
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Required packages:
+
+\RequirePackage{lineno}
+\RequirePackage{color}
+
+% Load natbib unless told otherwise
+\if@natbib
+  \RequirePackage[square,numbers]{natbib}
+  \bibliographystyle{corlabbrvnat}
+\fi
+
+% set page geometry
+\RequirePackage{hyperref}       % hyperlinks
+\RequirePackage[verbose=true,letterpaper]{geometry}
+\AtBeginDocument{
+  \newgeometry{
+    textheight=9in,
+    textwidth=5.5in,
+    top=1in,
+    headheight=12pt,
+    headsep=25pt,
+    footskip=30pt
+  }
+  \@ifpackageloaded{fullpage}
+    {\PackageWarning{corl_2026}{fullpage package not allowed! Overwriting formatting.}}
+    {}
+}
+
+
+\ifdefined\nohyperref\else\ifdefined\hypersetup
+  \definecolor{mydarkblue}{rgb}{0,0.08,0.45}
+  \hypersetup{ %
+    pdftitle={},
+    pdfauthor={},
+    pdfsubject={Proceedings of the \@conferenceordinal\/ Conference on Robot Learning (CoRL \@conferenceyear)},
+    pdfkeywords={},
+    pdfborder=0 0 0,
+    pdfpagemode=UseNone,
+    colorlinks=true,
+    linkcolor=mydarkblue,
+    citecolor=mydarkblue,
+    filecolor=mydarkblue,
+    urlcolor=mydarkblue,
+    pdfview=FitH}
+
+  \ifdefined\isaccepted \else
+    \hypersetup{pdfauthor={Anonymous Submission}}
+  \fi
+\fi\fi
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% 
+
+% fonts
+\renewcommand{\rmdefault}{ptm}
+\renewcommand{\sfdefault}{phv}
+
+% Create acknowledgments -- only if the option 'final' is activated
+\providecommand{\acknowledgments}{}
+\renewcommand{\acknowledgments}[1]{%
+\if@conferencefinal% 
+\subsubsection*{Acknowledgments} #1
+\fi
+\if@preprinttype% 
+\subsubsection*{Acknowledgments} #1
+\fi
+}
+
+
+% handle tweaks for camera-ready copy vs. submission copy
+\if@conferencefinal
+  \newcommand{\@noticestring}{%
+    \@conferenceordinal\/ Conference on Robot Learning
+    (CoRL \@conferenceyear), \@conferencelocation.%
+  }
+\else
+  \if@preprinttype
+    \newcommand{\@noticestring}{%
+      % Nothing here.
+    }
+  \else
+    \newcommand{\@noticestring}{%
+      Submitted to the \@conferenceordinal\/ Conference on Robot Learning (CoRL \@conferenceyear). Do not distribute.%
+    }
+
+    % line numbers for submission
+    \linenumbers
+
+    % fix incompatibilities between lineno and amsmath, if required, by
+    % transparently wrapping linenomath environments around amsmath
+    % environments
+    \AtBeginDocument{%
+     \@ifpackageloaded{amsmath}{%
+        \newcommand*\patchAmsMathEnvironmentForLineno[1]{%
+          \expandafter\let\csname old#1\expandafter\endcsname\csname #1\endcsname
+          \expandafter\let\csname oldend#1\expandafter\endcsname\csname end#1\endcsname
+          \renewenvironment{#1}%
+            {\linenomath\csname old#1\endcsname}%
+            {\csname oldend#1\endcsname\endlinenomath}%
+        }%
+        \newcommand*\patchBothAmsMathEnvironmentsForLineno[1]{%
+          \patchAmsMathEnvironmentForLineno{#1}%
+          \patchAmsMathEnvironmentForLineno{#1*}%
+        }%
+        \patchBothAmsMathEnvironmentsForLineno{equation}%
+        \patchBothAmsMathEnvironmentsForLineno{align}%
+        \patchBothAmsMathEnvironmentsForLineno{flalign}%
+        \patchBothAmsMathEnvironmentsForLineno{alignat}%
+        \patchBothAmsMathEnvironmentsForLineno{gather}%
+        \patchBothAmsMathEnvironmentsForLineno{multline}%
+      }{}
+    }
+  \fi
+\fi
+
+
+
+% The DOI will now automatically generate a URL link. Pretty cool!
+% + Fix for hyperref and DOI (https://www.tug.org/pipermail/tex-live/2012-August/032161.html)
+%-----------------------------
+\makeatletter
+\providecommand{\doi}[1]{%
+  \begingroup
+    \let\bibinfo\@secondoftwo
+    \urlstyle{rm}%
+    \href{http://dx.doi.org/#1}{%
+      doi:\discretionary{}{}{}%
+      \nolinkurl{#1}%
+    }%
+  \endgroup
+}
+% \makeatother
+% %-----------------------------
+
+\widowpenalty=10000
+\clubpenalty=10000
+\flushbottom
+\sloppy
+
+% font sizes with reduced leading
+\renewcommand{\normalsize}{%
+  \@setfontsize\normalsize\@xpt\@xipt
+  \abovedisplayskip      7\p@ \@plus 2\p@ \@minus 5\p@
+  \abovedisplayshortskip \z@ \@plus 3\p@
+  \belowdisplayskip      \abovedisplayskip
+  \belowdisplayshortskip 4\p@ \@plus 3\p@ \@minus 3\p@
+}
+\normalsize
+\renewcommand{\small}{%
+  \@setfontsize\small\@ixpt\@xpt
+  \abovedisplayskip      6\p@ \@plus 1.5\p@ \@minus 4\p@
+  \abovedisplayshortskip \z@  \@plus 2\p@
+  \belowdisplayskip      \abovedisplayskip
+  \belowdisplayshortskip 3\p@ \@plus 2\p@   \@minus 2\p@
+}
+\renewcommand{\footnotesize}{\@setfontsize\footnotesize\@ixpt\@xpt}
+\renewcommand{\scriptsize}{\@setfontsize\scriptsize\@viipt\@viiipt}
+\renewcommand{\tiny}{\@setfontsize\tiny\@vipt\@viipt}
+\renewcommand{\large}{\@setfontsize\large\@xiipt{14}}
+\renewcommand{\Large}{\@setfontsize\Large\@xivpt{16}}
+\renewcommand{\LARGE}{\@setfontsize\LARGE\@xviipt{20}}
+\renewcommand{\huge}{\@setfontsize\huge\@xxpt{23}}
+\renewcommand{\Huge}{\@setfontsize\Huge\@xxvpt{28}}
+
+% sections with less space
+\providecommand{\section}{}
+\renewcommand{\section}{%
+  \@startsection{section}{1}{\z@}%
+                {-2.0ex \@plus -0.5ex \@minus -0.2ex}%
+                { 1.5ex \@plus  0.3ex \@minus  0.2ex}%
+                {\large\bf\raggedright}%
+}
+\providecommand{\subsection}{}
+\renewcommand{\subsection}{%
+  \@startsection{subsection}{2}{\z@}%
+                {-1.8ex \@plus -0.5ex \@minus -0.2ex}%
+                { 0.8ex \@plus  0.2ex}%
+                {\normalsize\bf\raggedright}%
+}
+\providecommand{\subsubsection}{}
+\renewcommand{\subsubsection}{%
+  \@startsection{subsubsection}{3}{\z@}%
+                {-1.5ex \@plus -0.5ex \@minus -0.2ex}%
+                { 0.5ex \@plus  0.2ex}%
+                {\normalsize\bf\raggedright}%
+}
+\providecommand{\paragraph}{}
+\renewcommand{\paragraph}{%
+  \@startsection{paragraph}{4}{\z@}%
+                {1.5ex \@plus 0.5ex \@minus 0.2ex}%
+                {-1em}%
+                {\normalsize\bf}%
+}
+\providecommand{\subparagraph}{}
+\renewcommand{\subparagraph}{%
+  \@startsection{subparagraph}{5}{\z@}%
+                {1.5ex \@plus 0.5ex \@minus 0.2ex}%
+                {-1em}%
+                {\normalsize\bf}%
+}
+\providecommand{\subsubsubsection}{}
+\renewcommand{\subsubsubsection}{%
+  \vskip5pt{\noindent\normalsize\rm\raggedright}%
+}
+
+% float placement
+\renewcommand{\topfraction      }{0.85}
+\renewcommand{\bottomfraction   }{0.4}
+\renewcommand{\textfraction     }{0.1}
+\renewcommand{\floatpagefraction}{0.7}
+
+\newlength{\@nipsabovecaptionskip}\setlength{\@nipsabovecaptionskip}{7\p@}
+\newlength{\@nipsbelowcaptionskip}\setlength{\@nipsbelowcaptionskip}{\z@}
+
+\setlength{\abovecaptionskip}{\@nipsabovecaptionskip}
+\setlength{\belowcaptionskip}{\@nipsbelowcaptionskip}
+
+% swap above/belowcaptionskip lengths for tables
+\renewenvironment{table}
+  {\setlength{\abovecaptionskip}{\@nipsbelowcaptionskip}%
+   \setlength{\belowcaptionskip}{\@nipsabovecaptionskip}%
+   \@float{table}}
+  {\end@float}
+
+% footnote formatting
+\setlength{\footnotesep }{6.65\p@}
+\setlength{\skip\footins}{9\p@ \@plus 4\p@ \@minus 2\p@}
+\renewcommand{\footnoterule}{\kern-3\p@ \hrule width 12pc \kern 2.6\p@}
+\setcounter{footnote}{0}
+
+% paragraph formatting
+\setlength{\parindent}{\z@}
+\setlength{\parskip  }{5.5\p@}
+
+% list formatting
+\setlength{\topsep       }{4\p@ \@plus 1\p@   \@minus 2\p@}
+\setlength{\partopsep    }{1\p@ \@plus 0.5\p@ \@minus 0.5\p@}
+\setlength{\itemsep      }{2\p@ \@plus 1\p@   \@minus 0.5\p@}
+\setlength{\parsep       }{2\p@ \@plus 1\p@   \@minus 0.5\p@}
+\setlength{\leftmargin   }{3pc}
+\setlength{\leftmargini  }{\leftmargin}
+\setlength{\leftmarginii }{2em}
+\setlength{\leftmarginiii}{1.5em}
+\setlength{\leftmarginiv }{1.0em}
+\setlength{\leftmarginv  }{0.5em}
+\def\@listi  {\leftmargin\leftmargini}
+\def\@listii {\leftmargin\leftmarginii
+              \labelwidth\leftmarginii
+              \advance\labelwidth-\labelsep
+              \topsep  2\p@ \@plus 1\p@    \@minus 0.5\p@
+              \parsep  1\p@ \@plus 0.5\p@ \@minus 0.5\p@
+              \itemsep \parsep}
+\def\@listiii{\leftmargin\leftmarginiii
+              \labelwidth\leftmarginiii
+              \advance\labelwidth-\labelsep
+              \topsep    1\p@ \@plus 0.5\p@ \@minus 0.5\p@
+              \parsep    \z@
+              \partopsep 0.5\p@ \@plus 0\p@ \@minus 0.5\p@
+              \itemsep \topsep}
+\def\@listiv {\leftmargin\leftmarginiv
+              \labelwidth\leftmarginiv
+              \advance\labelwidth-\labelsep}
+\def\@listv  {\leftmargin\leftmarginv
+              \labelwidth\leftmarginv
+              \advance\labelwidth-\labelsep}
+\def\@listvi {\leftmargin\leftmarginvi
+              \labelwidth\leftmarginvi
+              \advance\labelwidth-\labelsep}
+
+% create title
+\providecommand{\maketitle}{}
+\renewcommand{\maketitle}{%
+  \par
+  \begingroup
+    \renewcommand{\thefootnote}{\fnsymbol{footnote}}
+    % for perfect author name centering
+    \renewcommand{\@makefnmark}{\hbox to \z@{$^{\@thefnmark}$\hss}}
+    % The footnote-mark was overlapping the footnote-text,
+    % added the following to fix this problem               (MK)
+    \long\def\@makefntext##1{%
+      \parindent 1em\noindent
+      \hbox to 1.8em{\hss $\m@th ^{\@thefnmark}$}##1
+    }
+    \thispagestyle{empty}
+    \@maketitle
+    \@thanks
+    \@notice
+  \endgroup
+  \let\maketitle\relax
+  \let\thanks\relax
+}
+
+% rules for title box at top of first page
+\newcommand{\@toptitlebar}{
+  \hrule height 4\p@
+  \vskip 0.25in
+  \vskip -\parskip%
+}
+\newcommand{\@bottomtitlebar}{
+  \vskip 0.29in
+  \vskip -\parskip
+  \hrule height 1\p@
+  \vskip 0.09in%
+}
+
+%% keywords as first class citizens
+\def\keywords#1{%
+%  \ifdefined\isaccepted \else
+%    \par {\bf Keywords:} #1%
+%  \fi
+%  \ifdefined\nohyperref\else\ifdefined\hypersetup
+%    \hypersetup{pdfkeywords={#1}}
+%  \fi\fi
+ \ifdefined\isaccepted \else
+    \begin{quote}
+    \textbf{Keywords:} #1%
+    \end{quote}
+ \fi
+  \ifdefined\nohyperref\else\ifdefined\hypersetup
+    \hypersetup{pdfkeywords={#1}}
+  \fi\fi
+}
+
+
+% create title (includes both anonymized and non-anonymized versions)
+\providecommand{\@maketitle}{}
+\renewcommand{\@maketitle}{%
+  \vbox{%
+    \hsize\textwidth
+    \linewidth\hsize
+    \vskip 0.1in
+%     \@toptitlebar
+    \centering
+    {\LARGE\bf \@title\par}
+%     \@bottomtitlebar
+    \if@conferencefinal
+      \def\And{%
+        \end{tabular}\hfil\linebreak[0]\hfil%
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+      }
+      \def\AND{%
+        \end{tabular}\hfil\linebreak[4]\hfil%
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+      }
+      \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\@author\end{tabular}%
+    \else
+      \if@preprinttype
+        \def\And{%
+          \end{tabular}\hfil\linebreak[0]\hfil%
+          \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+        }
+        \def\AND{%
+          \end{tabular}\hfil\linebreak[4]\hfil%
+          \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+        }
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\@author\end{tabular}%
+      \else
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}
+          Anonymous Author(s) \\
+          Affiliation \\
+          Address \\
+          \texttt{email} \\
+        \end{tabular}%
+      \fi
+    \fi
+    \vskip 0.3in \@minus 0.1in
+  }
+}
+
+% add conference notice to bottom of first page
+\newcommand{\ftype@noticebox}{8}
+\newcommand{\@notice}{%
+  % give a bit of extra room back to authors on first page
+  \enlargethispage{2\baselineskip}%
+  \@float{noticebox}[b]%
+    \footnotesize\@noticestring%
+  \end@float%
+}
+
+% abstract styling
+\renewenvironment{abstract}%
+{%
+%   \vskip 0.075in%
+%   \centerline%
+%   {\large\bf Abstract}%
+%   \vspace{0.5ex}%
+  \begin{quote}%
+  \textbf{Abstract:}%
+}
+{
+  \par%
+  \vskip 1ex%
+%   \ifdefined\keywords
+%   \textbf{Keywords:}%
+%   \@keywords%
+%   \else
+%   \fi
+  \end{quote}%
+}
+
+\endinput
diff --git a/workspace/drafts/corlabbrvnat.bst b/workspace/drafts/corlabbrvnat.bst
new file mode 100644
index 0000000..eab7c3e
--- /dev/null
+++ b/workspace/drafts/corlabbrvnat.bst
@@ -0,0 +1,1442 @@
+%% File: `corlabbrvnat.bst'
+%% A modification of `abbrvnat.bst' to order the references by the order
+%% of appearance in the paper.
+%%
+%% Modified by Roberto Calandra 12/06/2017
+%% email: roberto.calandra@berkeley.edu
+%%
+%% -------------------
+%%
+%% Copyright 1993-2007 Patrick W Daly
+%% Max-Planck-Institut f\"ur Sonnensystemforschung
+%% Max-Planck-Str. 2
+%% D-37191 Katlenburg-Lindau
+%% Germany
+%% E-mail: daly@mps.mpg.de
+%%
+%% This program can be redistributed and/or modified under the terms
+%% of the LaTeX Project Public License Distributed from CTAN
+%% archives in directory macros/latex/base/lppl.txt; either
+%% version 1 of the License, or any later version.
+%%
+ % Version and source file information:
+ % \ProvidesFile{natbst.mbs}[2007/11/26 1.93 (PWD)]
+ %
+ % BibTeX `plainnat' family
+ %   version 0.99b for BibTeX versions 0.99a or later,
+ %   for LaTeX versions 2.09 and 2e.
+ %
+ % For use with the `natbib.sty' package; emulates the corresponding
+ %   member of the `plain' family, but with author-year citations.
+ %
+ % With version 6.0 of `natbib.sty', it may also be used for numerical
+ %   citations, while retaining the commands \citeauthor, \citefullauthor,
+ %   and \citeyear to print the corresponding information.
+ %
+ % For version 7.0 of `natbib.sty', the KEY field replaces missing
+ %   authors/editors, and the date is left blank in \bibitem.
+ %
+ % Includes field EID for the sequence/citation number of electronic journals
+ %  which is used instead of page numbers.
+ %
+ % Includes fields ISBN and ISSN.
+ %
+ % Includes field URL for Internet addresses.
+ %
+ % Includes field DOI for Digital Object Idenfifiers.
+ %
+ % Works best with the url.sty package of Donald Arseneau.
+ %
+ % Works with identical authors and year are further sorted by
+ %   citation key, to preserve any natural sequence.
+ %
+ENTRY
+  { address
+    author
+    booktitle
+    chapter
+    doi
+    eid
+    edition
+    editor
+    howpublished
+    institution
+    isbn
+    issn
+    journal
+    key
+    month
+    note
+    number
+    organization
+    pages
+    publisher
+    school
+    series
+    title
+    type
+    url
+    volume
+    year
+  }
+  {}
+  { label extra.label sort.label short.list }
+
+INTEGERS { output.state before.all mid.sentence after.sentence after.block }
+
+FUNCTION {init.state.consts}
+{ #0 'before.all :=
+  #1 'mid.sentence :=
+  #2 'after.sentence :=
+  #3 'after.block :=
+}
+
+STRINGS { s t }
+
+FUNCTION {output.nonnull}
+{ 's :=
+  output.state mid.sentence =
+    { ", " * write$ }
+    { output.state after.block =
+        { add.period$ write$
+          newline$
+          "\newblock " write$
+        }
+        { output.state before.all =
+            'write$
+            { add.period$ " " * write$ }
+          if$
+        }
+      if$
+      mid.sentence 'output.state :=
+    }
+  if$
+  s
+}
+
+FUNCTION {output}
+{ duplicate$ empty$
+    'pop$
+    'output.nonnull
+  if$
+}
+
+FUNCTION {output.check}
+{ 't :=
+  duplicate$ empty$
+    { pop$ "empty " t * " in " * cite$ * warning$ }
+    'output.nonnull
+  if$
+}
+
+FUNCTION {fin.entry}
+{ add.period$
+  write$
+  newline$
+}
+
+FUNCTION {new.block}
+{ output.state before.all =
+    'skip$
+    { after.block 'output.state := }
+  if$
+}
+
+FUNCTION {new.sentence}
+{ output.state after.block =
+    'skip$
+    { output.state before.all =
+        'skip$
+        { after.sentence 'output.state := }
+      if$
+    }
+  if$
+}
+
+FUNCTION {not}
+{   { #0 }
+    { #1 }
+  if$
+}
+
+FUNCTION {and}
+{   'skip$
+    { pop$ #0 }
+  if$
+}
+
+FUNCTION {or}
+{   { pop$ #1 }
+    'skip$
+  if$
+}
+
+FUNCTION {new.block.checka}
+{ empty$
+    'skip$
+    'new.block
+  if$
+}
+
+FUNCTION {new.block.checkb}
+{ empty$
+  swap$ empty$
+  and
+    'skip$
+    'new.block
+  if$
+}
+
+FUNCTION {new.sentence.checka}
+{ empty$
+    'skip$
+    'new.sentence
+  if$
+}
+
+FUNCTION {new.sentence.checkb}
+{ empty$
+  swap$ empty$
+  and
+    'skip$
+    'new.sentence
+  if$
+}
+
+FUNCTION {field.or.null}
+{ duplicate$ empty$
+    { pop$ "" }
+    'skip$
+  if$
+}
+
+FUNCTION {emphasize}
+{ duplicate$ empty$
+    { pop$ "" }
+    { "\emph{" swap$ * "}" * }
+  if$
+}
+
+INTEGERS { nameptr namesleft numnames }
+
+FUNCTION {format.names}
+{ 's :=
+  #1 'nameptr :=
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    { s nameptr "{f.~}{vv~}{ll}{, jj}" format.name$ 't :=
+      nameptr #1 >
+        { namesleft #1 >
+            { ", " * t * }
+            { numnames #2 >
+                { "," * }
+                'skip$
+              if$
+              t "others" =
+                { " et~al." * }
+                { " and " * t * }
+              if$
+            }
+          if$
+        }
+        't
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {format.key}
+{ empty$
+    { key field.or.null }
+    { "" }
+  if$
+}
+
+FUNCTION {format.authors}
+{ author empty$
+    { "" }
+    { author format.names }
+  if$
+}
+
+FUNCTION {format.editors}
+{ editor empty$
+    { "" }
+    { editor format.names
+      editor num.names$ #1 >
+        { ", editors" * }
+        { ", editor" * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.isbn}
+{ isbn empty$
+    { "" }
+    { new.block "ISBN " isbn * }
+  if$
+}
+
+FUNCTION {format.issn}
+{ issn empty$
+    { "" }
+    { new.block "ISSN " issn * }
+  if$
+}
+
+FUNCTION {format.url}
+{ url empty$
+    { "" }
+    { new.block "URL \url{" url * "}" * }
+  if$
+}
+
+FUNCTION {format.doi}
+{ doi empty$
+    { "" }
+    { new.block "\doi{" doi * "}" * }
+  if$
+}
+
+FUNCTION {format.title}
+{ title empty$
+    { "" }
+    { title "t" change.case$ }
+  if$
+}
+
+FUNCTION {format.full.names}
+{'s :=
+  #1 'nameptr :=
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    { s nameptr
+      "{vv~}{ll}" format.name$ 't :=
+      nameptr #1 >
+        {
+          namesleft #1 >
+            { ", " * t * }
+            {
+              numnames #2 >
+                { "," * }
+                'skip$
+              if$
+              t "others" =
+                { " et~al." * }
+                { " and " * t * }
+              if$
+            }
+          if$
+        }
+        't
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {author.editor.full}
+{ author empty$
+    { editor empty$
+        { "" }
+        { editor format.full.names }
+      if$
+    }
+    { author format.full.names }
+  if$
+}
+
+FUNCTION {author.full}
+{ author empty$
+    { "" }
+    { author format.full.names }
+  if$
+}
+
+FUNCTION {editor.full}
+{ editor empty$
+    { "" }
+    { editor format.full.names }
+  if$
+}
+
+FUNCTION {make.full.names}
+{ type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.full
+    { type$ "proceedings" =
+        'editor.full
+        'author.full
+      if$
+    }
+  if$
+}
+
+FUNCTION {output.bibitem}
+{ newline$
+  "\bibitem[" write$
+  label write$
+  ")" make.full.names duplicate$ short.list =
+     { pop$ }
+     { * }
+   if$
+  "]{" * write$
+  cite$ write$
+  "}" write$
+  newline$
+  ""
+  before.all 'output.state :=
+}
+
+FUNCTION {n.dashify}
+{ 't :=
+  ""
+    { t empty$ not }
+    { t #1 #1 substring$ "-" =
+        { t #1 #2 substring$ "--" = not
+            { "--" *
+              t #2 global.max$ substring$ 't :=
+            }
+            {   { t #1 #1 substring$ "-" = }
+                { "-" *
+                  t #2 global.max$ substring$ 't :=
+                }
+              while$
+            }
+          if$
+        }
+        { t #1 #1 substring$ *
+          t #2 global.max$ substring$ 't :=
+        }
+      if$
+    }
+  while$
+}
+
+FUNCTION {format.date}
+{ year duplicate$ empty$
+    { "empty year in " cite$ * warning$
+       pop$ "" }
+    'skip$
+  if$
+  month empty$
+    'skip$
+    { month
+      " " * swap$ *
+    }
+  if$
+  extra.label *
+}
+
+FUNCTION {format.btitle}
+{ title emphasize
+}
+
+FUNCTION {tie.or.space.connect}
+{ duplicate$ text.length$ #3 <
+    { "~" }
+    { " " }
+  if$
+  swap$ * *
+}
+
+FUNCTION {either.or.check}
+{ empty$
+    'pop$
+    { "can't use both " swap$ * " fields in " * cite$ * warning$ }
+  if$
+}
+
+FUNCTION {format.bvolume}
+{ volume empty$
+    { "" }
+    { "volume" volume tie.or.space.connect
+      series empty$
+        'skip$
+        { " of " * series emphasize * }
+      if$
+      "volume and number" number either.or.check
+    }
+  if$
+}
+
+FUNCTION {format.number.series}
+{ volume empty$
+    { number empty$
+        { series field.or.null }
+        { output.state mid.sentence =
+            { "number" }
+            { "Number" }
+          if$
+          number tie.or.space.connect
+          series empty$
+            { "there's a number but no series in " cite$ * warning$ }
+            { " in " * series * }
+          if$
+        }
+      if$
+    }
+    { "" }
+  if$
+}
+
+FUNCTION {format.edition}
+{ edition empty$
+    { "" }
+    { output.state mid.sentence =
+        { edition "l" change.case$ " edition" * }
+        { edition "t" change.case$ " edition" * }
+      if$
+    }
+  if$
+}
+
+INTEGERS { multiresult }
+
+FUNCTION {multi.page.check}
+{ 't :=
+  #0 'multiresult :=
+    { multiresult not
+      t empty$ not
+      and
+    }
+    { t #1 #1 substring$
+      duplicate$ "-" =
+      swap$ duplicate$ "," =
+      swap$ "+" =
+      or or
+        { #1 'multiresult := }
+        { t #2 global.max$ substring$ 't := }
+      if$
+    }
+  while$
+  multiresult
+}
+
+FUNCTION {format.pages}
+{ pages empty$
+    { "" }
+    { pages multi.page.check
+        { "pages" pages n.dashify tie.or.space.connect }
+        { "page" pages tie.or.space.connect }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.eid}
+{ eid empty$
+    { "" }
+    { "art." eid tie.or.space.connect }
+  if$
+}
+
+FUNCTION {format.vol.num.pages}
+{ volume field.or.null
+  number empty$
+    'skip$
+    { "\penalty0 (" number * ")" * *
+      volume empty$
+        { "there's a number but no volume in " cite$ * warning$ }
+        'skip$
+      if$
+    }
+  if$
+  pages empty$
+    'skip$
+    { duplicate$ empty$
+        { pop$ format.pages }
+        { ":\penalty0 " * pages n.dashify * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.vol.num.eid}
+{ volume field.or.null
+  number empty$
+    'skip$
+    { "\penalty0 (" number * ")" * *
+      volume empty$
+        { "there's a number but no volume in " cite$ * warning$ }
+        'skip$
+      if$
+    }
+  if$
+  eid empty$
+    'skip$
+    { duplicate$ empty$
+        { pop$ format.eid }
+        { ":\penalty0 " * eid * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.chapter.pages}
+{ chapter empty$
+    'format.pages
+    { type empty$
+        { "chapter" }
+        { type "l" change.case$ }
+      if$
+      chapter tie.or.space.connect
+      pages empty$
+        'skip$
+        { ", " * format.pages * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.in.ed.booktitle}
+{ booktitle empty$
+    { "" }
+    { editor empty$
+        { "In " booktitle emphasize * }
+        { "In " format.editors * ", " * booktitle emphasize * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {empty.misc.check}
+{ author empty$ title empty$ howpublished empty$
+  month empty$ year empty$ note empty$
+  and and and and and
+  key empty$ not and
+    { "all relevant fields are empty in " cite$ * warning$ }
+    'skip$
+  if$
+}
+
+FUNCTION {format.thesis.type}
+{ type empty$
+    'skip$
+    { pop$
+      type "t" change.case$
+    }
+  if$
+}
+
+FUNCTION {format.tr.number}
+{ type empty$
+    { "Technical Report" }
+    'type
+  if$
+  number empty$
+    { "t" change.case$ }
+    { number tie.or.space.connect }
+  if$
+}
+
+FUNCTION {format.article.crossref}
+{ key empty$
+    { journal empty$
+        { "need key or journal for " cite$ * " to crossref " * crossref *
+          warning$
+          ""
+        }
+        { "In \emph{" journal * "}" * }
+      if$
+    }
+    { "In " }
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {format.book.crossref}
+{ volume empty$
+    { "empty volume in " cite$ * "'s crossref of " * crossref * warning$
+      "In "
+    }
+    { "Volume" volume tie.or.space.connect
+      " of " *
+    }
+  if$
+  editor empty$
+  editor field.or.null author field.or.null =
+  or
+    { key empty$
+        { series empty$
+            { "need editor, key, or series for " cite$ * " to crossref " *
+              crossref * warning$
+              "" *
+            }
+            { "\emph{" * series * "}" * }
+          if$
+        }
+        'skip$
+      if$
+    }
+    'skip$
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {format.incoll.inproc.crossref}
+{ editor empty$
+  editor field.or.null author field.or.null =
+  or
+    { key empty$
+        { booktitle empty$
+            { "need editor, key, or booktitle for " cite$ * " to crossref " *
+              crossref * warning$
+              ""
+            }
+            { "In \emph{" booktitle * "}" * }
+          if$
+        }
+        { "In " }
+      if$
+    }
+    { "In " }
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {article}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { journal emphasize "journal" output.check
+      eid empty$
+        { format.vol.num.pages output }
+        { format.vol.num.eid output }
+      if$
+      format.date "year" output.check
+    }
+    { format.article.crossref output.nonnull
+      eid empty$
+        { format.pages output }
+        { format.eid output }
+      if$
+    }
+  if$
+  format.issn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {book}
+{ output.bibitem
+  author empty$
+    { format.editors "author and editor" output.check
+      editor format.key output
+    }
+    { format.authors output.nonnull
+      crossref missing$
+        { "author and editor" editor either.or.check }
+        'skip$
+      if$
+    }
+  if$
+  new.block
+  format.btitle "title" output.check
+  crossref missing$
+    { format.bvolume output
+      new.block
+      format.number.series output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+    }
+    { new.block
+      format.book.crossref output.nonnull
+    }
+  if$
+  format.edition output
+  format.date "year" output.check
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {booklet}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  new.block
+  format.title "title" output.check
+  howpublished address new.block.checkb
+  howpublished output
+  address output
+  format.date output
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {inbook}
+{ output.bibitem
+  author empty$
+    { format.editors "author and editor" output.check
+      editor format.key output
+    }
+    { format.authors output.nonnull
+      crossref missing$
+        { "author and editor" editor either.or.check }
+        'skip$
+      if$
+    }
+  if$
+  new.block
+  format.btitle "title" output.check
+  crossref missing$
+    { format.bvolume output
+      format.chapter.pages "chapter and pages" output.check
+      new.block
+      format.number.series output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+    }
+    { format.chapter.pages "chapter and pages" output.check
+      new.block
+      format.book.crossref output.nonnull
+    }
+  if$
+  format.edition output
+  format.date "year" output.check
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {incollection}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { format.in.ed.booktitle "booktitle" output.check
+      format.bvolume output
+      format.number.series output
+      format.chapter.pages output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+      format.edition output
+      format.date "year" output.check
+    }
+    { format.incoll.inproc.crossref output.nonnull
+      format.chapter.pages output
+    }
+  if$
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {inproceedings}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { format.in.ed.booktitle "booktitle" output.check
+      format.bvolume output
+      format.number.series output
+      format.pages output
+      address empty$
+        { organization publisher new.sentence.checkb
+          organization output
+          publisher output
+          format.date "year" output.check
+        }
+        { address output.nonnull
+          format.date "year" output.check
+          new.sentence
+          organization output
+          publisher output
+        }
+      if$
+    }
+    { format.incoll.inproc.crossref output.nonnull
+      format.pages output
+    }
+  if$
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {conference} { inproceedings }
+
+FUNCTION {manual}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  new.block
+  format.btitle "title" output.check
+  organization address new.block.checkb
+  organization output
+  address output
+  format.edition output
+  format.date output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {mastersthesis}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  "Master's thesis" format.thesis.type output.nonnull
+  school "school" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {misc}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  title howpublished new.block.checkb
+  format.title output
+  howpublished new.block.checka
+  howpublished output
+  format.date output
+  format.issn output
+  format.url output
+  new.block
+  note output
+  fin.entry
+  empty.misc.check
+}
+
+FUNCTION {phdthesis}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.btitle "title" output.check
+  new.block
+  "PhD thesis" format.thesis.type output.nonnull
+  school "school" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {proceedings}
+{ output.bibitem
+  format.editors output
+  editor format.key output
+  new.block
+  format.btitle "title" output.check
+  format.bvolume output
+  format.number.series output
+  address output
+  format.date "year" output.check
+  new.sentence
+  organization output
+  publisher output
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {techreport}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  format.tr.number output.nonnull
+  institution "institution" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {unpublished}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  note "note" output.check
+  format.date output
+  format.url output
+  fin.entry
+}
+
+FUNCTION {default.type} { misc }
+
+
+MACRO {jan} {"Jan."}
+
+MACRO {feb} {"Feb."}
+
+MACRO {mar} {"Mar."}
+
+MACRO {apr} {"Apr."}
+
+MACRO {may} {"May"}
+
+MACRO {jun} {"June"}
+
+MACRO {jul} {"July"}
+
+MACRO {aug} {"Aug."}
+
+MACRO {sep} {"Sept."}
+
+MACRO {oct} {"Oct."}
+
+MACRO {nov} {"Nov."}
+
+MACRO {dec} {"Dec."}
+
+
+
+MACRO {acmcs} {"ACM Comput. Surv."}
+
+MACRO {acta} {"Acta Inf."}
+
+MACRO {cacm} {"Commun. ACM"}
+
+MACRO {ibmjrd} {"IBM J. Res. Dev."}
+
+MACRO {ibmsj} {"IBM Syst.~J."}
+
+MACRO {ieeese} {"IEEE Trans. Softw. Eng."}
+
+MACRO {ieeetc} {"IEEE Trans. Comput."}
+
+MACRO {ieeetcad}
+ {"IEEE Trans. Comput.-Aided Design Integrated Circuits"}
+
+MACRO {ipl} {"Inf. Process. Lett."}
+
+MACRO {jacm} {"J.~ACM"}
+
+MACRO {jcss} {"J.~Comput. Syst. Sci."}
+
+MACRO {scp} {"Sci. Comput. Programming"}
+
+MACRO {sicomp} {"SIAM J. Comput."}
+
+MACRO {tocs} {"ACM Trans. Comput. Syst."}
+
+MACRO {tods} {"ACM Trans. Database Syst."}
+
+MACRO {tog} {"ACM Trans. Gr."}
+
+MACRO {toms} {"ACM Trans. Math. Softw."}
+
+MACRO {toois} {"ACM Trans. Office Inf. Syst."}
+
+MACRO {toplas} {"ACM Trans. Prog. Lang. Syst."}
+
+MACRO {tcs} {"Theoretical Comput. Sci."}
+
+
+READ
+
+FUNCTION {sortify}
+{ purify$
+  "l" change.case$
+}
+
+INTEGERS { len }
+
+FUNCTION {chop.word}
+{ 's :=
+  'len :=
+  s #1 len substring$ =
+    { s len #1 + global.max$ substring$ }
+    's
+  if$
+}
+
+FUNCTION {format.lab.names}
+{ 's :=
+  s #1 "{vv~}{ll}" format.name$
+  s num.names$ duplicate$
+  #2 >
+    { pop$ " et~al." * }
+    { #2 <
+        'skip$
+        { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" =
+            { " et~al." * }
+            { " and " * s #2 "{vv~}{ll}" format.name$ * }
+          if$
+        }
+      if$
+    }
+  if$
+}
+
+FUNCTION {author.key.label}
+{ author empty$
+    { key empty$
+        { cite$ #1 #3 substring$ }
+        'key
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {author.editor.key.label}
+{ author empty$
+    { editor empty$
+        { key empty$
+            { cite$ #1 #3 substring$ }
+            'key
+          if$
+        }
+        { editor format.lab.names }
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {author.key.organization.label}
+{ author empty$
+    { key empty$
+        { organization empty$
+            { cite$ #1 #3 substring$ }
+            { "The " #4 organization chop.word #3 text.prefix$ }
+          if$
+        }
+        'key
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {editor.key.organization.label}
+{ editor empty$
+    { key empty$
+        { organization empty$
+            { cite$ #1 #3 substring$ }
+            { "The " #4 organization chop.word #3 text.prefix$ }
+          if$
+        }
+        'key
+      if$
+    }
+    { editor format.lab.names }
+  if$
+}
+
+FUNCTION {calc.short.authors}
+{ type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.key.label
+    { type$ "proceedings" =
+        'editor.key.organization.label
+        { type$ "manual" =
+            'author.key.organization.label
+            'author.key.label
+          if$
+        }
+      if$
+    }
+  if$
+  'short.list :=
+}
+
+FUNCTION {calc.label}
+{ calc.short.authors
+  short.list
+  "("
+  *
+  year duplicate$ empty$
+  short.list key field.or.null = or
+     { pop$ "" }
+     'skip$
+  if$
+  *
+  'label :=
+}
+
+FUNCTION {sort.format.names}
+{ 's :=
+  #1 'nameptr :=
+  ""
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    {
+      s nameptr "{vv{ } }{ll{ }}{  f{ }}{  jj{ }}" format.name$ 't :=
+      nameptr #1 >
+        {
+          "   "  *
+          namesleft #1 = t "others" = and
+            { "zzzzz" * }
+            { numnames #2 > nameptr #2 = and
+                { "zz" * year field.or.null * "   " * }
+                'skip$
+              if$
+              t sortify *
+            }
+          if$
+        }
+        { t sortify * }
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {sort.format.title}
+{ 't :=
+  "A " #2
+    "An " #3
+      "The " #4 t chop.word
+    chop.word
+  chop.word
+  sortify
+  #1 global.max$ substring$
+}
+
+FUNCTION {author.sort}
+{ author empty$
+    { key empty$
+        { "to sort, need author or key in " cite$ * warning$
+          ""
+        }
+        { key sortify }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {author.editor.sort}
+{ author empty$
+    { editor empty$
+        { key empty$
+            { "to sort, need author, editor, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { editor sort.format.names }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {author.organization.sort}
+{ author empty$
+    { organization empty$
+        { key empty$
+            { "to sort, need author, organization, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { "The " #4 organization chop.word sortify }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {editor.organization.sort}
+{ editor empty$
+    { organization empty$
+        { key empty$
+            { "to sort, need editor, organization, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { "The " #4 organization chop.word sortify }
+      if$
+    }
+    { editor sort.format.names }
+  if$
+}
+
+
+FUNCTION {presort}
+{ calc.label
+  label sortify
+  "    "
+  *
+  type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.sort
+    { type$ "proceedings" =
+        'editor.organization.sort
+        { type$ "manual" =
+            'author.organization.sort
+            'author.sort
+          if$
+        }
+      if$
+    }
+  if$
+  "    "
+  *
+  year field.or.null sortify
+  *
+  "    "
+  *
+  cite$
+  *
+  #1 entry.max$ substring$
+  'sort.label :=
+  sort.label *
+  #1 entry.max$ substring$
+  'sort.key$ :=
+}
+
+ITERATE {presort}
+
+% SORT
+
+STRINGS { longest.label last.label next.extra }
+
+INTEGERS { longest.label.width last.extra.num number.label }
+
+FUNCTION {initialize.longest.label}
+{ "" 'longest.label :=
+  #0 int.to.chr$ 'last.label :=
+  "" 'next.extra :=
+  #0 'longest.label.width :=
+  #0 'last.extra.num :=
+  #0 'number.label :=
+}
+
+FUNCTION {forward.pass}
+{ last.label label =
+    { last.extra.num #1 + 'last.extra.num :=
+      last.extra.num int.to.chr$ 'extra.label :=
+    }
+    { "a" chr.to.int$ 'last.extra.num :=
+      "" 'extra.label :=
+      label 'last.label :=
+    }
+  if$
+  number.label #1 + 'number.label :=
+}
+
+FUNCTION {reverse.pass}
+{ next.extra "b" =
+    { "a" 'extra.label := }
+    'skip$
+  if$
+  extra.label 'next.extra :=
+  extra.label
+  duplicate$ empty$
+    'skip$
+    { "{\natexlab{" swap$ * "}}" * }
+  if$
+  'extra.label :=
+  label extra.label * 'label :=
+}
+
+EXECUTE {initialize.longest.label}
+
+ITERATE {forward.pass}
+
+REVERSE {reverse.pass}
+
+FUNCTION {bib.sort.order}
+{ sort.label  'sort.key$ :=
+}
+
+ITERATE {bib.sort.order}
+
+% SORT
+
+FUNCTION {begin.bib}
+{   preamble$ empty$
+    'skip$
+    { preamble$ write$ newline$ }
+  if$
+  "\begin{thebibliography}{" number.label int.to.str$ * "}" *
+  write$ newline$
+  "\providecommand{\natexlab}[1]{#1}"
+  write$ newline$
+  "\providecommand{\url}[1]{\texttt{#1}}"
+  write$ newline$
+  "\expandafter\ifx\csname urlstyle\endcsname\relax"
+  write$ newline$
+  "  \providecommand{\doi}[1]{doi: #1}\else"
+  write$ newline$
+  "  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi"
+  write$ newline$
+}
+
+EXECUTE {begin.bib}
+
+EXECUTE {init.state.consts}
+
+ITERATE {call.type$}
+
+FUNCTION {end.bib}
+{ newline$
+  "\end{thebibliography}" write$ newline$
+}
+
+EXECUTE {end.bib}
diff --git a/workspace/drafts/paper.aux b/workspace/drafts/paper.aux
new file mode 100644
index 0000000..12a7aa1
--- /dev/null
+++ b/workspace/drafts/paper.aux
@@ -0,0 +1,118 @@
+\relax 
+\bibstyle{corlabbrvnat}
+\providecommand\hyper@newdestlabel[2]{}
+\providecommand\HyField@AuxAddToFields[1]{}
+\providecommand\HyField@AuxAddToCoFields[2]{}
+\citation{chi2023diffusion}
+\citation{ho2020denoising}
+\citation{peebles2023scalable}
+\citation{lipman2023flow,liu2022flow}
+\citation{geng2025mean}
+\citation{geng2025improved}
+\citation{he2016deep,vaswani2017attention}
+\citation{kimi2026attention}
+\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}{section.1}\protected@file@percent }
+\newlabel{sec:introduction}{{1}{1}{Introduction}{section.1}{}}
+\newlabel{sec:introduction@cref}{{[section][1][]1}{[1][1][]1}{}{}{}}
+\citation{zhao2023learning,shukor2025smolvla}
+\citation{chi2023diffusion}
+\citation{ho2020denoising}
+\citation{peebles2023scalable}
+\citation{lipman2023flow}
+\citation{liu2022flow}
+\citation{brohan2022rt,brohan2023rt}
+\citation{kim2024openvla}
+\citation{shukor2025smolvla,pertsch2025fast}
+\citation{zhao2023learning}
+\citation{geng2025mean}
+\def\@LN@column{1}
+\@writefile{toc}{\contentsline {section}{\numberline {2}Related Work}{2}{section.2}\protected@file@percent }
+\newlabel{sec:related_work}{{2}{2}{Related Work}{section.2}{}}
+\newlabel{sec:related_work@cref}{{[section][2][]2}{[1][2][]2}{}{}{}}
+\@writefile{toc}{\contentsline {paragraph}{Diffusion and flow policies for robot action generation.}{2}{section*.1}\protected@file@percent }
+\@writefile{toc}{\contentsline {paragraph}{Vision-language-action models and action chunking.}{2}{section*.2}\protected@file@percent }
+\@writefile{toc}{\contentsline {paragraph}{Mean-flow training for one-step generation.}{2}{section*.3}\protected@file@percent }
+\citation{geng2025improved}
+\citation{kimi2026attention}
+\def\@LN@column{1}
+\@writefile{toc}{\contentsline {paragraph}{Residual aggregation and attention residuals.}{3}{section*.4}\protected@file@percent }
+\@writefile{toc}{\contentsline {section}{\numberline {3}Method}{3}{section.3}\protected@file@percent }
+\newlabel{sec:method}{{3}{3}{Method}{section.3}{}}
+\newlabel{sec:method@cref}{{[section][3][]3}{[1][3][]3}{}{}{}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Policy formulation}{3}{subsection.3.1}\protected@file@percent }
+\newlabel{sec:policy_formulation}{{3.1}{3}{Policy formulation}{subsection.3.1}{}}
+\newlabel{sec:policy_formulation@cref}{{[subsection][1][3]3.1}{[1][3][]3}{}{}{}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Improved Mean Flow action generation}{3}{subsection.3.2}\protected@file@percent }
+\newlabel{sec:imf_action_generation}{{3.2}{3}{Improved Mean Flow action generation}{subsection.3.2}{}}
+\newlabel{sec:imf_action_generation@cref}{{[subsection][2][3]3.2}{[1][3][]3}{}{}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Planned method overview figure. The current draft intentionally stores the Paper Banana generation prompt as a placeholder instead of generating an image.}}{4}{figure.1}\protected@file@percent }
+\newlabel{fig:method_overview}{{1}{4}{Planned method overview figure. The current draft intentionally stores the Paper Banana generation prompt as a placeholder instead of generating an image}{figure.1}{}}
+\newlabel{fig:method_overview@cref}{{[figure][1][]1}{[1][4][]4}{}{}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Planned technical component figure. The prompt is retained as a placeholder for later Paper Banana rendering.}}{4}{figure.2}\protected@file@percent }
+\newlabel{fig:imf_attnres_components}{{2}{4}{Planned technical component figure. The prompt is retained as a placeholder for later Paper Banana rendering}{figure.2}{}}
+\newlabel{fig:imf_attnres_components@cref}{{[figure][2][]2}{[1][4][]4}{}{}{}}
+\def\@LN@column{1}
+\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Attention Residual policy transformer}{4}{subsection.3.3}\protected@file@percent }
+\newlabel{sec:attnres_policy}{{3.3}{4}{Attention Residual policy transformer}{subsection.3.3}{}}
+\newlabel{sec:attnres_policy@cref}{{[subsection][3][3]3.3}{[1][4][]4}{}{}{}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {3.4}Inference and control loop}{4}{subsection.3.4}\protected@file@percent }
+\newlabel{sec:inference_loop}{{3.4}{4}{Inference and control loop}{subsection.3.4}{}}
+\newlabel{sec:inference_loop@cref}{{[subsection][4][3]3.4}{[1][4][]4}{}{}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {4}Experiments}{4}{section.4}\protected@file@percent }
+\newlabel{sec:experiments}{{4}{4}{Experiments}{section.4}{}}
+\newlabel{sec:experiments@cref}{{[section][4][]4}{[1][4][]4}{}{}{}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}Setup and metrics}{4}{subsection.4.1}\protected@file@percent }
+\newlabel{sec:setup_metrics}{{4.1}{4}{Setup and metrics}{subsection.4.1}{}}
+\newlabel{sec:setup_metrics@cref}{{[subsection][1][4]4.1}{[1][4][]4}{}{}{}}
+\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces Socket peg insertion over 100 rollouts. Success-like uses the recorded \texttt  {max\_reward > 4} count.}}{5}{table.1}\protected@file@percent }
+\newlabel{tab:socket_results}{{1}{5}{Socket peg insertion over 100 rollouts. Success-like uses the recorded \texttt {max\_reward > 4} count}{table.1}{}}
+\newlabel{tab:socket_results@cref}{{[table][1][]1}{[1][5][]5}{}{}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces Planned socket reward-latency figure. The current draft contains the Paper Banana prompt placeholder instead of a rendered image.}}{5}{figure.3}\protected@file@percent }
+\newlabel{fig:socket_reward_latency}{{3}{5}{Planned socket reward-latency figure. The current draft contains the Paper Banana prompt placeholder instead of a rendered image}{figure.3}{}}
+\newlabel{fig:socket_reward_latency@cref}{{[figure][3][]3}{[1][5][]5}{}{}{}}
+\def\@LN@column{1}
+\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}Socket peg insertion}{5}{subsection.4.2}\protected@file@percent }
+\newlabel{sec:socket_results}{{4.2}{5}{Socket peg insertion}{subsection.4.2}{}}
+\newlabel{sec:socket_results@cref}{{[subsection][2][4]4.2}{[1][5][]5}{}{}{}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}Object transfer and ablations}{5}{subsection.4.3}\protected@file@percent }
+\newlabel{sec:sim_transfer_results}{{4.3}{5}{Object transfer and ablations}{subsection.4.3}{}}
+\newlabel{sec:sim_transfer_results@cref}{{[subsection][3][4]4.3}{[1][5][]5}{}{}{}}
+\@writefile{lot}{\contentsline {table}{\numberline {2}{\ignorespaces Object transfer / sim\_transfer over 100 rollouts. Success-like uses the recorded \texttt  {max\_reward >= 4} count.}}{6}{table.2}\protected@file@percent }
+\newlabel{tab:sim_transfer_results}{{2}{6}{Object transfer / sim\_transfer over 100 rollouts. Success-like uses the recorded \texttt {max\_reward >= 4} count}{table.2}{}}
+\newlabel{tab:sim_transfer_results@cref}{{[table][2][]2}{[1][5][]6}{}{}{}}
+\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces Planned sim\_transfer ablation figure. The prompt is retained for later Paper Banana rendering.}}{6}{figure.4}\protected@file@percent }
+\newlabel{fig:sim_transfer_ablation}{{4}{6}{Planned sim\_transfer ablation figure. The prompt is retained for later Paper Banana rendering}{figure.4}{}}
+\newlabel{fig:sim_transfer_ablation@cref}{{[figure][4][]4}{[1][6][]6}{}{}{}}
+\def\@LN@column{1}
+\@writefile{toc}{\contentsline {subsection}{\numberline {4.4}Derived speed-quality comparisons}{6}{subsection.4.4}\protected@file@percent }
+\newlabel{sec:derived_comparisons}{{4.4}{6}{Derived speed-quality comparisons}{subsection.4.4}{}}
+\newlabel{sec:derived_comparisons@cref}{{[subsection][4][4]4.4}{[1][6][]6}{}{}{}}
+\@writefile{toc}{\contentsline {section}{\numberline {5}Limitations}{6}{section.5}\protected@file@percent }
+\newlabel{sec:limitations}{{5}{6}{Limitations}{section.5}{}}
+\newlabel{sec:limitations@cref}{{[section][5][]5}{[1][6][]6}{}{}{}}
+\@writefile{lot}{\contentsline {table}{\numberline {3}{\ignorespaces Derived comparisons from the 100-rollout logs.}}{7}{table.3}\protected@file@percent }
+\newlabel{tab:derived_comparisons}{{3}{7}{Derived comparisons from the 100-rollout logs}{table.3}{}}
+\newlabel{tab:derived_comparisons@cref}{{[table][3][]3}{[1][6][]7}{}{}{}}
+\def\@LN@column{1}
+\@writefile{toc}{\contentsline {section}{\numberline {6}Conclusion}{7}{section.6}\protected@file@percent }
+\newlabel{sec:conclusion}{{6}{7}{Conclusion}{section.6}{}}
+\newlabel{sec:conclusion@cref}{{[section][6][]6}{[1][7][]7}{}{}{}}
+\bibdata{refs}
+\bibcite{chi2023diffusion}{{1}{2023}{{Chi et~al.}}{{Chi, Xu, Feng, Cousineau, Du, Burchfiel, Tedrake, and Song}}}
+\bibcite{ho2020denoising}{{2}{2020}{{Ho et~al.}}{{Ho, Jain, and Abbeel}}}
+\bibcite{peebles2023scalable}{{3}{2023}{{Peebles and Xie}}{{}}}
+\bibcite{lipman2023flow}{{4}{2023}{{Lipman et~al.}}{{Lipman, Chen, Ben-Hamu, Nickel, and Le}}}
+\bibcite{liu2022flow}{{5}{2022}{{Liu et~al.}}{{Liu, Gong, and Liu}}}
+\bibcite{geng2025mean}{{6}{2025{}}{{Geng et~al.}}{{Geng, Deng, Bai, Kolter, and He}}}
+\bibcite{geng2025improved}{{7}{2025{}}{{Geng et~al.}}{{Geng, Lu, Wu, Shechtman, Kolter, and He}}}
+\bibcite{he2016deep}{{8}{2016}{{He et~al.}}{{He, Zhang, Ren, and Sun}}}
+\bibcite{vaswani2017attention}{{9}{2017}{{Vaswani et~al.}}{{Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, and Polosukhin}}}
+\bibcite{kimi2026attention}{{10}{2026}{{Kimi Team}}{{}}}
+\bibcite{zhao2023learning}{{11}{2023}{{Zhao et~al.}}{{Zhao, Kumar, Levine, and Finn}}}
+\bibcite{shukor2025smolvla}{{12}{2025}{{Shukor et~al.}}{{Shukor, Aubakirova, Capuano, Kooijmans, Palma, Zouitine, Aractingi, Pascal, Russi, Marafioti, et~al.}}}
+\bibcite{brohan2022rt}{{13}{2022}{{Brohan et~al.}}{{Brohan, Brown, Carbajal, Chebotar, Dabis, Finn, Gopalakrishnan, Hausman, Herzog, Hsu, et~al.}}}
+\bibcite{brohan2023rt}{{14}{2023}{{Brohan et~al.}}{{Brohan, Brown, Carbajal, Chebotar, Chen, Choromanski, Ding, Driess, Dubey, Finn, et~al.}}}
+\bibcite{kim2024openvla}{{15}{2024}{{Kim et~al.}}{{Kim, Pertsch, Karamcheti, Xiao, Balakrishna, Nair, Rafailov, Foster, Lam, Sanketi, et~al.}}}
+\bibcite{pertsch2025fast}{{16}{2025}{{Pertsch et~al.}}{{Pertsch, Stachowicz, Ichter, Driess, Nair, Vuong, Mees, Finn, and Levine}}}
+\def\@LN@column{1}
+\gdef \@abspage@last{8}
diff --git a/workspace/drafts/paper.bbl b/workspace/drafts/paper.bbl
new file mode 100644
index 0000000..42c4a54
--- /dev/null
+++ b/workspace/drafts/paper.bbl
@@ -0,0 +1,111 @@
+\begin{thebibliography}{16}
+\providecommand{\natexlab}[1]{#1}
+\providecommand{\url}[1]{\texttt{#1}}
+\expandafter\ifx\csname urlstyle\endcsname\relax
+  \providecommand{\doi}[1]{doi: #1}\else
+  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi
+
+\bibitem[Chi et~al.(2023)Chi, Xu, Feng, Cousineau, Du, Burchfiel, Tedrake, and
+  Song]{chi2023diffusion}
+C.~Chi, Z.~Xu, S.~Feng, E.~Cousineau, Y.~Du, B.~Burchfiel, R.~Tedrake, and
+  S.~Song.
+\newblock Diffusion policy: Visuomotor policy learning via action diffusion.
+\newblock \emph{arXiv preprint arXiv:2303.04137}, 2023.
+
+\bibitem[Ho et~al.(2020)Ho, Jain, and Abbeel]{ho2020denoising}
+J.~Ho, A.~Jain, and P.~Abbeel.
+\newblock Denoising diffusion probabilistic models.
+\newblock In \emph{Advances in Neural Information Processing Systems}, 2020.
+
+\bibitem[Peebles and Xie(2023)]{peebles2023scalable}
+W.~Peebles and S.~Xie.
+\newblock Scalable diffusion models with transformers.
+\newblock In \emph{IEEE/CVF International Conference on Computer Vision}, 2023.
+
+\bibitem[Lipman et~al.(2023)Lipman, Chen, Ben-Hamu, Nickel, and
+  Le]{lipman2023flow}
+Y.~Lipman, R.~T.~Q. Chen, H.~Ben-Hamu, M.~Nickel, and M.~Le.
+\newblock Flow matching for generative modeling.
+\newblock In \emph{International Conference on Learning Representations}, 2023.
+
+\bibitem[Liu et~al.(2022)Liu, Gong, and Liu]{liu2022flow}
+X.~Liu, C.~Gong, and Q.~Liu.
+\newblock Flow straight and fast: Learning to generate and transfer data with
+  rectified flow.
+\newblock \emph{arXiv preprint arXiv:2209.03003}, 2022.
+
+\bibitem[Geng et~al.(2025{\natexlab{a}})Geng, Deng, Bai, Kolter, and
+  He]{geng2025mean}
+Z.~Geng, M.~Deng, X.~Bai, J.~Z. Kolter, and K.~He.
+\newblock Mean flows for one-step generative modeling.
+\newblock \emph{arXiv preprint arXiv:2505.13447}, 2025{\natexlab{a}}.
+
+\bibitem[Geng et~al.(2025{\natexlab{b}})Geng, Lu, Wu, Shechtman, Kolter, and
+  He]{geng2025improved}
+Z.~Geng, Y.~Lu, Z.~Wu, E.~Shechtman, J.~Z. Kolter, and K.~He.
+\newblock Improved mean flows: On the challenges of fastforward generative
+  models.
+\newblock \emph{arXiv preprint arXiv:2512.02012}, 2025{\natexlab{b}}.
+
+\bibitem[He et~al.(2016)He, Zhang, Ren, and Sun]{he2016deep}
+K.~He, X.~Zhang, S.~Ren, and J.~Sun.
+\newblock Deep residual learning for image recognition.
+\newblock In \emph{IEEE Conference on Computer Vision and Pattern Recognition},
+  2016.
+
+\bibitem[Vaswani et~al.(2017)Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez,
+  Kaiser, and Polosukhin]{vaswani2017attention}
+A.~Vaswani, N.~Shazeer, N.~Parmar, J.~Uszkoreit, L.~Jones, A.~N. Gomez,
+  L.~Kaiser, and I.~Polosukhin.
+\newblock Attention is all you need.
+\newblock In \emph{Advances in Neural Information Processing Systems}, 2017.
+
+\bibitem[{Kimi Team}(2026)]{kimi2026attention}
+{Kimi Team}.
+\newblock Attention residuals.
+\newblock \emph{arXiv preprint arXiv:2603.15031}, 2026.
+
+\bibitem[Zhao et~al.(2023)Zhao, Kumar, Levine, and Finn]{zhao2023learning}
+T.~Z. Zhao, V.~Kumar, S.~Levine, and C.~Finn.
+\newblock Learning fine-grained bimanual manipulation with low-cost hardware.
+\newblock \emph{arXiv preprint arXiv:2304.13705}, 2023.
+
+\bibitem[Shukor et~al.(2025)Shukor, Aubakirova, Capuano, Kooijmans, Palma,
+  Zouitine, Aractingi, Pascal, Russi, Marafioti, et~al.]{shukor2025smolvla}
+M.~Shukor, D.~Aubakirova, F.~Capuano, P.~Kooijmans, S.~Palma, A.~Zouitine,
+  M.~Aractingi, C.~Pascal, M.~Russi, A.~Marafioti, et~al.
+\newblock Smolvla: A vision-language-action model for affordable and efficient
+  robotics.
+\newblock \emph{arXiv preprint arXiv:2506.01844}, 2025.
+
+\bibitem[Brohan et~al.(2022)Brohan, Brown, Carbajal, Chebotar, Dabis, Finn,
+  Gopalakrishnan, Hausman, Herzog, Hsu, et~al.]{brohan2022rt}
+A.~Brohan, N.~Brown, J.~Carbajal, Y.~Chebotar, J.~Dabis, C.~Finn,
+  K.~Gopalakrishnan, K.~Hausman, A.~Herzog, J.~Hsu, et~al.
+\newblock Rt-1: Robotics transformer for real-world control at scale.
+\newblock \emph{arXiv preprint arXiv:2212.06817}, 2022.
+
+\bibitem[Brohan et~al.(2023)Brohan, Brown, Carbajal, Chebotar, Chen,
+  Choromanski, Ding, Driess, Dubey, Finn, et~al.]{brohan2023rt}
+A.~Brohan, N.~Brown, J.~Carbajal, Y.~Chebotar, X.~Chen, K.~Choromanski,
+  T.~Ding, D.~Driess, A.~Dubey, C.~Finn, et~al.
+\newblock Rt-2: Vision-language-action models transfer web knowledge to robotic
+  control.
+\newblock In \emph{Conference on Robot Learning}, 2023.
+
+\bibitem[Kim et~al.(2024)Kim, Pertsch, Karamcheti, Xiao, Balakrishna, Nair,
+  Rafailov, Foster, Lam, Sanketi, et~al.]{kim2024openvla}
+M.~J. Kim, K.~Pertsch, S.~Karamcheti, T.~Xiao, A.~Balakrishna, S.~Nair,
+  R.~Rafailov, E.~Foster, G.~Lam, P.~Sanketi, et~al.
+\newblock Openvla: An open-source vision-language-action model.
+\newblock \emph{arXiv preprint arXiv:2406.09246}, 2024.
+
+\bibitem[Pertsch et~al.(2025)Pertsch, Stachowicz, Ichter, Driess, Nair, Vuong,
+  Mees, Finn, and Levine]{pertsch2025fast}
+K.~Pertsch, K.~Stachowicz, B.~Ichter, D.~Driess, S.~Nair, Q.~Vuong, O.~Mees,
+  C.~Finn, and S.~Levine.
+\newblock Fast: Efficient action tokenization for vision-language-action
+  models.
+\newblock \emph{arXiv preprint arXiv:2501.09747}, 2025.
+
+\end{thebibliography}
diff --git a/workspace/drafts/paper.blg b/workspace/drafts/paper.blg
new file mode 100644
index 0000000..8a536d2
--- /dev/null
+++ b/workspace/drafts/paper.blg
@@ -0,0 +1,46 @@
+This is BibTeX, Version 0.99e (TeX Live 2026/Arch Linux)
+Capacity: max_strings=200000, hash_size=200000, hash_prime=170003
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+
+\documentclass{article}
+
+\usepackage{corl_2026}
+\usepackage[T1]{fontenc}
+\usepackage{microtype}
+\usepackage{booktabs}
+\usepackage{multirow}
+\usepackage{graphicx}
+\usepackage{amsmath,amssymb}
+\usepackage{enumitem}
+\usepackage{tcolorbox}
+\usepackage[capitalize]{cleveref}
+\usepackage{url}
+
+\title{iMF-AttnRes: Fast Mean-Flow Action Generation with Attention Residuals for Simulated Robot Manipulation}
+
+\author{Anonymous Authors}
+
+\begin{document}
+\maketitle
+
+\begin{abstract}
+Diffusion-style vision-language-action policies provide expressive action distributions, but iterative inference can be too slow for closed-loop manipulation. We study whether improved Mean Flow (iMF), originally motivated by fast-forward generative modeling, can be adapted to action generation so that a policy uses one to a few flow evaluations rather than long denoising chains. We combine iMF with Attention Residuals (AttnRes), which replace fixed residual accumulation in a deep policy transformer with learned depth-wise aggregation over previous layer outputs. In RoboIMI socket peg insertion, the best iMF-AttnRes policy reaches an average reward of 1513.56 and median reward of 1901.5 with 15.122 ms average inference time, compared with diffusion-style infer100 baselines at 861.53/338.291 ms and 1019.39/397.912 ms. In RoboIMI object transfer, the best iMF-AttnRes policy reaches average reward 526.22 and 44/100 success-like episodes, compared with a native diffusion-policy baseline at 319.2 and 29/100. These results suggest that average-flow action generation is a promising route to low-latency robot policies, while also revealing sensitivity to horizon choices, vision-token design, and simulation-to-real validation.
+\end{abstract}
+
+\keywords{Robot learning, imitation learning, flow matching, vision-language-action models}
+
+\section{Introduction}
+\label{sec:introduction}
+
+Learning-based robot manipulation policies increasingly use expressive generative action models. Diffusion Policy showed that action diffusion can be an effective visuomotor policy class for manipulation~\citep{black2024pi0,chi2023diffusion}, inheriting the representational flexibility of denoising diffusion models~\citep{ho2020denoising} and transformer-based diffusion backbones~\citep{peebles2023scalable}. However, this expressivity often comes with a deployment cost: the policy must be evaluated repeatedly during sampling. In the RoboIMI socket peg experiments studied here, two diffusion-style VLA baselines use infer100 sampling and require 338.291 ms and 397.912 ms per inference on their recorded rollouts. Such latencies are undesirable when the robot must repeatedly close the perception-action loop.
+
+This paper asks a direct question: can a robot action generator retain the quality benefits of generative policies while reducing inference to one or a few evaluations? We explore this question by adapting improved Mean Flow (iMF) to VLA-style imitation learning. Flow matching and rectified flow formulate generation through continuous vector fields~\citep{lipman2023flow,liu2022flow}. Mean Flow reframes generation around average velocity, enabling one-step generation~\citep{geng2025mean}; iMF further addresses instability caused by substituting conditional velocities into nonlinear mean-flow relations~\citep{geng2025improved}. For robot control, this distinction matters because fewer evaluations directly increase control responsiveness.
+
+We pair iMF with Attention Residuals (AttnRes). Residual connections are essential for deep visual and transformer networks~\citep{he2016deep,vaswani2017attention}, but standard residual accumulation adds all layer outputs with fixed unit weight. The motivation in our notes is to rewrite residual networks as a sum over layer increments, then replace the uniform sum with a learned softmax aggregation. AttnRes implements this view by letting each layer attend over preceding residual outputs~\citep{kimi2026attention}. We use this mechanism in the policy transformer to reduce depth-wise feature dilution while keeping the action generator fast.
+
+Our contributions are:
+\begin{enumerate}[leftmargin=*]
+    \item We formulate an iMF-AttnRes VLA policy for simulated robot imitation learning, combining one-to-few-step average-flow action generation with learned residual aggregation.
+    \item We provide 100-rollout evaluations on two RoboIMI manipulation environments, socket peg insertion and object transfer, comparing against diffusion-style VLA baselines, a native Diffusion Policy baseline, ACT-style action chunking, and SmolVLA-style compact VLA inference~\citep{zhao2023learning,shukor2025smolvla}.
+    \item We report both task quality and deployment speed. On socket insertion, the strongest iMF-AttnRes run improves average reward over the ph16 diffusion-style baseline by 1.76$\times$ and inference FPS by 22.97$\times$; on object transfer, the strongest iMF-AttnRes run improves average reward over the native diffusion-policy baseline by 1.65$\times$.
+\end{enumerate}
+We deliberately keep the claims scoped to simulation: hardware varies across runs, and real-robot transfer remains future work.
+
+\section{Related Work}
+\label{sec:related_work}
+
+\paragraph{Diffusion and flow policies for robot action generation.}
+Diffusion Policy introduced action diffusion as a visuomotor policy learning framework~\citep{black2024pi0,chi2023diffusion}, building on denoising diffusion objectives~\citep{ho2020denoising}. Diffusion transformers further show that transformer backbones can scale generative modeling~\citep{peebles2023scalable}. These models are expressive because they iteratively refine samples, but iterative refinement is also a latency source. Flow matching provides an alternative continuous generative formulation by learning vector fields that transport probability paths~\citep{lipman2023flow}. Rectified flow emphasizes straighter transport paths and faster sampling~\citep{liu2022flow}. Our method follows this fast-generation lineage but uses the improved mean-flow relation to target one-to-few-step action generation.
+
+\paragraph{Vision-language-action models and action chunking.}
+Transformer robot policies such as RT-1 and RT-2 show how large sequence models can map observations and task context to robot actions~\citep{brohan2022rt,brohan2023rt}. OpenVLA studies open VLA modeling at scale~\citep{kim2024openvla}, while compact or efficient VLA models such as SmolVLA and FAST reduce deployment cost through smaller backbones or efficient action tokenization~\citep{shukor2025smolvla,pertsch2025fast}. Action Chunking with Transformers (ACT) predicts chunks of future actions to improve temporal consistency and execution efficiency~\citep{zhao2023learning}. The iMF-AttnRes policy is complementary: it retains chunked execution but changes the generative action sampler so that each chunk can be produced with one to a few evaluations.
+
+
+\paragraph{Residual aggregation and attention residuals.}
+Standard residual networks update $x_t=x_{t-1}+y_t$, so recursively $x_t=y_0+y_1+\cdots+y_t$. Equivalently, the next layer receives a uniform aggregation of all previous residual increments. A natural generalization is
+\begin{equation}
+    y_{t+1}=f_{t+1}\left(\sum_{s=0}^t a_{t+1,s}y_s\right),\qquad a_{t+1,s}\ge0,\quad \sum_{s=0}^t a_{t+1,s}=1.
+\end{equation}
+AttnRes instantiates the weights with an attention distribution over previous residual outputs,
+\begin{equation}
+    a_{t+1,s}\propto\exp\left(w_{t+1}^{\top}\mathrm{RMSNorm}(y_s)\right).
+\end{equation}
+This preserves the residual pathway while allowing each layer to select useful earlier representations instead of uniformly accumulating all of them~\citep{kimi2026attention}. We use this mechanism in the policy transformer; our ablations suggest that applying AttnRes too broadly inside the vision stack is not automatically beneficial.
+
+\section{Method}
+\label{sec:method}
+
+\subsection{Policy formulation}
+\label{sec:policy_formulation}
+
+We consider simulated imitation learning in RoboIMI. At each control step, the policy receives visual observations, robot state, and a task context, and predicts an action chunk of length \texttt{exec}. The main iMF-AttnRes socket and object-transfer policies use ph32/exec16 or related horizon-execution settings. The rollout evaluator records cumulative reward, median reward, mean maximum reward, nonzero-reward episode count, task-specific success-like threshold count, inference FPS, control FPS, and inference latency.
+
+The policy contains three conceptual parts. First, an observation encoder maps visual and state inputs into policy tokens. Second, a transformer policy core uses AttnRes in place of fixed residual accumulation. Third, the action generator uses iMF to predict an average flow over action noise-to-data paths, enabling one-to-few inference evaluations. In the strongest socket variants, the generator uses infer2 or infer3; in the strongest object-transfer run, it uses infer1.
+
+\subsection{Improved Mean Flow action generation}
+\label{sec:imf_action_generation}
+
+Classical flow matching trains an instantaneous velocity field. Let $x_t$ denote a sample at time $t$ and let the instantaneous flow satisfy
+\begin{equation}
+    \frac{d x_t}{dt} = v(x_t,t).
+\end{equation}
+If a policy takes very few integration or denoising steps, using only this local instantaneous velocity can cause large path errors when the learned trajectory is curved. Mean Flow addresses this by training an average velocity over an interval. For two times $r<t$, define
+\begin{equation}
+    \bar{v}(z_t,r,t) = \frac{1}{t-r}\int_r^t v(z_\tau,\tau)d\tau.
+\end{equation}
+The model receives $(z_t,r,t)$ and predicts $u_\theta(z_t,r,t)$, an approximation to $\bar{v}(z_t,r,t)$. At inference, this average velocity is used to move directly across a larger interval, replacing a long sequence of small denoising or ODE steps.
+
+The useful training identity comes from differentiating $(t-r)\bar{v}$ with respect to $t$:
+\begin{equation}
+    \bar{v}(z_t,r,t)=v(z_t,t) - (t-r)\frac{d}{dt}\bar{v}(z_t,r,t).
+\end{equation}
+The total derivative contains the dependence of $\bar{v}$ on the current state and time, and can be written as a Jacobian-vector product,
+\begin{equation}
+    \frac{d}{dt}\bar{v}(z_t,r,t)
+    = \mathrm{jvp}\big(\bar{v},(z,r,t),(v,0,1)\big).
+\end{equation}
+Mean Flow uses this relation to train an average-velocity predictor for one-step generation~\citep{geng2025mean}. In our setting, the target action sample and noise sample provide a conditional straight-path velocity, but the nonlinear JVP expression should conceptually depend on the marginal velocity field. Directly substituting the conditional velocity inside the nonlinear term can destabilize training. We therefore use the improved Mean Flow correction~\citep{geng2025improved}:
+\begin{equation}
+    V_\theta(z_t,r,t) = u_\theta(z_t,r,t) + (t-r)\mathrm{JVP}_{\mathrm{sg}}\big(u_\theta; v_\theta\big).
+\end{equation}
+Here $v_\theta$ is obtained from the same network at the instantaneous case, and the stop-gradient JVP direction prevents the nonlinear term from destabilizing the target. The supervised target remains the conditional straight-path velocity available from the imitation action sample and noise sample, but the learned branch used at deployment is the average-flow predictor $u_\theta$. This is the mechanism that allows infer1, infer2, and infer3 policies to compete with infer100 diffusion-style baselines.
+
+\subsection{Attention Residual policy transformer}
+\label{sec:attnres_policy}
+
+We spell out AttnRes by first rewriting an ordinary residual stack. Let $x_l$ be the hidden state entering layer $l$, and let the layer output increment be
+\begin{equation}
+    y_l = f_l(x_{l-1}), \qquad x_l = x_{l-1}+y_l .
+\end{equation}
+With the convention $y_0=x_0$, recursively expanding the residual updates gives
+\begin{equation}
+    x_l = y_0+y_1+\cdots+y_l = \sum_{s=0}^{l} y_s .
+\end{equation}
+Therefore, the next residual block can be written as
+\begin{equation}
+    y_{l+1}=f_{l+1}(x_l)
+           = f_{l+1}\left(\sum_{s=0}^{l} y_s\right).
+    \label{eq:standard_residual_sum}
+\end{equation}
+This makes the implicit assumption clear: a standard residual network gives every previous increment a fixed coefficient of one before layer $l+1$ computes the next increment. If we normalize the coefficients only to emphasize their relative importance, this is equivalent to uniform depth aggregation,
+\begin{equation}
+    y_{l+1}=f_{l+1}\left((l+1)\sum_{s=0}^{l}\frac{1}{l+1}y_s\right),
+\end{equation}
+so the layer cannot choose which earlier residual features are most useful.
+
+AttnRes replaces this fixed aggregation with learned, layer-dependent weights. Instead of feeding $f_{l+1}$ the unweighted residual sum, we form
+\begin{equation}
+    \tilde{x}_l = \sum_{s=0}^{l} a_{l+1,s} y_s,
+    \qquad
+    a_{l+1,s}\ge 0,
+    \qquad
+    \sum_{s=0}^{l} a_{l+1,s}=1,
+    \label{eq:weighted_residual_sum}
+\end{equation}
+then compute
+\begin{equation}
+    y_{l+1}=f_{l+1}(\tilde{x}_l),
+    \qquad
+    x_{l+1}=x_l+y_{l+1}.
+\end{equation}
+The attention weights are obtained by scoring each previous residual increment with a learned query vector for the current layer:
+\begin{equation}
+    e_{l+1,s}=w_{l+1}^{\top}\mathrm{RMSNorm}(y_s),
+    \qquad
+    a_{l+1,s}=\frac{\exp(e_{l+1,s})}{\sum_{j=0}^{l}\exp(e_{l+1,j})}.
+    \label{eq:attnres_weights}
+\end{equation}
+Equations~\eqref{eq:standard_residual_sum}--\eqref{eq:attnres_weights} show the difference in one line: standard residuals use a fixed sum $\sum_s y_s$, whereas AttnRes uses an attention-weighted sum $\sum_s a_{l+1,s}y_s$ whose coefficients depend on the current layer. This keeps the residual pathway but lets each policy layer select earlier visual-state-action features rather than uniformly accumulating all previous increments. In our object-transfer ablations, the best settings apply AttnRes in the policy transformer; replacing residuals throughout the vision encoder underperforms, suggesting that the placement of learned residual aggregation is important.
+
+\subsection{Inference and control loop}
+\label{sec:inference_loop}
+
+At deployment, the iMF-AttnRes policy samples an action chunk with one to a few average-flow evaluations and then executes the chunk for \texttt{exec} control steps. This contrasts with diffusion-style baselines whose run names include infer100. For socket insertion, the strongest iMF-AttnRes policies use ph32/exec16 and infer2 or infer3. For object transfer, the strongest iMF-AttnRes run uses ph32/exec16 and infer1. The resulting control loop is simple: encode observations, run the AttnRes transformer, evaluate the iMF action generator a small number of times, execute the predicted chunk, and replan on the next observation window.
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-method-overview}}]
+\small Paper Banana prompt: create a clean 16:9 academic system diagram for iMF-AttnRes VLA. Show multi-view images, robot state, and optional language/task conditioning entering a VLA encoder; a policy transformer with AttnRes depth-wise residual aggregation; an improved Mean Flow action generator predicting average flow with one-to-few evaluations; and an exec-length action chunk controlling RoboIMI socket-insert and sim\_transfer robots. Use muted conference-paper colors, clear arrows, no decorative 3D, and labels for iMF, AttnRes, infer1/infer2/infer3, and exec16.
+\end{tcolorbox}
+\caption{Planned method overview figure. The current draft intentionally stores the Paper Banana generation prompt as a placeholder instead of generating an image.}
+\label{fig:method_overview}
+\end{figure}
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-imf-attnres-components}}]
+\small Paper Banana prompt: create a 16:9 technical diagram contrasting classical flow matching instantaneous velocity $dx_t/dt=v(x_t,t)$; Mean Flow average velocity over $[r,t]$ with the JVP correction term; improved Mean Flow training relation $V_\theta(z_t)=u_\theta(z_t)+(t-r)\mathrm{JVP}_{\mathrm{sg}}(u_\theta;v_\theta)$; and AttnRes weighted residual aggregation $a_{t+1,s}$ proportional to $\exp(w_{t+1}\cdot\mathrm{RMSNorm}(y_s))$. Use equation callouts, minimal arrows, and a final callout saying one-to-few-step action generation for robot control.
+\end{tcolorbox}
+\caption{Planned technical component figure. The prompt is retained as a placeholder for later Paper Banana rendering.}
+\label{fig:imf_attnres_components}
+\end{figure}
+
+\section{Experiments}
+\label{sec:experiments}
+
+\subsection{Setup and metrics}
+\label{sec:setup_metrics}
+
+We evaluate in two RoboIMI simulation environments. The socket peg task measures progress toward inserting a peg-like object into a socket. The sim\_transfer task measures object-transfer manipulation. Each reported row uses 100 rollouts. Metrics are copied directly from the rollout logs: average cumulative reward, median reward, mean maximum reward, maximum cumulative reward, count of nonzero-reward episodes, count of success-like episodes, inference FPS, control FPS, and average inference time when available. For socket insertion, the recorded success-like threshold is \texttt{max\_reward > 4}; for sim\_transfer, it is \texttt{max\_reward >= 4}. Hardware differs across runs, so speed comparisons are practical deployment measurements rather than perfectly normalized throughput benchmarks.
+
+\subsection{Socket peg insertion}
+\label{sec:socket_results}
+
+\Cref{tab:socket_results} summarizes the socket peg insertion results. The best iMF-AttnRes policy by average reward is the infer3 model, with average reward 1513.56 and median reward 1901.5. It exceeds both diffusion-style infer100 baselines: the ph16 baseline obtains average reward 861.53 and median reward 668.0, while the ph32/exec16 baseline obtains average reward 1019.39 and median reward 804.0. The latency gap is large. The iMF-AttnRes infer2 and infer3 policies require 14.409 ms and 15.122 ms per inference, while the two infer100 baselines require 338.291 ms and 397.912 ms.
+
+\begin{table*}[t]
+\centering
+\small
+\setlength{\tabcolsep}{3.5pt}
+\caption{Socket peg insertion over 100 rollouts. Success-like uses the recorded \texttt{max\_reward > 4} count.}
+\label{tab:socket_results}
+\begin{tabular}{llrrrrrr}
+\toprule
+Method & Setting & Avg. reward & Median & Avg. max & Nonzero & Success-like & Latency ms \\
+\midrule
+iMF-AttnRes & infer1, ph32, exec16, 50k & 1275.22 & 1490.5 & 3.12 & 84/100 & 0/100 & 16.186 \\
+Diffusion-style VLA & infer100, ph16, 150k & 861.53 & 668.0 & 2.58 & 97/100 & 2/100 & 338.291 \\
+Diffusion-style VLA & infer100, ph32, exec16, 150k & 1019.39 & 804.0 & 2.47 & 92/100 & 4/100 & 397.912 \\
+iMF-AttnRes & infer2, ph32, exec16, 150k & 1472.62 & 1825.0 & 3.29 & 83/100 & 5/100 & 14.409 \\
+iMF-AttnRes & infer3, ph32, exec16, 150k & \textbf{1513.56} & \textbf{1901.5} & 3.28 & 83/100 & 3/100 & 15.122 \\
+ACT & action chunking & 289.60 & 13.0 & 1.29 & 55/100 & 1/100 & 100.212 \\
+SmolVLA & 100k & 466.16 & 106.0 & 1.72 & 89/100 & 0/100 & \textbf{2.614} \\
+\bottomrule
+\end{tabular}
+\end{table*}
+
+The nonzero-reward count reveals an important caveat. The ph16 diffusion-style baseline has 97/100 nonzero episodes, higher than the iMF-AttnRes infer2 and infer3 policies at 83/100. Yet its average and median rewards are much lower. Thus, in this task, nonzero contact or partial progress is not sufficient; the iMF-AttnRes policies more often produce high-reward trajectories when they engage the task successfully. SmolVLA is the fastest method in raw latency and control FPS, but its reward is lower than iMF-AttnRes. ACT underperforms both iMF-AttnRes and the diffusion-style VLA baselines in this setup.
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-socket-reward-latency}}]
+\small Paper Banana prompt: create a 4:3 publication-quality reward-latency comparison for socket peg insertion. Plot methods from the socket table with average reward on the y-axis and average inference time or inverse latency on the x-axis. Highlight iMF-AttnRes infer2/infer3 at 1472.62/1513.56 average reward and 14.409/15.122 ms, diffusion-style infer100 baselines at 861.53/1019.39 average reward and 338.291/397.912 ms, ACT at 289.6 and 100.2116 ms, and SmolVLA at 466.16 and 2.614 ms. Use log-scale latency if helpful and label the speed-quality Pareto frontier.
+\end{tcolorbox}
+\caption{Planned socket reward-latency figure. The current draft contains the Paper Banana prompt placeholder instead of a rendered image.}
+\label{fig:socket_reward_latency}
+\end{figure}
+
+\subsection{Object transfer and ablations}
+\label{sec:sim_transfer_results}
+
+\Cref{tab:sim_transfer_results} reports the object-transfer results. The strongest iMF-AttnRes run reaches average reward 526.22 and 44/100 success-like episodes, exceeding the native diffusion-policy DiT/DDPM/ResNet baseline at average reward 319.2 and 29/100 success-like episodes. This is the clearest sim\_transfer evidence that iMF-AttnRes can improve both reward and success-like threshold count.
+
+\begin{table*}[t]
+\centering
+\small
+\setlength{\tabcolsep}{3.2pt}
+\caption{Object transfer / sim\_transfer over 100 rollouts. Success-like uses the recorded \texttt{max\_reward >= 4} count.}
+\label{tab:sim_transfer_results}
+\begin{tabular}{llrrrrrr}
+\toprule
+Method & Setting & Avg. reward & Median & Avg. max & Nonzero & Success-like & Inf. FPS \\
+\midrule
+Native Diffusion Policy & DiT + DDPM + ResNet & 319.20 & 6.0 & 1.68 & 55/100 & 29/100 & 32.09 \\
+Diffusion-style VLA & emb384, layer18 & 233.52 & 0.0 & 1.12 & 39/100 & 17/100 & 1.859 \\
+iMF multi-token ResNet18 & step34999, ph16, exec08 & 260.66 & 0.0 & 1.12 & 33/100 & 23/100 & \textbf{441.80} \\
+iMF full AttnRes vision & ph16, exec08, 50k & 228.42 & 0.0 & 0.94 & 31/100 & 16/100 & 55.997 \\
+iMF-AttnRes DiT only & ph16, exec16, 50k & 240.64 & 0.0 & 1.02 & 32/100 & 19/100 & 137.996 \\
+iMF-AttnRes DiT only & ph32, exec08, 50k & 163.28 & 0.0 & 0.76 & 26/100 & 12/100 & 85.638 \\
+iMF-AttnRes DiT only & ph32, exec32, 50k & 260.72 & 0.0 & 1.18 & 38/100 & 21/100 & 9.557 \\
+iMF-AttnRes DiT only & ph16, exec08, 50k & 229.56 & 0.0 & 1.18 & 41/100 & 18/100 & 69.984 \\
+iMF-AttnRes DiT only & ph08, exec08, 50k & 237.02 & 0.0 & 1.32 & 48/100 & 18/100 & 69.192 \\
+iMF-AttnRes DiT only & ph32, exec16, 50k & 49.88 & 0.0 & 0.32 & 13/100 & 4/100 & 138.903 \\
+iMF-AttnRes & infer1, ph32, exec16, 50k & \textbf{526.22} & \textbf{86.0} & \textbf{2.14} & \textbf{63/100} & \textbf{44/100} & 8.911 \\
+\bottomrule
+\end{tabular}
+\end{table*}
+
+The ablations are mixed and therefore useful. The ResNet18 multi-token iMF model is extremely fast at 441.80 inference FPS, but its average reward is 260.66, below the native diffusion-policy baseline. The full-AttnRes vision model reaches 228.42 average reward, suggesting that replacing residuals inside the vision encoder is not automatically helpful. Horizon and execution length are also sensitive: the ph32/exec16 DiT-only iMF variant reaches only 49.88 average reward despite high inference FPS. The strongest object-transfer run therefore combines iMF-AttnRes with the right horizon and execution setting rather than showing a universally dominant architectural change.
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-sim-transfer-ablation}}]
+\small Paper Banana prompt: create a 4:3 grouped bar chart for sim\_transfer ablations. Show average reward and success-like episode count for native diffusion policy, best sim-transfer iMF-AttnRes infer1, ResNet18 multi-token iMF, full-AttnRes vision, and selected horizon/execution variants. Emphasize that best iMF-AttnRes reaches 526.22 average reward and 44/100 success-like episodes versus native diffusion policy at 319.2 and 29/100, while several ablations underperform.
+\end{tcolorbox}
+\caption{Planned sim\_transfer ablation figure. The prompt is retained for later Paper Banana rendering.}
+\label{fig:sim_transfer_ablation}
+\end{figure}
+
+\subsection{Derived speed-quality comparisons}
+\label{sec:derived_comparisons}
+
+\Cref{tab:derived_comparisons} lists the derived comparisons used to summarize the tradeoff. On socket insertion, iMF-AttnRes infer3 improves average reward by 1.76$\times$ over the ph16 diffusion-style baseline and by 1.48$\times$ over the ph32 diffusion-style baseline. The same infer3 run improves inference FPS by 22.97$\times$ over the ph16 diffusion-style baseline. The infer2 run is 23.48$\times$ lower latency than the ph16 diffusion-style baseline and 28.45$\times$ higher inference FPS than the ph32 diffusion-style baseline. On object transfer, best iMF-AttnRes improves average reward by 1.65$\times$ and success-like episodes by +15 over native Diffusion Policy.
+
+\begin{table}[t]
+\centering
+\small
+\caption{Derived comparisons from the 100-rollout logs.}
+\label{tab:derived_comparisons}
+\begin{tabular}{llr}
+\toprule
+Comparison & Metric & Result \\
+\midrule
+Socket iMF infer3 vs ph16 diffusion & Avg. reward & 1.76$\times$ \\
+Socket iMF infer3 vs ph32 diffusion & Avg. reward & 1.48$\times$ \\
+Socket iMF infer3 vs ACT & Avg. reward & 5.23$\times$ \\
+Socket iMF infer3 vs SmolVLA & Avg. reward & 3.25$\times$ \\
+Socket iMF infer2 vs ph16 diffusion & Latency reduction & 23.48$\times$ \\
+Socket iMF infer3 vs ph16 diffusion & Inference FPS & 22.97$\times$ \\
+Socket iMF infer2 vs ph32 diffusion & Inference FPS & 28.45$\times$ \\
+Sim transfer iMF vs native diffusion & Avg. reward & 1.65$\times$ \\
+Sim transfer iMF vs native diffusion & Success-like episodes & +15 \\
+ResNet18 iMF vs layer18 baseline & Inference FPS & 237.65$\times$ \\
+\bottomrule
+\end{tabular}
+\end{table}
+
+\section{Limitations}
+\label{sec:limitations}
+
+All experiments are simulation rollouts. The data do not establish real-robot transfer, robustness to sensing changes, or safety under hardware execution. CoRL submissions should provide convincing robotics evidence; the present draft should therefore be viewed as a simulation-first manuscript that still needs real-robot or stronger transfer evidence before formal submission.
+
+The speed measurements are also not perfectly hardware-normalized. Runs were collected on RTX 5880 Ada, L20, and RTX 5090 machines as encoded by their run names. Large latency differences between infer100 diffusion-style policies and infer1--3 iMF-AttnRes policies are meaningful because they reflect algorithmic sampling cost, but exact FPS ratios may include hardware and implementation effects. Future experiments should rerun all main policies on the same GPU, with identical rollout parallelism and identical observation preprocessing.
+
+Finally, iMF-AttnRes is sensitive to design choices. In sim\_transfer, several iMF variants underperform the native diffusion-policy baseline, and full AttnRes in the vision encoder performs worse than more conservative policy-transformer AttnRes. This suggests that average-flow training is not a plug-in guarantee; horizon, execution length, visual tokenization, and residual placement must be tuned carefully.
+
+\section{Conclusion}
+\label{sec:conclusion}
+
+We presented a first simulation study of iMF-AttnRes for fast robot action generation. The method adapts improved Mean Flow to VLA imitation learning and uses Attention Residuals to replace fixed residual accumulation in the policy transformer. In socket peg insertion, iMF-AttnRes achieves higher average and median reward than diffusion-style infer100 baselines while reducing inference latency from hundreds of milliseconds to roughly 15 ms. In object transfer, the best iMF-AttnRes run improves average reward and success-like episode count over the native diffusion-policy baseline. At the same time, ablations show that the method is sensitive to horizon/execution settings and residual placement. The next step is controlled, hardware-normalized evaluation with real-robot transfer.
+
+\clearpage
+\bibliography{refs}
+
+\end{document}
diff --git a/workspace/drafts/refs.bib b/workspace/drafts/refs.bib
new file mode 100644
index 0000000..a892f5b
--- /dev/null
+++ b/workspace/drafts/refs.bib
@@ -0,0 +1,140 @@
+@article{chi2023diffusion,
+  title = {Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
+  author = {Chi, Cheng and Xu, Zhenjia and Feng, Siyuan and Cousineau, Eric and Du, Yilun and Burchfiel, Benjamin and Tedrake, Russ and Song, Shuran},
+  year = {2023},
+  journal = {arXiv preprint arXiv:2303.04137},
+  eprint = {2303.04137},
+  archivePrefix = {arXiv}
+}
+
+@inproceedings{ho2020denoising,
+  title = {Denoising Diffusion Probabilistic Models},
+  author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
+  year = {2020},
+  booktitle = {Advances in Neural Information Processing Systems}
+}
+
+@inproceedings{peebles2023scalable,
+  title = {Scalable Diffusion Models with Transformers},
+  author = {Peebles, William and Xie, Saining},
+  year = {2023},
+  booktitle = {IEEE/CVF International Conference on Computer Vision}
+}
+
+@inproceedings{lipman2023flow,
+  title = {Flow Matching for Generative Modeling},
+  author = {Lipman, Yaron and Chen, Ricky T. Q. and Ben-Hamu, Heli and Nickel, Maximilian and Le, Matt},
+  year = {2023},
+  booktitle = {International Conference on Learning Representations}
+}
+
+@article{liu2022flow,
+  title = {Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow},
+  author = {Liu, Xingchao and Gong, Chengyue and Liu, Qiang},
+  year = {2022},
+  journal = {arXiv preprint arXiv:2209.03003},
+  eprint = {2209.03003},
+  archivePrefix = {arXiv}
+}
+
+@article{geng2025mean,
+  title = {Mean Flows for One-step Generative Modeling},
+  author = {Geng, Zhengyang and Deng, Mingyang and Bai, Xingjian and Kolter, J. Zico and He, Kaiming},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2505.13447},
+  eprint = {2505.13447},
+  archivePrefix = {arXiv}
+}
+
+@article{geng2025improved,
+  title = {Improved Mean Flows: On the Challenges of Fastforward Generative Models},
+  author = {Geng, Zhengyang and Lu, Yiyang and Wu, Zongze and Shechtman, Eli and Kolter, J. Zico and He, Kaiming},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2512.02012},
+  eprint = {2512.02012},
+  archivePrefix = {arXiv}
+}
+
+@article{brohan2022rt,
+  title = {RT-1: Robotics Transformer for Real-World Control at Scale},
+  author = {Brohan, Anthony and Brown, Noah and Carbajal, Justice and Chebotar, Yevgen and Dabis, Joseph and Finn, Chelsea and Gopalakrishnan, Keerthana and Hausman, Karol and Herzog, Alexander and Hsu, Jasmine and others},
+  year = {2022},
+  journal = {arXiv preprint arXiv:2212.06817},
+  eprint = {2212.06817},
+  archivePrefix = {arXiv}
+}
+
+@inproceedings{brohan2023rt,
+  title = {RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control},
+  author = {Brohan, Anthony and Brown, Noah and Carbajal, Justice and Chebotar, Yevgen and Chen, Xi and Choromanski, Krzysztof and Ding, Tianli and Driess, Danny and Dubey, Avinava and Finn, Chelsea and others},
+  year = {2023},
+  booktitle = {Conference on Robot Learning}
+}
+
+@article{kim2024openvla,
+  title = {OpenVLA: An Open-Source Vision-Language-Action Model},
+  author = {Kim, Moo Jin and Pertsch, Karl and Karamcheti, Siddharth and Xiao, Ted and Balakrishna, Ashwin and Nair, Suraj and Rafailov, Rafael and Foster, Ethan and Lam, Grace and Sanketi, Pannag and others},
+  year = {2024},
+  journal = {arXiv preprint arXiv:2406.09246},
+  eprint = {2406.09246},
+  archivePrefix = {arXiv}
+}
+
+@article{shukor2025smolvla,
+  title = {SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},
+  author = {Shukor, Mustafa and Aubakirova, Dana and Capuano, Francesco and Kooijmans, Pepijn and Palma, Steven and Zouitine, Adil and Aractingi, Michel and Pascal, Caroline and Russi, Martino and Marafioti, Andres and others},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2506.01844},
+  eprint = {2506.01844},
+  archivePrefix = {arXiv}
+}
+
+@article{black2024pi0,
+  title = {{$\pi_0$}: A Vision-Language-Action Flow Model for General Robot Control},
+  author = {Black, Kevin and Brown, Noah and Driess, Danny and Esmail, Adnan and Equi, Michael and Finn, Chelsea and Fusai, Niccolo and Groom, Lachy and Hausman, Karol and Ichter, Brian and others},
+  year = {2024},
+  journal = {arXiv preprint arXiv:2410.24164},
+  eprint = {2410.24164},
+  archivePrefix = {arXiv}
+}
+
+@article{pertsch2025fast,
+  title = {FAST: Efficient Action Tokenization for Vision-Language-Action Models},
+  author = {Pertsch, Karl and Stachowicz, Kyle and Ichter, Brian and Driess, Danny and Nair, Suraj and Vuong, Quan and Mees, Oier and Finn, Chelsea and Levine, Sergey},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2501.09747},
+  eprint = {2501.09747},
+  archivePrefix = {arXiv}
+}
+
+@article{zhao2023learning,
+  title = {Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware},
+  author = {Zhao, Tony Z. and Kumar, Vikash and Levine, Sergey and Finn, Chelsea},
+  year = {2023},
+  journal = {arXiv preprint arXiv:2304.13705},
+  eprint = {2304.13705},
+  archivePrefix = {arXiv}
+}
+
+@inproceedings{he2016deep,
+  title = {Deep Residual Learning for Image Recognition},
+  author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
+  year = {2016},
+  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}
+}
+
+@inproceedings{vaswani2017attention,
+  title = {Attention Is All You Need},
+  author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
+  year = {2017},
+  booktitle = {Advances in Neural Information Processing Systems}
+}
+
+@article{kimi2026attention,
+  title = {Attention Residuals},
+  author = {{Kimi Team}},
+  year = {2026},
+  journal = {arXiv preprint arXiv:2603.15031},
+  eprint = {2603.15031},
+  archivePrefix = {arXiv}
+}
diff --git a/workspace/exa_test.json b/workspace/exa_test.json
new file mode 100644
index 0000000..56c0c40
--- /dev/null
+++ b/workspace/exa_test.json
@@ -0,0 +1,54 @@
+{
+  "candidates": [
+    {
+      "title": "Diffusion Policy",
+      "snippet": "# Diffusion Policy: Visuomotor Policy Learning via Action Diffusion\n[...]\nThis paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 12 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details will be publicly available.\n[...]\n[width=0.95]figure/DP_teaser.pdf \\ca",
+      "source_url": "https://arxiv.org/html/2502.12371v1",
+      "discovered_for": [
+        "related_work.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/html/2502.12371v1",
+      "_exa_published_date": "2025-01-01T00:00:00.000Z"
+    },
+    {
+      "title": "Visuomotor policy learning via action diffusion - ACM Digital Library",
+      "snippet": "Diffusion policy: : Visuomotor policy learning via action diffusion: International Journal of Robotics Research: Vol 44, No 10-11 other-periodical;requestedJournal:\n[...]\n:rbrs;wgroup:string:ACM Publication Websites;subPage:string:Basic Abstract;article:article:doi\\:10.\n[...]\n783649241273668;page:string:Article/Chapter View;ctype:string:Journal Content;groupTopic:topic:acm-pubtype>other-periodical;website:website:dl-site;issue:issue:doi\\:10.5555/rbrs.2025.44.issue-10-11;csubtype:string:Periodical;taxonomy:taxonomy:acm-pubtype;pageGroup:string:Publication Pages\"> skip to main content\n\n \n\n \n\nContents\n[...]\nThis paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 15 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the p",
+      "source_url": "https://dl.acm.org/doi/10.1177/02783649241273668",
+      "discovered_for": [
+        "related_work.diffusion_policy"
+      ],
+      "_exa_id": "https://dl.acm.org/doi/10.1177/02783649241273668",
+      "_exa_published_date": null
+    },
+    {
+      "title": "",
+      "snippet": "This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor\n[...]\npolicy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 15 different tasks from 4\n[...]\ndifferent robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning\n[...]\nmethods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score\n[...]\nfunction and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin\n[...]\ndynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including\n[...]\ngracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting\n[...]\nimpressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical\n[...]\nrobots, this paper presents a set of key technical contributions including the incorporation of receding horizon control,\n[...]\nvisual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of\n[...]\npolicy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models.\n[...]\nintroducing a new form of robot visuomotor policy that\n[...]\ngenerates behavior via a “conditional denoising di",
+      "source_url": "https://arxiv.org/pdf/2303.04137v5",
+      "discovered_for": [
+        "related_work.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2303.04137v5",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Visuomotor Policy Learning via Action Diffusion - arXiv",
+      "snippet": "This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 15 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details is available diffusion-policy.cs.columbia.edu\n[...]\nIn this work, we seek to address this challenge by introducing a new form of robot visuomoto",
+      "source_url": "https://arxiv.org/abs/2303.04137",
+      "discovered_for": [
+        "related_work.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2303.04137",
+      "_exa_published_date": "2023-03-07T00:00:00.000Z"
+    },
+    {
+      "title": "3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations",
+      "snippet": "Imitation learning provides an efficient way to teach robots dexterous skills; however, learning complex skills robustly and generalizablely usually consumes large amounts of human demonstrations. To tackle this challenging problem, we present 3D Diffusion Policy (DP3), a novel visual imitation learning approach that incorporates the power of 3D visual representations into diffusion policies, a class of conditional action generative models. The core design of DP3 is the utilization of a compact 3D visual representation, extracted from sparse point clouds with an efficient point encoder. In our experiments involving 72 simulation tasks, DP3 successfully handles most tasks with just 10 demonstrations and surpasses baselines with a 24.2% relative improvement. In 4 real robot tasks, DP3 demonstrates precise control with a high success rate of 85%, given only 40 demonstrations of each task, and shows excellent generalization abilities in diverse aspects, including space, viewpoint, appearance, and instance. Interestingly, in real robot experiments, DP3 rarely violates safety requirements, in contrast to baseline methods which frequently do, necessitating human intervention. Our extensive evaluation highlights the critical importance of 3D representations in real-world robot learning. Videos, code, and data are available on 3d-diffusion-policy.github.io.\n[...]\nTo tackle this challenging problem, we introduce 3D Diffusion Policy (DP3), a simple yet effective visual imitation learnin",
+      "source_url": "https://arxiv.org/html/2403.03954v2",
+      "discovered_for": [
+        "related_work.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/html/2403.03954v2",
+      "_exa_published_date": null
+    }
+  ]
+}
diff --git a/workspace/figures/captions.json b/workspace/figures/captions.json
new file mode 100644
index 0000000..dcf2055
--- /dev/null
+++ b/workspace/figures/captions.json
@@ -0,0 +1,6 @@
+{
+  "fig_method_overview": "Paper Banana prompt placeholder: create a clean 16:9 academic system diagram for iMF-AttnRes VLA. Show multi-view images, robot state, and optional language/task conditioning entering a VLA encoder; a policy transformer with AttnRes depth-wise residual aggregation; an improved Mean Flow action generator predicting average flow with one-to-few evaluations; and an exec-length action chunk controlling RoboIMI socket-insert and sim_transfer robots. Use muted conference-paper colors, clear arrows, no decorative 3D, and labels for iMF, AttnRes, infer1/infer2/infer3, and exec16.",
+  "fig_socket_reward_latency": "Paper Banana prompt placeholder: create a 4:3 publication-quality reward-latency comparison for socket peg insertion. Plot methods from experimental_log Table 1 with avg_reward on the y-axis and avg_inference_time_ms or inverse latency on the x-axis. Highlight iMF-AttnRes infer2/infer3 at 1472.62/1513.56 avg_reward and 14.409/15.122 ms, diffusion-style infer100 baselines at 861.53/1019.39 avg_reward and 338.291/397.912 ms, ACT at 289.6 and 100.2116 ms, and SmolVLA at 466.16 and 2.614 ms. Use log-scale latency if helpful and label the speed-quality Pareto frontier.",
+  "fig_sim_transfer_ablation": "Paper Banana prompt placeholder: create a 4:3 grouped bar chart for sim_transfer ablations from experimental_log Table 2. Show avg_reward and success_like episode count for native diffusion policy, best sim-transfer iMF-AttnRes infer1, ResNet18 multi-token iMF, full-AttnRes vision, and selected horizon/execution variants. Emphasize that best iMF-AttnRes reaches 526.22 avg_reward and 44/100 success-like episodes versus native diffusion policy at 319.2 and 29/100, while several ablations underperform.",
+  "fig_imf_attnres_components": "Paper Banana prompt placeholder: create a 16:9 technical diagram contrasting four mathematical components: classical flow matching instantaneous velocity dx_t/dt=v(x_t,t); Mean Flow average velocity over [r,t] with the JVP correction term; improved Mean Flow training relation V_theta(z_t)=u_theta(z_t)+(t-r)JVP_sg(u_theta;v_theta); and AttnRes weighted residual aggregation a_{t+1,s} proportional to exp(w_{t+1} dot RMSNorm(y_s)). Use equation callouts, minimal arrows, and a final callout saying one-to-few-step action generation for robot control."
+}
diff --git a/workspace/figures/fig_imf_attnres_components.paperbanana_prompt.txt b/workspace/figures/fig_imf_attnres_components.paperbanana_prompt.txt
new file mode 100644
index 0000000..058659f
--- /dev/null
+++ b/workspace/figures/fig_imf_attnres_components.paperbanana_prompt.txt
@@ -0,0 +1 @@
+Paper Banana prompt placeholder: create a 16:9 technical diagram contrasting four mathematical components: classical flow matching instantaneous velocity dx_t/dt=v(x_t,t); Mean Flow average velocity over [r,t] with the JVP correction term; improved Mean Flow training relation V_theta(z_t)=u_theta(z_t)+(t-r)JVP_sg(u_theta;v_theta); and AttnRes weighted residual aggregation a_{t+1,s} proportional to exp(w_{t+1} dot RMSNorm(y_s)). Use equation callouts, minimal arrows, and a final callout saying one-to-few-step action generation for robot control.
diff --git a/workspace/figures/fig_method_overview.paperbanana_prompt.txt b/workspace/figures/fig_method_overview.paperbanana_prompt.txt
new file mode 100644
index 0000000..c4114fc
--- /dev/null
+++ b/workspace/figures/fig_method_overview.paperbanana_prompt.txt
@@ -0,0 +1 @@
+Paper Banana prompt placeholder: create a clean 16:9 academic system diagram for iMF-AttnRes VLA. Show multi-view images, robot state, and optional language/task conditioning entering a VLA encoder; a policy transformer with AttnRes depth-wise residual aggregation; an improved Mean Flow action generator predicting average flow with one-to-few evaluations; and an exec-length action chunk controlling RoboIMI socket-insert and sim_transfer robots. Use muted conference-paper colors, clear arrows, no decorative 3D, and labels for iMF, AttnRes, infer1/infer2/infer3, and exec16.
diff --git a/workspace/figures/fig_sim_transfer_ablation.paperbanana_prompt.txt b/workspace/figures/fig_sim_transfer_ablation.paperbanana_prompt.txt
new file mode 100644
index 0000000..2516c9a
--- /dev/null
+++ b/workspace/figures/fig_sim_transfer_ablation.paperbanana_prompt.txt
@@ -0,0 +1 @@
+Paper Banana prompt placeholder: create a 4:3 grouped bar chart for sim_transfer ablations from experimental_log Table 2. Show avg_reward and success_like episode count for native diffusion policy, best sim-transfer iMF-AttnRes infer1, ResNet18 multi-token iMF, full-AttnRes vision, and selected horizon/execution variants. Emphasize that best iMF-AttnRes reaches 526.22 avg_reward and 44/100 success-like episodes versus native diffusion policy at 319.2 and 29/100, while several ablations underperform.
diff --git a/workspace/figures/fig_socket_reward_latency.paperbanana_prompt.txt b/workspace/figures/fig_socket_reward_latency.paperbanana_prompt.txt
new file mode 100644
index 0000000..a6f9c37
--- /dev/null
+++ b/workspace/figures/fig_socket_reward_latency.paperbanana_prompt.txt
@@ -0,0 +1 @@
+Paper Banana prompt placeholder: create a 4:3 publication-quality reward-latency comparison for socket peg insertion. Plot methods from experimental_log Table 1 with avg_reward on the y-axis and avg_inference_time_ms or inverse latency on the x-axis. Highlight iMF-AttnRes infer2/infer3 at 1472.62/1513.56 avg_reward and 14.409/15.122 ms, diffusion-style infer100 baselines at 861.53/1019.39 avg_reward and 338.291/397.912 ms, ACT at 289.6 and 100.2116 ms, and SmolVLA at 466.16 and 2.614 ms. Use log-scale latency if helpful and label the speed-quality Pareto frontier.
diff --git a/workspace/final.zip b/workspace/final.zip
new file mode 100644
index 0000000..cd2051c
Binary files /dev/null and b/workspace/final.zip differ
diff --git a/workspace/final/corl_2026.sty b/workspace/final/corl_2026.sty
new file mode 100644
index 0000000..1180459
--- /dev/null
+++ b/workspace/final/corl_2026.sty
@@ -0,0 +1,472 @@
+% File: corl_2026.sty 
+%
+% Latex templates for the Conference on Robot Learning (CoRL)
+%
+% This template is heavily inspired by the NeurIPS, ICML, ICLR and IEEE Transactions latex templates.
+% Hence we would like to thank: Roman Garnett and the previous mantainers of the NIPS style, Percy Liang and the previous mantainers of the ICML style, Hugo Larochelle for the ICLR style, and Michael Shell for the IEEE Transactions style.
+%
+% History:
+% 2017/04/16 - First revision by Roberto Calandra (roberto.calandra@berkeley.edu).
+%	       Main changes:
+%	       - The abstract is more compact compared to NeurIPS/ICML
+%	       - References are by default using natbib with squared numbers (e.g., [1])
+%	       - DOI fields from the bibtex are automatically converted to hyperlinks 
+%	       to the corresponding page
+%	       - acknowledgments are now a command, and the corresponding subsubsection is
+%	       automatically included only in the final version
+% 2017/06/12 - Modified to use corlabbrvnat.bst, which order the reference by order of appearance in the paper
+% 2017/06/13 - fixed typo
+% 2018/05/09 - Slightly modified for CoRL 2018 by Jun Morimoto (xmorimo@atr.jp)
+% 2019/01/28 - Slightly modified for CoRL 2019 by Jun Nakanishi (jnakanis@meijo-u.ac.jp)
+% 2020/02/02 - Slightly modified for CoRL 2020 by Cynthia Matuszek (cmat@umbc.edu)
+% 2020/08/19 - Added preprint option by Roberto Calandra (rcalandra@fb.com)
+% 2021/05/06 - Slightly modified for CoRL 2021 by Gerhard Neumann (gerhard.neumann@kit.edu)
+% 2022/03/09 - Slightly modified for CoRL 2022 by Minas Liarokapis (minas.liarokapis@auckland.ac.nz)
+% 2022/03/06 - Slightly modified for CoRL 2023 by Marc Toussaint (toussaint@tu-berlin.de)
+% 2024/03/26 - Slightly modified for CoRL 2024 by David Held (dheld@andrew.cmu.edu)
+% 2026/01/10 - Slightly modified for CoRL 2026 by Yoonchang Sung (yoonchang.sung@ntu.edu.sg)
+%
+% TODO: nohyperref is not working at the moment
+% 
+
+\NeedsTeXFormat{LaTeX2e}
+
+% Content to be changed from year to year
+\ProvidesPackage{corl_2026}[2026/08/15 CORL2026 submission/preprint/camera-ready style file]
+\newcommand{\@conferenceordinal}{10th}
+\newcommand{\@conferenceyear}{2026}
+\newcommand{\@conferencelocation}{Austin TX, USA}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Accepted options: [final,preprint,nonatbib,nohyperref]
+
+% Declare the final option, which creates camera-ready copy
+\newif\if@conferencefinal\@conferencefinalfalse
+\DeclareOption{final}{
+  \@conferencefinaltrue
+}
+
+% Declare the preprint option, which creates a camera-ready copy without the corl footnote
+\newif\if@preprinttype\@preprinttypefalse
+\DeclareOption{preprint}{
+  \@preprinttypetrue
+}
+
+% The natbib package is loaded by default. Declaring the nonatbib option, does not load natbib in case of package clash (users can pass options to natbib via \PassOptionsToPackage)
+\newif\if@natbib\@natbibtrue
+\DeclareOption{nonatbib}{
+  \@natbibfalse
+}
+
+% The hyperref package is loaded by default. Declaring the  nohyperref option, does not load the hyperref.
+\DeclareOption{nohyperref}{%
+  \gdef\nohyperref{1}
+}
+
+% Activate the options
+\ProcessOptions\relax
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Required packages:
+
+\RequirePackage{lineno}
+\RequirePackage{color}
+
+% Load natbib unless told otherwise
+\if@natbib
+  \RequirePackage[square,numbers]{natbib}
+  \bibliographystyle{corlabbrvnat}
+\fi
+
+% set page geometry
+\RequirePackage{hyperref}       % hyperlinks
+\RequirePackage[verbose=true,letterpaper]{geometry}
+\AtBeginDocument{
+  \newgeometry{
+    textheight=9in,
+    textwidth=5.5in,
+    top=1in,
+    headheight=12pt,
+    headsep=25pt,
+    footskip=30pt
+  }
+  \@ifpackageloaded{fullpage}
+    {\PackageWarning{corl_2026}{fullpage package not allowed! Overwriting formatting.}}
+    {}
+}
+
+
+\ifdefined\nohyperref\else\ifdefined\hypersetup
+  \definecolor{mydarkblue}{rgb}{0,0.08,0.45}
+  \hypersetup{ %
+    pdftitle={},
+    pdfauthor={},
+    pdfsubject={Proceedings of the \@conferenceordinal\/ Conference on Robot Learning (CoRL \@conferenceyear)},
+    pdfkeywords={},
+    pdfborder=0 0 0,
+    pdfpagemode=UseNone,
+    colorlinks=true,
+    linkcolor=mydarkblue,
+    citecolor=mydarkblue,
+    filecolor=mydarkblue,
+    urlcolor=mydarkblue,
+    pdfview=FitH}
+
+  \ifdefined\isaccepted \else
+    \hypersetup{pdfauthor={Anonymous Submission}}
+  \fi
+\fi\fi
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% 
+
+% fonts
+\renewcommand{\rmdefault}{ptm}
+\renewcommand{\sfdefault}{phv}
+
+% Create acknowledgments -- only if the option 'final' is activated
+\providecommand{\acknowledgments}{}
+\renewcommand{\acknowledgments}[1]{%
+\if@conferencefinal% 
+\subsubsection*{Acknowledgments} #1
+\fi
+\if@preprinttype% 
+\subsubsection*{Acknowledgments} #1
+\fi
+}
+
+
+% handle tweaks for camera-ready copy vs. submission copy
+\if@conferencefinal
+  \newcommand{\@noticestring}{%
+    \@conferenceordinal\/ Conference on Robot Learning
+    (CoRL \@conferenceyear), \@conferencelocation.%
+  }
+\else
+  \if@preprinttype
+    \newcommand{\@noticestring}{%
+      % Nothing here.
+    }
+  \else
+    \newcommand{\@noticestring}{%
+      Submitted to the \@conferenceordinal\/ Conference on Robot Learning (CoRL \@conferenceyear). Do not distribute.%
+    }
+
+    % line numbers for submission
+    \linenumbers
+
+    % fix incompatibilities between lineno and amsmath, if required, by
+    % transparently wrapping linenomath environments around amsmath
+    % environments
+    \AtBeginDocument{%
+     \@ifpackageloaded{amsmath}{%
+        \newcommand*\patchAmsMathEnvironmentForLineno[1]{%
+          \expandafter\let\csname old#1\expandafter\endcsname\csname #1\endcsname
+          \expandafter\let\csname oldend#1\expandafter\endcsname\csname end#1\endcsname
+          \renewenvironment{#1}%
+            {\linenomath\csname old#1\endcsname}%
+            {\csname oldend#1\endcsname\endlinenomath}%
+        }%
+        \newcommand*\patchBothAmsMathEnvironmentsForLineno[1]{%
+          \patchAmsMathEnvironmentForLineno{#1}%
+          \patchAmsMathEnvironmentForLineno{#1*}%
+        }%
+        \patchBothAmsMathEnvironmentsForLineno{equation}%
+        \patchBothAmsMathEnvironmentsForLineno{align}%
+        \patchBothAmsMathEnvironmentsForLineno{flalign}%
+        \patchBothAmsMathEnvironmentsForLineno{alignat}%
+        \patchBothAmsMathEnvironmentsForLineno{gather}%
+        \patchBothAmsMathEnvironmentsForLineno{multline}%
+      }{}
+    }
+  \fi
+\fi
+
+
+
+% The DOI will now automatically generate a URL link. Pretty cool!
+% + Fix for hyperref and DOI (https://www.tug.org/pipermail/tex-live/2012-August/032161.html)
+%-----------------------------
+\makeatletter
+\providecommand{\doi}[1]{%
+  \begingroup
+    \let\bibinfo\@secondoftwo
+    \urlstyle{rm}%
+    \href{http://dx.doi.org/#1}{%
+      doi:\discretionary{}{}{}%
+      \nolinkurl{#1}%
+    }%
+  \endgroup
+}
+% \makeatother
+% %-----------------------------
+
+\widowpenalty=10000
+\clubpenalty=10000
+\flushbottom
+\sloppy
+
+% font sizes with reduced leading
+\renewcommand{\normalsize}{%
+  \@setfontsize\normalsize\@xpt\@xipt
+  \abovedisplayskip      7\p@ \@plus 2\p@ \@minus 5\p@
+  \abovedisplayshortskip \z@ \@plus 3\p@
+  \belowdisplayskip      \abovedisplayskip
+  \belowdisplayshortskip 4\p@ \@plus 3\p@ \@minus 3\p@
+}
+\normalsize
+\renewcommand{\small}{%
+  \@setfontsize\small\@ixpt\@xpt
+  \abovedisplayskip      6\p@ \@plus 1.5\p@ \@minus 4\p@
+  \abovedisplayshortskip \z@  \@plus 2\p@
+  \belowdisplayskip      \abovedisplayskip
+  \belowdisplayshortskip 3\p@ \@plus 2\p@   \@minus 2\p@
+}
+\renewcommand{\footnotesize}{\@setfontsize\footnotesize\@ixpt\@xpt}
+\renewcommand{\scriptsize}{\@setfontsize\scriptsize\@viipt\@viiipt}
+\renewcommand{\tiny}{\@setfontsize\tiny\@vipt\@viipt}
+\renewcommand{\large}{\@setfontsize\large\@xiipt{14}}
+\renewcommand{\Large}{\@setfontsize\Large\@xivpt{16}}
+\renewcommand{\LARGE}{\@setfontsize\LARGE\@xviipt{20}}
+\renewcommand{\huge}{\@setfontsize\huge\@xxpt{23}}
+\renewcommand{\Huge}{\@setfontsize\Huge\@xxvpt{28}}
+
+% sections with less space
+\providecommand{\section}{}
+\renewcommand{\section}{%
+  \@startsection{section}{1}{\z@}%
+                {-2.0ex \@plus -0.5ex \@minus -0.2ex}%
+                { 1.5ex \@plus  0.3ex \@minus  0.2ex}%
+                {\large\bf\raggedright}%
+}
+\providecommand{\subsection}{}
+\renewcommand{\subsection}{%
+  \@startsection{subsection}{2}{\z@}%
+                {-1.8ex \@plus -0.5ex \@minus -0.2ex}%
+                { 0.8ex \@plus  0.2ex}%
+                {\normalsize\bf\raggedright}%
+}
+\providecommand{\subsubsection}{}
+\renewcommand{\subsubsection}{%
+  \@startsection{subsubsection}{3}{\z@}%
+                {-1.5ex \@plus -0.5ex \@minus -0.2ex}%
+                { 0.5ex \@plus  0.2ex}%
+                {\normalsize\bf\raggedright}%
+}
+\providecommand{\paragraph}{}
+\renewcommand{\paragraph}{%
+  \@startsection{paragraph}{4}{\z@}%
+                {1.5ex \@plus 0.5ex \@minus 0.2ex}%
+                {-1em}%
+                {\normalsize\bf}%
+}
+\providecommand{\subparagraph}{}
+\renewcommand{\subparagraph}{%
+  \@startsection{subparagraph}{5}{\z@}%
+                {1.5ex \@plus 0.5ex \@minus 0.2ex}%
+                {-1em}%
+                {\normalsize\bf}%
+}
+\providecommand{\subsubsubsection}{}
+\renewcommand{\subsubsubsection}{%
+  \vskip5pt{\noindent\normalsize\rm\raggedright}%
+}
+
+% float placement
+\renewcommand{\topfraction      }{0.85}
+\renewcommand{\bottomfraction   }{0.4}
+\renewcommand{\textfraction     }{0.1}
+\renewcommand{\floatpagefraction}{0.7}
+
+\newlength{\@nipsabovecaptionskip}\setlength{\@nipsabovecaptionskip}{7\p@}
+\newlength{\@nipsbelowcaptionskip}\setlength{\@nipsbelowcaptionskip}{\z@}
+
+\setlength{\abovecaptionskip}{\@nipsabovecaptionskip}
+\setlength{\belowcaptionskip}{\@nipsbelowcaptionskip}
+
+% swap above/belowcaptionskip lengths for tables
+\renewenvironment{table}
+  {\setlength{\abovecaptionskip}{\@nipsbelowcaptionskip}%
+   \setlength{\belowcaptionskip}{\@nipsabovecaptionskip}%
+   \@float{table}}
+  {\end@float}
+
+% footnote formatting
+\setlength{\footnotesep }{6.65\p@}
+\setlength{\skip\footins}{9\p@ \@plus 4\p@ \@minus 2\p@}
+\renewcommand{\footnoterule}{\kern-3\p@ \hrule width 12pc \kern 2.6\p@}
+\setcounter{footnote}{0}
+
+% paragraph formatting
+\setlength{\parindent}{\z@}
+\setlength{\parskip  }{5.5\p@}
+
+% list formatting
+\setlength{\topsep       }{4\p@ \@plus 1\p@   \@minus 2\p@}
+\setlength{\partopsep    }{1\p@ \@plus 0.5\p@ \@minus 0.5\p@}
+\setlength{\itemsep      }{2\p@ \@plus 1\p@   \@minus 0.5\p@}
+\setlength{\parsep       }{2\p@ \@plus 1\p@   \@minus 0.5\p@}
+\setlength{\leftmargin   }{3pc}
+\setlength{\leftmargini  }{\leftmargin}
+\setlength{\leftmarginii }{2em}
+\setlength{\leftmarginiii}{1.5em}
+\setlength{\leftmarginiv }{1.0em}
+\setlength{\leftmarginv  }{0.5em}
+\def\@listi  {\leftmargin\leftmargini}
+\def\@listii {\leftmargin\leftmarginii
+              \labelwidth\leftmarginii
+              \advance\labelwidth-\labelsep
+              \topsep  2\p@ \@plus 1\p@    \@minus 0.5\p@
+              \parsep  1\p@ \@plus 0.5\p@ \@minus 0.5\p@
+              \itemsep \parsep}
+\def\@listiii{\leftmargin\leftmarginiii
+              \labelwidth\leftmarginiii
+              \advance\labelwidth-\labelsep
+              \topsep    1\p@ \@plus 0.5\p@ \@minus 0.5\p@
+              \parsep    \z@
+              \partopsep 0.5\p@ \@plus 0\p@ \@minus 0.5\p@
+              \itemsep \topsep}
+\def\@listiv {\leftmargin\leftmarginiv
+              \labelwidth\leftmarginiv
+              \advance\labelwidth-\labelsep}
+\def\@listv  {\leftmargin\leftmarginv
+              \labelwidth\leftmarginv
+              \advance\labelwidth-\labelsep}
+\def\@listvi {\leftmargin\leftmarginvi
+              \labelwidth\leftmarginvi
+              \advance\labelwidth-\labelsep}
+
+% create title
+\providecommand{\maketitle}{}
+\renewcommand{\maketitle}{%
+  \par
+  \begingroup
+    \renewcommand{\thefootnote}{\fnsymbol{footnote}}
+    % for perfect author name centering
+    \renewcommand{\@makefnmark}{\hbox to \z@{$^{\@thefnmark}$\hss}}
+    % The footnote-mark was overlapping the footnote-text,
+    % added the following to fix this problem               (MK)
+    \long\def\@makefntext##1{%
+      \parindent 1em\noindent
+      \hbox to 1.8em{\hss $\m@th ^{\@thefnmark}$}##1
+    }
+    \thispagestyle{empty}
+    \@maketitle
+    \@thanks
+    \@notice
+  \endgroup
+  \let\maketitle\relax
+  \let\thanks\relax
+}
+
+% rules for title box at top of first page
+\newcommand{\@toptitlebar}{
+  \hrule height 4\p@
+  \vskip 0.25in
+  \vskip -\parskip%
+}
+\newcommand{\@bottomtitlebar}{
+  \vskip 0.29in
+  \vskip -\parskip
+  \hrule height 1\p@
+  \vskip 0.09in%
+}
+
+%% keywords as first class citizens
+\def\keywords#1{%
+%  \ifdefined\isaccepted \else
+%    \par {\bf Keywords:} #1%
+%  \fi
+%  \ifdefined\nohyperref\else\ifdefined\hypersetup
+%    \hypersetup{pdfkeywords={#1}}
+%  \fi\fi
+ \ifdefined\isaccepted \else
+    \begin{quote}
+    \textbf{Keywords:} #1%
+    \end{quote}
+ \fi
+  \ifdefined\nohyperref\else\ifdefined\hypersetup
+    \hypersetup{pdfkeywords={#1}}
+  \fi\fi
+}
+
+
+% create title (includes both anonymized and non-anonymized versions)
+\providecommand{\@maketitle}{}
+\renewcommand{\@maketitle}{%
+  \vbox{%
+    \hsize\textwidth
+    \linewidth\hsize
+    \vskip 0.1in
+%     \@toptitlebar
+    \centering
+    {\LARGE\bf \@title\par}
+%     \@bottomtitlebar
+    \if@conferencefinal
+      \def\And{%
+        \end{tabular}\hfil\linebreak[0]\hfil%
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+      }
+      \def\AND{%
+        \end{tabular}\hfil\linebreak[4]\hfil%
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+      }
+      \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\@author\end{tabular}%
+    \else
+      \if@preprinttype
+        \def\And{%
+          \end{tabular}\hfil\linebreak[0]\hfil%
+          \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+        }
+        \def\AND{%
+          \end{tabular}\hfil\linebreak[4]\hfil%
+          \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+        }
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\@author\end{tabular}%
+      \else
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}
+          Anonymous Author(s) \\
+          Affiliation \\
+          Address \\
+          \texttt{email} \\
+        \end{tabular}%
+      \fi
+    \fi
+    \vskip 0.3in \@minus 0.1in
+  }
+}
+
+% add conference notice to bottom of first page
+\newcommand{\ftype@noticebox}{8}
+\newcommand{\@notice}{%
+  % give a bit of extra room back to authors on first page
+  \enlargethispage{2\baselineskip}%
+  \@float{noticebox}[b]%
+    \footnotesize\@noticestring%
+  \end@float%
+}
+
+% abstract styling
+\renewenvironment{abstract}%
+{%
+%   \vskip 0.075in%
+%   \centerline%
+%   {\large\bf Abstract}%
+%   \vspace{0.5ex}%
+  \begin{quote}%
+  \textbf{Abstract:}%
+}
+{
+  \par%
+  \vskip 1ex%
+%   \ifdefined\keywords
+%   \textbf{Keywords:}%
+%   \@keywords%
+%   \else
+%   \fi
+  \end{quote}%
+}
+
+\endinput
diff --git a/workspace/final/corlabbrvnat.bst b/workspace/final/corlabbrvnat.bst
new file mode 100644
index 0000000..eab7c3e
--- /dev/null
+++ b/workspace/final/corlabbrvnat.bst
@@ -0,0 +1,1442 @@
+%% File: `corlabbrvnat.bst'
+%% A modification of `abbrvnat.bst' to order the references by the order
+%% of appearance in the paper.
+%%
+%% Modified by Roberto Calandra 12/06/2017
+%% email: roberto.calandra@berkeley.edu
+%%
+%% -------------------
+%%
+%% Copyright 1993-2007 Patrick W Daly
+%% Max-Planck-Institut f\"ur Sonnensystemforschung
+%% Max-Planck-Str. 2
+%% D-37191 Katlenburg-Lindau
+%% Germany
+%% E-mail: daly@mps.mpg.de
+%%
+%% This program can be redistributed and/or modified under the terms
+%% of the LaTeX Project Public License Distributed from CTAN
+%% archives in directory macros/latex/base/lppl.txt; either
+%% version 1 of the License, or any later version.
+%%
+ % Version and source file information:
+ % \ProvidesFile{natbst.mbs}[2007/11/26 1.93 (PWD)]
+ %
+ % BibTeX `plainnat' family
+ %   version 0.99b for BibTeX versions 0.99a or later,
+ %   for LaTeX versions 2.09 and 2e.
+ %
+ % For use with the `natbib.sty' package; emulates the corresponding
+ %   member of the `plain' family, but with author-year citations.
+ %
+ % With version 6.0 of `natbib.sty', it may also be used for numerical
+ %   citations, while retaining the commands \citeauthor, \citefullauthor,
+ %   and \citeyear to print the corresponding information.
+ %
+ % For version 7.0 of `natbib.sty', the KEY field replaces missing
+ %   authors/editors, and the date is left blank in \bibitem.
+ %
+ % Includes field EID for the sequence/citation number of electronic journals
+ %  which is used instead of page numbers.
+ %
+ % Includes fields ISBN and ISSN.
+ %
+ % Includes field URL for Internet addresses.
+ %
+ % Includes field DOI for Digital Object Idenfifiers.
+ %
+ % Works best with the url.sty package of Donald Arseneau.
+ %
+ % Works with identical authors and year are further sorted by
+ %   citation key, to preserve any natural sequence.
+ %
+ENTRY
+  { address
+    author
+    booktitle
+    chapter
+    doi
+    eid
+    edition
+    editor
+    howpublished
+    institution
+    isbn
+    issn
+    journal
+    key
+    month
+    note
+    number
+    organization
+    pages
+    publisher
+    school
+    series
+    title
+    type
+    url
+    volume
+    year
+  }
+  {}
+  { label extra.label sort.label short.list }
+
+INTEGERS { output.state before.all mid.sentence after.sentence after.block }
+
+FUNCTION {init.state.consts}
+{ #0 'before.all :=
+  #1 'mid.sentence :=
+  #2 'after.sentence :=
+  #3 'after.block :=
+}
+
+STRINGS { s t }
+
+FUNCTION {output.nonnull}
+{ 's :=
+  output.state mid.sentence =
+    { ", " * write$ }
+    { output.state after.block =
+        { add.period$ write$
+          newline$
+          "\newblock " write$
+        }
+        { output.state before.all =
+            'write$
+            { add.period$ " " * write$ }
+          if$
+        }
+      if$
+      mid.sentence 'output.state :=
+    }
+  if$
+  s
+}
+
+FUNCTION {output}
+{ duplicate$ empty$
+    'pop$
+    'output.nonnull
+  if$
+}
+
+FUNCTION {output.check}
+{ 't :=
+  duplicate$ empty$
+    { pop$ "empty " t * " in " * cite$ * warning$ }
+    'output.nonnull
+  if$
+}
+
+FUNCTION {fin.entry}
+{ add.period$
+  write$
+  newline$
+}
+
+FUNCTION {new.block}
+{ output.state before.all =
+    'skip$
+    { after.block 'output.state := }
+  if$
+}
+
+FUNCTION {new.sentence}
+{ output.state after.block =
+    'skip$
+    { output.state before.all =
+        'skip$
+        { after.sentence 'output.state := }
+      if$
+    }
+  if$
+}
+
+FUNCTION {not}
+{   { #0 }
+    { #1 }
+  if$
+}
+
+FUNCTION {and}
+{   'skip$
+    { pop$ #0 }
+  if$
+}
+
+FUNCTION {or}
+{   { pop$ #1 }
+    'skip$
+  if$
+}
+
+FUNCTION {new.block.checka}
+{ empty$
+    'skip$
+    'new.block
+  if$
+}
+
+FUNCTION {new.block.checkb}
+{ empty$
+  swap$ empty$
+  and
+    'skip$
+    'new.block
+  if$
+}
+
+FUNCTION {new.sentence.checka}
+{ empty$
+    'skip$
+    'new.sentence
+  if$
+}
+
+FUNCTION {new.sentence.checkb}
+{ empty$
+  swap$ empty$
+  and
+    'skip$
+    'new.sentence
+  if$
+}
+
+FUNCTION {field.or.null}
+{ duplicate$ empty$
+    { pop$ "" }
+    'skip$
+  if$
+}
+
+FUNCTION {emphasize}
+{ duplicate$ empty$
+    { pop$ "" }
+    { "\emph{" swap$ * "}" * }
+  if$
+}
+
+INTEGERS { nameptr namesleft numnames }
+
+FUNCTION {format.names}
+{ 's :=
+  #1 'nameptr :=
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    { s nameptr "{f.~}{vv~}{ll}{, jj}" format.name$ 't :=
+      nameptr #1 >
+        { namesleft #1 >
+            { ", " * t * }
+            { numnames #2 >
+                { "," * }
+                'skip$
+              if$
+              t "others" =
+                { " et~al." * }
+                { " and " * t * }
+              if$
+            }
+          if$
+        }
+        't
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {format.key}
+{ empty$
+    { key field.or.null }
+    { "" }
+  if$
+}
+
+FUNCTION {format.authors}
+{ author empty$
+    { "" }
+    { author format.names }
+  if$
+}
+
+FUNCTION {format.editors}
+{ editor empty$
+    { "" }
+    { editor format.names
+      editor num.names$ #1 >
+        { ", editors" * }
+        { ", editor" * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.isbn}
+{ isbn empty$
+    { "" }
+    { new.block "ISBN " isbn * }
+  if$
+}
+
+FUNCTION {format.issn}
+{ issn empty$
+    { "" }
+    { new.block "ISSN " issn * }
+  if$
+}
+
+FUNCTION {format.url}
+{ url empty$
+    { "" }
+    { new.block "URL \url{" url * "}" * }
+  if$
+}
+
+FUNCTION {format.doi}
+{ doi empty$
+    { "" }
+    { new.block "\doi{" doi * "}" * }
+  if$
+}
+
+FUNCTION {format.title}
+{ title empty$
+    { "" }
+    { title "t" change.case$ }
+  if$
+}
+
+FUNCTION {format.full.names}
+{'s :=
+  #1 'nameptr :=
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    { s nameptr
+      "{vv~}{ll}" format.name$ 't :=
+      nameptr #1 >
+        {
+          namesleft #1 >
+            { ", " * t * }
+            {
+              numnames #2 >
+                { "," * }
+                'skip$
+              if$
+              t "others" =
+                { " et~al." * }
+                { " and " * t * }
+              if$
+            }
+          if$
+        }
+        't
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {author.editor.full}
+{ author empty$
+    { editor empty$
+        { "" }
+        { editor format.full.names }
+      if$
+    }
+    { author format.full.names }
+  if$
+}
+
+FUNCTION {author.full}
+{ author empty$
+    { "" }
+    { author format.full.names }
+  if$
+}
+
+FUNCTION {editor.full}
+{ editor empty$
+    { "" }
+    { editor format.full.names }
+  if$
+}
+
+FUNCTION {make.full.names}
+{ type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.full
+    { type$ "proceedings" =
+        'editor.full
+        'author.full
+      if$
+    }
+  if$
+}
+
+FUNCTION {output.bibitem}
+{ newline$
+  "\bibitem[" write$
+  label write$
+  ")" make.full.names duplicate$ short.list =
+     { pop$ }
+     { * }
+   if$
+  "]{" * write$
+  cite$ write$
+  "}" write$
+  newline$
+  ""
+  before.all 'output.state :=
+}
+
+FUNCTION {n.dashify}
+{ 't :=
+  ""
+    { t empty$ not }
+    { t #1 #1 substring$ "-" =
+        { t #1 #2 substring$ "--" = not
+            { "--" *
+              t #2 global.max$ substring$ 't :=
+            }
+            {   { t #1 #1 substring$ "-" = }
+                { "-" *
+                  t #2 global.max$ substring$ 't :=
+                }
+              while$
+            }
+          if$
+        }
+        { t #1 #1 substring$ *
+          t #2 global.max$ substring$ 't :=
+        }
+      if$
+    }
+  while$
+}
+
+FUNCTION {format.date}
+{ year duplicate$ empty$
+    { "empty year in " cite$ * warning$
+       pop$ "" }
+    'skip$
+  if$
+  month empty$
+    'skip$
+    { month
+      " " * swap$ *
+    }
+  if$
+  extra.label *
+}
+
+FUNCTION {format.btitle}
+{ title emphasize
+}
+
+FUNCTION {tie.or.space.connect}
+{ duplicate$ text.length$ #3 <
+    { "~" }
+    { " " }
+  if$
+  swap$ * *
+}
+
+FUNCTION {either.or.check}
+{ empty$
+    'pop$
+    { "can't use both " swap$ * " fields in " * cite$ * warning$ }
+  if$
+}
+
+FUNCTION {format.bvolume}
+{ volume empty$
+    { "" }
+    { "volume" volume tie.or.space.connect
+      series empty$
+        'skip$
+        { " of " * series emphasize * }
+      if$
+      "volume and number" number either.or.check
+    }
+  if$
+}
+
+FUNCTION {format.number.series}
+{ volume empty$
+    { number empty$
+        { series field.or.null }
+        { output.state mid.sentence =
+            { "number" }
+            { "Number" }
+          if$
+          number tie.or.space.connect
+          series empty$
+            { "there's a number but no series in " cite$ * warning$ }
+            { " in " * series * }
+          if$
+        }
+      if$
+    }
+    { "" }
+  if$
+}
+
+FUNCTION {format.edition}
+{ edition empty$
+    { "" }
+    { output.state mid.sentence =
+        { edition "l" change.case$ " edition" * }
+        { edition "t" change.case$ " edition" * }
+      if$
+    }
+  if$
+}
+
+INTEGERS { multiresult }
+
+FUNCTION {multi.page.check}
+{ 't :=
+  #0 'multiresult :=
+    { multiresult not
+      t empty$ not
+      and
+    }
+    { t #1 #1 substring$
+      duplicate$ "-" =
+      swap$ duplicate$ "," =
+      swap$ "+" =
+      or or
+        { #1 'multiresult := }
+        { t #2 global.max$ substring$ 't := }
+      if$
+    }
+  while$
+  multiresult
+}
+
+FUNCTION {format.pages}
+{ pages empty$
+    { "" }
+    { pages multi.page.check
+        { "pages" pages n.dashify tie.or.space.connect }
+        { "page" pages tie.or.space.connect }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.eid}
+{ eid empty$
+    { "" }
+    { "art." eid tie.or.space.connect }
+  if$
+}
+
+FUNCTION {format.vol.num.pages}
+{ volume field.or.null
+  number empty$
+    'skip$
+    { "\penalty0 (" number * ")" * *
+      volume empty$
+        { "there's a number but no volume in " cite$ * warning$ }
+        'skip$
+      if$
+    }
+  if$
+  pages empty$
+    'skip$
+    { duplicate$ empty$
+        { pop$ format.pages }
+        { ":\penalty0 " * pages n.dashify * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.vol.num.eid}
+{ volume field.or.null
+  number empty$
+    'skip$
+    { "\penalty0 (" number * ")" * *
+      volume empty$
+        { "there's a number but no volume in " cite$ * warning$ }
+        'skip$
+      if$
+    }
+  if$
+  eid empty$
+    'skip$
+    { duplicate$ empty$
+        { pop$ format.eid }
+        { ":\penalty0 " * eid * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.chapter.pages}
+{ chapter empty$
+    'format.pages
+    { type empty$
+        { "chapter" }
+        { type "l" change.case$ }
+      if$
+      chapter tie.or.space.connect
+      pages empty$
+        'skip$
+        { ", " * format.pages * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.in.ed.booktitle}
+{ booktitle empty$
+    { "" }
+    { editor empty$
+        { "In " booktitle emphasize * }
+        { "In " format.editors * ", " * booktitle emphasize * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {empty.misc.check}
+{ author empty$ title empty$ howpublished empty$
+  month empty$ year empty$ note empty$
+  and and and and and
+  key empty$ not and
+    { "all relevant fields are empty in " cite$ * warning$ }
+    'skip$
+  if$
+}
+
+FUNCTION {format.thesis.type}
+{ type empty$
+    'skip$
+    { pop$
+      type "t" change.case$
+    }
+  if$
+}
+
+FUNCTION {format.tr.number}
+{ type empty$
+    { "Technical Report" }
+    'type
+  if$
+  number empty$
+    { "t" change.case$ }
+    { number tie.or.space.connect }
+  if$
+}
+
+FUNCTION {format.article.crossref}
+{ key empty$
+    { journal empty$
+        { "need key or journal for " cite$ * " to crossref " * crossref *
+          warning$
+          ""
+        }
+        { "In \emph{" journal * "}" * }
+      if$
+    }
+    { "In " }
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {format.book.crossref}
+{ volume empty$
+    { "empty volume in " cite$ * "'s crossref of " * crossref * warning$
+      "In "
+    }
+    { "Volume" volume tie.or.space.connect
+      " of " *
+    }
+  if$
+  editor empty$
+  editor field.or.null author field.or.null =
+  or
+    { key empty$
+        { series empty$
+            { "need editor, key, or series for " cite$ * " to crossref " *
+              crossref * warning$
+              "" *
+            }
+            { "\emph{" * series * "}" * }
+          if$
+        }
+        'skip$
+      if$
+    }
+    'skip$
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {format.incoll.inproc.crossref}
+{ editor empty$
+  editor field.or.null author field.or.null =
+  or
+    { key empty$
+        { booktitle empty$
+            { "need editor, key, or booktitle for " cite$ * " to crossref " *
+              crossref * warning$
+              ""
+            }
+            { "In \emph{" booktitle * "}" * }
+          if$
+        }
+        { "In " }
+      if$
+    }
+    { "In " }
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {article}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { journal emphasize "journal" output.check
+      eid empty$
+        { format.vol.num.pages output }
+        { format.vol.num.eid output }
+      if$
+      format.date "year" output.check
+    }
+    { format.article.crossref output.nonnull
+      eid empty$
+        { format.pages output }
+        { format.eid output }
+      if$
+    }
+  if$
+  format.issn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {book}
+{ output.bibitem
+  author empty$
+    { format.editors "author and editor" output.check
+      editor format.key output
+    }
+    { format.authors output.nonnull
+      crossref missing$
+        { "author and editor" editor either.or.check }
+        'skip$
+      if$
+    }
+  if$
+  new.block
+  format.btitle "title" output.check
+  crossref missing$
+    { format.bvolume output
+      new.block
+      format.number.series output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+    }
+    { new.block
+      format.book.crossref output.nonnull
+    }
+  if$
+  format.edition output
+  format.date "year" output.check
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {booklet}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  new.block
+  format.title "title" output.check
+  howpublished address new.block.checkb
+  howpublished output
+  address output
+  format.date output
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {inbook}
+{ output.bibitem
+  author empty$
+    { format.editors "author and editor" output.check
+      editor format.key output
+    }
+    { format.authors output.nonnull
+      crossref missing$
+        { "author and editor" editor either.or.check }
+        'skip$
+      if$
+    }
+  if$
+  new.block
+  format.btitle "title" output.check
+  crossref missing$
+    { format.bvolume output
+      format.chapter.pages "chapter and pages" output.check
+      new.block
+      format.number.series output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+    }
+    { format.chapter.pages "chapter and pages" output.check
+      new.block
+      format.book.crossref output.nonnull
+    }
+  if$
+  format.edition output
+  format.date "year" output.check
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {incollection}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { format.in.ed.booktitle "booktitle" output.check
+      format.bvolume output
+      format.number.series output
+      format.chapter.pages output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+      format.edition output
+      format.date "year" output.check
+    }
+    { format.incoll.inproc.crossref output.nonnull
+      format.chapter.pages output
+    }
+  if$
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {inproceedings}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { format.in.ed.booktitle "booktitle" output.check
+      format.bvolume output
+      format.number.series output
+      format.pages output
+      address empty$
+        { organization publisher new.sentence.checkb
+          organization output
+          publisher output
+          format.date "year" output.check
+        }
+        { address output.nonnull
+          format.date "year" output.check
+          new.sentence
+          organization output
+          publisher output
+        }
+      if$
+    }
+    { format.incoll.inproc.crossref output.nonnull
+      format.pages output
+    }
+  if$
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {conference} { inproceedings }
+
+FUNCTION {manual}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  new.block
+  format.btitle "title" output.check
+  organization address new.block.checkb
+  organization output
+  address output
+  format.edition output
+  format.date output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {mastersthesis}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  "Master's thesis" format.thesis.type output.nonnull
+  school "school" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {misc}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  title howpublished new.block.checkb
+  format.title output
+  howpublished new.block.checka
+  howpublished output
+  format.date output
+  format.issn output
+  format.url output
+  new.block
+  note output
+  fin.entry
+  empty.misc.check
+}
+
+FUNCTION {phdthesis}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.btitle "title" output.check
+  new.block
+  "PhD thesis" format.thesis.type output.nonnull
+  school "school" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {proceedings}
+{ output.bibitem
+  format.editors output
+  editor format.key output
+  new.block
+  format.btitle "title" output.check
+  format.bvolume output
+  format.number.series output
+  address output
+  format.date "year" output.check
+  new.sentence
+  organization output
+  publisher output
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {techreport}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  format.tr.number output.nonnull
+  institution "institution" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {unpublished}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  note "note" output.check
+  format.date output
+  format.url output
+  fin.entry
+}
+
+FUNCTION {default.type} { misc }
+
+
+MACRO {jan} {"Jan."}
+
+MACRO {feb} {"Feb."}
+
+MACRO {mar} {"Mar."}
+
+MACRO {apr} {"Apr."}
+
+MACRO {may} {"May"}
+
+MACRO {jun} {"June"}
+
+MACRO {jul} {"July"}
+
+MACRO {aug} {"Aug."}
+
+MACRO {sep} {"Sept."}
+
+MACRO {oct} {"Oct."}
+
+MACRO {nov} {"Nov."}
+
+MACRO {dec} {"Dec."}
+
+
+
+MACRO {acmcs} {"ACM Comput. Surv."}
+
+MACRO {acta} {"Acta Inf."}
+
+MACRO {cacm} {"Commun. ACM"}
+
+MACRO {ibmjrd} {"IBM J. Res. Dev."}
+
+MACRO {ibmsj} {"IBM Syst.~J."}
+
+MACRO {ieeese} {"IEEE Trans. Softw. Eng."}
+
+MACRO {ieeetc} {"IEEE Trans. Comput."}
+
+MACRO {ieeetcad}
+ {"IEEE Trans. Comput.-Aided Design Integrated Circuits"}
+
+MACRO {ipl} {"Inf. Process. Lett."}
+
+MACRO {jacm} {"J.~ACM"}
+
+MACRO {jcss} {"J.~Comput. Syst. Sci."}
+
+MACRO {scp} {"Sci. Comput. Programming"}
+
+MACRO {sicomp} {"SIAM J. Comput."}
+
+MACRO {tocs} {"ACM Trans. Comput. Syst."}
+
+MACRO {tods} {"ACM Trans. Database Syst."}
+
+MACRO {tog} {"ACM Trans. Gr."}
+
+MACRO {toms} {"ACM Trans. Math. Softw."}
+
+MACRO {toois} {"ACM Trans. Office Inf. Syst."}
+
+MACRO {toplas} {"ACM Trans. Prog. Lang. Syst."}
+
+MACRO {tcs} {"Theoretical Comput. Sci."}
+
+
+READ
+
+FUNCTION {sortify}
+{ purify$
+  "l" change.case$
+}
+
+INTEGERS { len }
+
+FUNCTION {chop.word}
+{ 's :=
+  'len :=
+  s #1 len substring$ =
+    { s len #1 + global.max$ substring$ }
+    's
+  if$
+}
+
+FUNCTION {format.lab.names}
+{ 's :=
+  s #1 "{vv~}{ll}" format.name$
+  s num.names$ duplicate$
+  #2 >
+    { pop$ " et~al." * }
+    { #2 <
+        'skip$
+        { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" =
+            { " et~al." * }
+            { " and " * s #2 "{vv~}{ll}" format.name$ * }
+          if$
+        }
+      if$
+    }
+  if$
+}
+
+FUNCTION {author.key.label}
+{ author empty$
+    { key empty$
+        { cite$ #1 #3 substring$ }
+        'key
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {author.editor.key.label}
+{ author empty$
+    { editor empty$
+        { key empty$
+            { cite$ #1 #3 substring$ }
+            'key
+          if$
+        }
+        { editor format.lab.names }
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {author.key.organization.label}
+{ author empty$
+    { key empty$
+        { organization empty$
+            { cite$ #1 #3 substring$ }
+            { "The " #4 organization chop.word #3 text.prefix$ }
+          if$
+        }
+        'key
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {editor.key.organization.label}
+{ editor empty$
+    { key empty$
+        { organization empty$
+            { cite$ #1 #3 substring$ }
+            { "The " #4 organization chop.word #3 text.prefix$ }
+          if$
+        }
+        'key
+      if$
+    }
+    { editor format.lab.names }
+  if$
+}
+
+FUNCTION {calc.short.authors}
+{ type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.key.label
+    { type$ "proceedings" =
+        'editor.key.organization.label
+        { type$ "manual" =
+            'author.key.organization.label
+            'author.key.label
+          if$
+        }
+      if$
+    }
+  if$
+  'short.list :=
+}
+
+FUNCTION {calc.label}
+{ calc.short.authors
+  short.list
+  "("
+  *
+  year duplicate$ empty$
+  short.list key field.or.null = or
+     { pop$ "" }
+     'skip$
+  if$
+  *
+  'label :=
+}
+
+FUNCTION {sort.format.names}
+{ 's :=
+  #1 'nameptr :=
+  ""
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    {
+      s nameptr "{vv{ } }{ll{ }}{  f{ }}{  jj{ }}" format.name$ 't :=
+      nameptr #1 >
+        {
+          "   "  *
+          namesleft #1 = t "others" = and
+            { "zzzzz" * }
+            { numnames #2 > nameptr #2 = and
+                { "zz" * year field.or.null * "   " * }
+                'skip$
+              if$
+              t sortify *
+            }
+          if$
+        }
+        { t sortify * }
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {sort.format.title}
+{ 't :=
+  "A " #2
+    "An " #3
+      "The " #4 t chop.word
+    chop.word
+  chop.word
+  sortify
+  #1 global.max$ substring$
+}
+
+FUNCTION {author.sort}
+{ author empty$
+    { key empty$
+        { "to sort, need author or key in " cite$ * warning$
+          ""
+        }
+        { key sortify }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {author.editor.sort}
+{ author empty$
+    { editor empty$
+        { key empty$
+            { "to sort, need author, editor, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { editor sort.format.names }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {author.organization.sort}
+{ author empty$
+    { organization empty$
+        { key empty$
+            { "to sort, need author, organization, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { "The " #4 organization chop.word sortify }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {editor.organization.sort}
+{ editor empty$
+    { organization empty$
+        { key empty$
+            { "to sort, need editor, organization, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { "The " #4 organization chop.word sortify }
+      if$
+    }
+    { editor sort.format.names }
+  if$
+}
+
+
+FUNCTION {presort}
+{ calc.label
+  label sortify
+  "    "
+  *
+  type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.sort
+    { type$ "proceedings" =
+        'editor.organization.sort
+        { type$ "manual" =
+            'author.organization.sort
+            'author.sort
+          if$
+        }
+      if$
+    }
+  if$
+  "    "
+  *
+  year field.or.null sortify
+  *
+  "    "
+  *
+  cite$
+  *
+  #1 entry.max$ substring$
+  'sort.label :=
+  sort.label *
+  #1 entry.max$ substring$
+  'sort.key$ :=
+}
+
+ITERATE {presort}
+
+% SORT
+
+STRINGS { longest.label last.label next.extra }
+
+INTEGERS { longest.label.width last.extra.num number.label }
+
+FUNCTION {initialize.longest.label}
+{ "" 'longest.label :=
+  #0 int.to.chr$ 'last.label :=
+  "" 'next.extra :=
+  #0 'longest.label.width :=
+  #0 'last.extra.num :=
+  #0 'number.label :=
+}
+
+FUNCTION {forward.pass}
+{ last.label label =
+    { last.extra.num #1 + 'last.extra.num :=
+      last.extra.num int.to.chr$ 'extra.label :=
+    }
+    { "a" chr.to.int$ 'last.extra.num :=
+      "" 'extra.label :=
+      label 'last.label :=
+    }
+  if$
+  number.label #1 + 'number.label :=
+}
+
+FUNCTION {reverse.pass}
+{ next.extra "b" =
+    { "a" 'extra.label := }
+    'skip$
+  if$
+  extra.label 'next.extra :=
+  extra.label
+  duplicate$ empty$
+    'skip$
+    { "{\natexlab{" swap$ * "}}" * }
+  if$
+  'extra.label :=
+  label extra.label * 'label :=
+}
+
+EXECUTE {initialize.longest.label}
+
+ITERATE {forward.pass}
+
+REVERSE {reverse.pass}
+
+FUNCTION {bib.sort.order}
+{ sort.label  'sort.key$ :=
+}
+
+ITERATE {bib.sort.order}
+
+% SORT
+
+FUNCTION {begin.bib}
+{   preamble$ empty$
+    'skip$
+    { preamble$ write$ newline$ }
+  if$
+  "\begin{thebibliography}{" number.label int.to.str$ * "}" *
+  write$ newline$
+  "\providecommand{\natexlab}[1]{#1}"
+  write$ newline$
+  "\providecommand{\url}[1]{\texttt{#1}}"
+  write$ newline$
+  "\expandafter\ifx\csname urlstyle\endcsname\relax"
+  write$ newline$
+  "  \providecommand{\doi}[1]{doi: #1}\else"
+  write$ newline$
+  "  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi"
+  write$ newline$
+}
+
+EXECUTE {begin.bib}
+
+EXECUTE {init.state.consts}
+
+ITERATE {call.type$}
+
+FUNCTION {end.bib}
+{ newline$
+  "\end{thebibliography}" write$ newline$
+}
+
+EXECUTE {end.bib}
diff --git a/workspace/final/paper.aux b/workspace/final/paper.aux
new file mode 100644
index 0000000..9e2e278
--- /dev/null
+++ b/workspace/final/paper.aux
@@ -0,0 +1,124 @@
+\relax 
+\bibstyle{corlabbrvnat}
+\providecommand\hyper@newdestlabel[2]{}
+\providecommand\HyField@AuxAddToFields[1]{}
+\providecommand\HyField@AuxAddToCoFields[2]{}
+\citation{black2024pi0,chi2023diffusion}
+\citation{ho2020denoising}
+\citation{peebles2023scalable}
+\citation{lipman2023flow,liu2022flow}
+\citation{geng2025mean}
+\citation{geng2025improved}
+\citation{he2016deep,vaswani2017attention}
+\citation{kimi2026attention}
+\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}{section.1}\protected@file@percent }
+\newlabel{sec:introduction}{{1}{1}{Introduction}{section.1}{}}
+\newlabel{sec:introduction@cref}{{[section][1][]1}{[1][1][]1}{}{}{}}
+\citation{zhao2023learning,shukor2025smolvla}
+\citation{black2024pi0,chi2023diffusion}
+\citation{ho2020denoising}
+\citation{peebles2023scalable}
+\citation{lipman2023flow}
+\citation{liu2022flow}
+\citation{brohan2022rt,brohan2023rt}
+\citation{kim2024openvla}
+\citation{shukor2025smolvla,pertsch2025fast}
+\citation{zhao2023learning}
+\citation{kimi2026attention}
+\def\@LN@column{1}
+\@writefile{toc}{\contentsline {section}{\numberline {2}Related Work}{2}{section.2}\protected@file@percent }
+\newlabel{sec:related_work}{{2}{2}{Related Work}{section.2}{}}
+\newlabel{sec:related_work@cref}{{[section][2][]2}{[1][2][]2}{}{}{}}
+\@writefile{toc}{\contentsline {paragraph}{Diffusion and flow policies for robot action generation.}{2}{section*.1}\protected@file@percent }
+\@writefile{toc}{\contentsline {paragraph}{Vision-language-action models and action chunking.}{2}{section*.2}\protected@file@percent }
+\@writefile{toc}{\contentsline {paragraph}{Residual aggregation and attention residuals.}{2}{section*.3}\protected@file@percent }
+\citation{geng2025mean}
+\citation{geng2025improved}
+\def\@LN@column{1}
+\@writefile{toc}{\contentsline {section}{\numberline {3}Method}{3}{section.3}\protected@file@percent }
+\newlabel{sec:method}{{3}{3}{Method}{section.3}{}}
+\newlabel{sec:method@cref}{{[section][3][]3}{[1][2][]3}{}{}{}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Policy formulation}{3}{subsection.3.1}\protected@file@percent }
+\newlabel{sec:policy_formulation}{{3.1}{3}{Policy formulation}{subsection.3.1}{}}
+\newlabel{sec:policy_formulation@cref}{{[subsection][1][3]3.1}{[1][3][]3}{}{}{}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}Improved Mean Flow action generation}{3}{subsection.3.2}\protected@file@percent }
+\newlabel{sec:imf_action_generation}{{3.2}{3}{Improved Mean Flow action generation}{subsection.3.2}{}}
+\newlabel{sec:imf_action_generation@cref}{{[subsection][2][3]3.2}{[1][3][]3}{}{}{}}
+\def\@LN@column{1}
+\@writefile{toc}{\contentsline {subsection}{\numberline {3.3}Attention Residual policy transformer}{4}{subsection.3.3}\protected@file@percent }
+\newlabel{sec:attnres_policy}{{3.3}{4}{Attention Residual policy transformer}{subsection.3.3}{}}
+\newlabel{sec:attnres_policy@cref}{{[subsection][3][3]3.3}{[1][3][]4}{}{}{}}
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+\bibdata{refs}
+\bibcite{black2024pi0}{{1}{2024}{{Black et~al.}}{{Black, Brown, Driess, Esmail, Equi, Finn, Fusai, Groom, Hausman, Ichter, et~al.}}}
+\bibcite{chi2023diffusion}{{2}{2023}{{Chi et~al.}}{{Chi, Xu, Feng, Cousineau, Du, Burchfiel, Tedrake, and Song}}}
+\bibcite{ho2020denoising}{{3}{2020}{{Ho et~al.}}{{Ho, Jain, and Abbeel}}}
+\bibcite{peebles2023scalable}{{4}{2023}{{Peebles and Xie}}{{}}}
+\bibcite{lipman2023flow}{{5}{2023}{{Lipman et~al.}}{{Lipman, Chen, Ben-Hamu, Nickel, and Le}}}
+\bibcite{liu2022flow}{{6}{2022}{{Liu et~al.}}{{Liu, Gong, and Liu}}}
+\bibcite{geng2025mean}{{7}{2025{}}{{Geng et~al.}}{{Geng, Deng, Bai, Kolter, and He}}}
+\bibcite{geng2025improved}{{8}{2025{}}{{Geng et~al.}}{{Geng, Lu, Wu, Shechtman, Kolter, and He}}}
+\bibcite{he2016deep}{{9}{2016}{{He et~al.}}{{He, Zhang, Ren, and Sun}}}
+\bibcite{vaswani2017attention}{{10}{2017}{{Vaswani et~al.}}{{Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez, Kaiser, and Polosukhin}}}
+\bibcite{kimi2026attention}{{11}{2026}{{Kimi Team}}{{}}}
+\bibcite{zhao2023learning}{{12}{2023}{{Zhao et~al.}}{{Zhao, Kumar, Levine, and Finn}}}
+\bibcite{shukor2025smolvla}{{13}{2025}{{Shukor et~al.}}{{Shukor, Aubakirova, Capuano, Kooijmans, Palma, Zouitine, Aractingi, Pascal, Russi, Marafioti, et~al.}}}
+\bibcite{brohan2022rt}{{14}{2022}{{Brohan et~al.}}{{Brohan, Brown, Carbajal, Chebotar, Dabis, Finn, Gopalakrishnan, Hausman, Herzog, Hsu, et~al.}}}
+\bibcite{brohan2023rt}{{15}{2023}{{Brohan et~al.}}{{Brohan, Brown, Carbajal, Chebotar, Chen, Choromanski, Ding, Driess, Dubey, Finn, et~al.}}}
+\bibcite{kim2024openvla}{{16}{2024}{{Kim et~al.}}{{Kim, Pertsch, Karamcheti, Xiao, Balakrishna, Nair, Rafailov, Foster, Lam, Sanketi, et~al.}}}
+\bibcite{pertsch2025fast}{{17}{2025}{{Pertsch et~al.}}{{Pertsch, Stachowicz, Ichter, Driess, Nair, Vuong, Mees, Finn, and Levine}}}
+\def\@LN@column{1}
+\gdef \@abspage@last{9}
diff --git a/workspace/final/paper.bbl b/workspace/final/paper.bbl
new file mode 100644
index 0000000..e24b88b
--- /dev/null
+++ b/workspace/final/paper.bbl
@@ -0,0 +1,119 @@
+\begin{thebibliography}{17}
+\providecommand{\natexlab}[1]{#1}
+\providecommand{\url}[1]{\texttt{#1}}
+\expandafter\ifx\csname urlstyle\endcsname\relax
+  \providecommand{\doi}[1]{doi: #1}\else
+  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi
+
+\bibitem[Black et~al.(2024)Black, Brown, Driess, Esmail, Equi, Finn, Fusai,
+  Groom, Hausman, Ichter, et~al.]{black2024pi0}
+K.~Black, N.~Brown, D.~Driess, A.~Esmail, M.~Equi, C.~Finn, N.~Fusai, L.~Groom,
+  K.~Hausman, B.~Ichter, et~al.
+\newblock {$\pi_0$}: A vision-language-action flow model for general robot
+  control.
+\newblock \emph{arXiv preprint arXiv:2410.24164}, 2024.
+
+\bibitem[Chi et~al.(2023)Chi, Xu, Feng, Cousineau, Du, Burchfiel, Tedrake, and
+  Song]{chi2023diffusion}
+C.~Chi, Z.~Xu, S.~Feng, E.~Cousineau, Y.~Du, B.~Burchfiel, R.~Tedrake, and
+  S.~Song.
+\newblock Diffusion policy: Visuomotor policy learning via action diffusion.
+\newblock \emph{arXiv preprint arXiv:2303.04137}, 2023.
+
+\bibitem[Ho et~al.(2020)Ho, Jain, and Abbeel]{ho2020denoising}
+J.~Ho, A.~Jain, and P.~Abbeel.
+\newblock Denoising diffusion probabilistic models.
+\newblock In \emph{Advances in Neural Information Processing Systems}, 2020.
+
+\bibitem[Peebles and Xie(2023)]{peebles2023scalable}
+W.~Peebles and S.~Xie.
+\newblock Scalable diffusion models with transformers.
+\newblock In \emph{IEEE/CVF International Conference on Computer Vision}, 2023.
+
+\bibitem[Lipman et~al.(2023)Lipman, Chen, Ben-Hamu, Nickel, and
+  Le]{lipman2023flow}
+Y.~Lipman, R.~T.~Q. Chen, H.~Ben-Hamu, M.~Nickel, and M.~Le.
+\newblock Flow matching for generative modeling.
+\newblock In \emph{International Conference on Learning Representations}, 2023.
+
+\bibitem[Liu et~al.(2022)Liu, Gong, and Liu]{liu2022flow}
+X.~Liu, C.~Gong, and Q.~Liu.
+\newblock Flow straight and fast: Learning to generate and transfer data with
+  rectified flow.
+\newblock \emph{arXiv preprint arXiv:2209.03003}, 2022.
+
+\bibitem[Geng et~al.(2025{\natexlab{a}})Geng, Deng, Bai, Kolter, and
+  He]{geng2025mean}
+Z.~Geng, M.~Deng, X.~Bai, J.~Z. Kolter, and K.~He.
+\newblock Mean flows for one-step generative modeling.
+\newblock \emph{arXiv preprint arXiv:2505.13447}, 2025{\natexlab{a}}.
+
+\bibitem[Geng et~al.(2025{\natexlab{b}})Geng, Lu, Wu, Shechtman, Kolter, and
+  He]{geng2025improved}
+Z.~Geng, Y.~Lu, Z.~Wu, E.~Shechtman, J.~Z. Kolter, and K.~He.
+\newblock Improved mean flows: On the challenges of fastforward generative
+  models.
+\newblock \emph{arXiv preprint arXiv:2512.02012}, 2025{\natexlab{b}}.
+
+\bibitem[He et~al.(2016)He, Zhang, Ren, and Sun]{he2016deep}
+K.~He, X.~Zhang, S.~Ren, and J.~Sun.
+\newblock Deep residual learning for image recognition.
+\newblock In \emph{IEEE Conference on Computer Vision and Pattern Recognition},
+  2016.
+
+\bibitem[Vaswani et~al.(2017)Vaswani, Shazeer, Parmar, Uszkoreit, Jones, Gomez,
+  Kaiser, and Polosukhin]{vaswani2017attention}
+A.~Vaswani, N.~Shazeer, N.~Parmar, J.~Uszkoreit, L.~Jones, A.~N. Gomez,
+  L.~Kaiser, and I.~Polosukhin.
+\newblock Attention is all you need.
+\newblock In \emph{Advances in Neural Information Processing Systems}, 2017.
+
+\bibitem[{Kimi Team}(2026)]{kimi2026attention}
+{Kimi Team}.
+\newblock Attention residuals.
+\newblock \emph{arXiv preprint arXiv:2603.15031}, 2026.
+
+\bibitem[Zhao et~al.(2023)Zhao, Kumar, Levine, and Finn]{zhao2023learning}
+T.~Z. Zhao, V.~Kumar, S.~Levine, and C.~Finn.
+\newblock Learning fine-grained bimanual manipulation with low-cost hardware.
+\newblock \emph{arXiv preprint arXiv:2304.13705}, 2023.
+
+\bibitem[Shukor et~al.(2025)Shukor, Aubakirova, Capuano, Kooijmans, Palma,
+  Zouitine, Aractingi, Pascal, Russi, Marafioti, et~al.]{shukor2025smolvla}
+M.~Shukor, D.~Aubakirova, F.~Capuano, P.~Kooijmans, S.~Palma, A.~Zouitine,
+  M.~Aractingi, C.~Pascal, M.~Russi, A.~Marafioti, et~al.
+\newblock Smolvla: A vision-language-action model for affordable and efficient
+  robotics.
+\newblock \emph{arXiv preprint arXiv:2506.01844}, 2025.
+
+\bibitem[Brohan et~al.(2022)Brohan, Brown, Carbajal, Chebotar, Dabis, Finn,
+  Gopalakrishnan, Hausman, Herzog, Hsu, et~al.]{brohan2022rt}
+A.~Brohan, N.~Brown, J.~Carbajal, Y.~Chebotar, J.~Dabis, C.~Finn,
+  K.~Gopalakrishnan, K.~Hausman, A.~Herzog, J.~Hsu, et~al.
+\newblock Rt-1: Robotics transformer for real-world control at scale.
+\newblock \emph{arXiv preprint arXiv:2212.06817}, 2022.
+
+\bibitem[Brohan et~al.(2023)Brohan, Brown, Carbajal, Chebotar, Chen,
+  Choromanski, Ding, Driess, Dubey, Finn, et~al.]{brohan2023rt}
+A.~Brohan, N.~Brown, J.~Carbajal, Y.~Chebotar, X.~Chen, K.~Choromanski,
+  T.~Ding, D.~Driess, A.~Dubey, C.~Finn, et~al.
+\newblock Rt-2: Vision-language-action models transfer web knowledge to robotic
+  control.
+\newblock In \emph{Conference on Robot Learning}, 2023.
+
+\bibitem[Kim et~al.(2024)Kim, Pertsch, Karamcheti, Xiao, Balakrishna, Nair,
+  Rafailov, Foster, Lam, Sanketi, et~al.]{kim2024openvla}
+M.~J. Kim, K.~Pertsch, S.~Karamcheti, T.~Xiao, A.~Balakrishna, S.~Nair,
+  R.~Rafailov, E.~Foster, G.~Lam, P.~Sanketi, et~al.
+\newblock Openvla: An open-source vision-language-action model.
+\newblock \emph{arXiv preprint arXiv:2406.09246}, 2024.
+
+\bibitem[Pertsch et~al.(2025)Pertsch, Stachowicz, Ichter, Driess, Nair, Vuong,
+  Mees, Finn, and Levine]{pertsch2025fast}
+K.~Pertsch, K.~Stachowicz, B.~Ichter, D.~Driess, S.~Nair, Q.~Vuong, O.~Mees,
+  C.~Finn, and S.~Levine.
+\newblock Fast: Efficient action tokenization for vision-language-action
+  models.
+\newblock \emph{arXiv preprint arXiv:2501.09747}, 2025.
+
+\end{thebibliography}
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+
+\documentclass{article}
+
+\usepackage{corl_2026}
+\usepackage[T1]{fontenc}
+\usepackage{microtype}
+\usepackage{booktabs}
+\usepackage{multirow}
+\usepackage{graphicx}
+\usepackage{amsmath,amssymb}
+\usepackage{enumitem}
+\usepackage{tcolorbox}
+\usepackage[capitalize]{cleveref}
+\usepackage{url}
+
+\title{iMF-AttnRes: Fast Mean-Flow Action Generation with Attention Residuals for Simulated Robot Manipulation}
+
+\author{Anonymous Authors}
+
+\begin{document}
+\maketitle
+
+\begin{abstract}
+Diffusion-style vision-language-action policies provide expressive action distributions, but iterative inference can be too slow for closed-loop manipulation. We study whether improved Mean Flow (iMF), originally motivated by fast-forward generative modeling, can be adapted to action generation so that a policy uses one to a few flow evaluations rather than long denoising chains. We combine iMF with Attention Residuals (AttnRes), which replace fixed residual accumulation in a deep policy transformer with learned depth-wise aggregation over previous layer outputs. In RoboIMI socket peg insertion, the best iMF-AttnRes policy reaches an average reward of 1513.56 and median reward of 1901.5 with 15.122 ms average inference time, compared with diffusion-style infer100 baselines at 861.53/338.291 ms and 1019.39/397.912 ms. In RoboIMI object transfer, the best iMF-AttnRes policy reaches average reward 526.22 and 44/100 success-like episodes, compared with a native diffusion-policy baseline at 319.2 and 29/100. These results suggest that average-flow action generation is a promising route to low-latency robot policies, while also revealing sensitivity to horizon choices, vision-token design, and simulation-to-real validation.
+\end{abstract}
+
+\keywords{Robot learning, imitation learning, flow matching, vision-language-action models}
+
+\section{Introduction}
+\label{sec:introduction}
+
+Learning-based robot manipulation policies increasingly use expressive generative action models. Diffusion Policy showed that action diffusion can be an effective visuomotor policy class for manipulation~\citep{black2024pi0,chi2023diffusion}, inheriting the representational flexibility of denoising diffusion models~\citep{ho2020denoising} and transformer-based diffusion backbones~\citep{peebles2023scalable}. However, this expressivity often comes with a deployment cost: the policy must be evaluated repeatedly during sampling. In the RoboIMI socket peg experiments studied here, two diffusion-style VLA baselines use infer100 sampling and require 338.291 ms and 397.912 ms per inference on their recorded rollouts. Such latencies are undesirable when the robot must repeatedly close the perception-action loop.
+
+This paper asks a direct question: can a robot action generator retain the quality benefits of generative policies while reducing inference to one or a few evaluations? We explore this question by adapting improved Mean Flow (iMF) to VLA-style imitation learning. Flow matching and rectified flow formulate generation through continuous vector fields~\citep{lipman2023flow,liu2022flow}. Mean Flow reframes generation around average velocity, enabling one-step generation~\citep{geng2025mean}; iMF further addresses instability caused by substituting conditional velocities into nonlinear mean-flow relations~\citep{geng2025improved}. For robot control, this distinction matters because fewer evaluations directly increase control responsiveness.
+
+We pair iMF with Attention Residuals (AttnRes). Residual connections are essential for deep visual and transformer networks~\citep{he2016deep,vaswani2017attention}, but standard residual accumulation adds all layer outputs with fixed unit weight. The motivation in our notes is to rewrite residual networks as a sum over layer increments, then replace the uniform sum with a learned softmax aggregation. AttnRes implements this view by letting each layer attend over preceding residual outputs~\citep{kimi2026attention}. We use this mechanism in the policy transformer to reduce depth-wise feature dilution while keeping the action generator fast.
+
+Our contributions are:
+\begin{enumerate}[leftmargin=*]
+    \item We formulate an iMF-AttnRes VLA policy for simulated robot imitation learning, combining one-to-few-step average-flow action generation with learned residual aggregation.
+    \item We provide 100-rollout evaluations on two RoboIMI manipulation environments, socket peg insertion and object transfer, comparing against diffusion-style VLA baselines, a native Diffusion Policy baseline, ACT-style action chunking, and SmolVLA-style compact VLA inference~\citep{zhao2023learning,shukor2025smolvla}.
+    \item We report both task quality and deployment speed. On socket insertion, the strongest iMF-AttnRes run improves average reward over the ph16 diffusion-style baseline by 1.76$\times$ and inference FPS by 22.97$\times$; on object transfer, the strongest iMF-AttnRes run improves average reward over the native diffusion-policy baseline by 1.65$\times$.
+\end{enumerate}
+We deliberately keep the claims scoped to simulation: hardware varies across runs, and real-robot transfer remains future work.
+
+\section{Related Work}
+\label{sec:related_work}
+
+\paragraph{Diffusion and flow policies for robot action generation.}
+Diffusion Policy introduced action diffusion as a visuomotor policy learning framework~\citep{black2024pi0,chi2023diffusion}, building on denoising diffusion objectives~\citep{ho2020denoising}. Diffusion transformers further show that transformer backbones can scale generative modeling~\citep{peebles2023scalable}. These models are expressive because they iteratively refine samples, but iterative refinement is also a latency source. Flow matching provides an alternative continuous generative formulation by learning vector fields that transport probability paths~\citep{lipman2023flow}. Rectified flow emphasizes straighter transport paths and faster sampling~\citep{liu2022flow}. Our method follows this fast-generation lineage but uses the improved mean-flow relation to target one-to-few-step action generation.
+
+\paragraph{Vision-language-action models and action chunking.}
+Transformer robot policies such as RT-1 and RT-2 show how large sequence models can map observations and task context to robot actions~\citep{brohan2022rt,brohan2023rt}. OpenVLA studies open VLA modeling at scale~\citep{kim2024openvla}, while compact or efficient VLA models such as SmolVLA and FAST reduce deployment cost through smaller backbones or efficient action tokenization~\citep{shukor2025smolvla,pertsch2025fast}. Action Chunking with Transformers (ACT) predicts chunks of future actions to improve temporal consistency and execution efficiency~\citep{zhao2023learning}. The iMF-AttnRes policy is complementary: it retains chunked execution but changes the generative action sampler so that each chunk can be produced with one to a few evaluations.
+
+
+\paragraph{Residual aggregation and attention residuals.}
+Standard residual networks update $x_t=x_{t-1}+y_t$, so recursively $x_t=y_0+y_1+\cdots+y_t$. Equivalently, the next layer receives a uniform aggregation of all previous residual increments. A natural generalization is
+\begin{equation}
+    y_{t+1}=f_{t+1}\left(\sum_{s=0}^t a_{t+1,s}y_s\right),\qquad a_{t+1,s}\ge0,\quad \sum_{s=0}^t a_{t+1,s}=1.
+\end{equation}
+AttnRes instantiates the weights with an attention distribution over previous residual outputs,
+\begin{equation}
+    a_{t+1,s}\propto\exp\left(w_{t+1}^{\top}\mathrm{RMSNorm}(y_s)\right).
+\end{equation}
+This preserves the residual pathway while allowing each layer to select useful earlier representations instead of uniformly accumulating all of them~\citep{kimi2026attention}. We use this mechanism in the policy transformer; our ablations suggest that applying AttnRes too broadly inside the vision stack is not automatically beneficial.
+
+\section{Method}
+\label{sec:method}
+
+\subsection{Policy formulation}
+\label{sec:policy_formulation}
+
+We consider simulated imitation learning in RoboIMI. At each control step, the policy receives visual observations, robot state, and a task context, and predicts an action chunk of length \texttt{exec}. The main iMF-AttnRes socket and object-transfer policies use ph32/exec16 or related horizon-execution settings. The rollout evaluator records cumulative reward, median reward, mean maximum reward, nonzero-reward episode count, task-specific success-like threshold count, inference FPS, control FPS, and inference latency.
+
+The policy contains three conceptual parts. First, an observation encoder maps visual and state inputs into policy tokens. Second, a transformer policy core uses AttnRes in place of fixed residual accumulation. Third, the action generator uses iMF to predict an average flow over action noise-to-data paths, enabling one-to-few inference evaluations. In the strongest socket variants, the generator uses infer2 or infer3; in the strongest object-transfer run, it uses infer1.
+
+\subsection{Improved Mean Flow action generation}
+\label{sec:imf_action_generation}
+
+Classical flow matching trains an instantaneous velocity field. Let $x_t$ denote a sample at time $t$ and let the instantaneous flow satisfy
+\begin{equation}
+    \frac{d x_t}{dt} = v(x_t,t).
+\end{equation}
+If a policy takes very few integration or denoising steps, using only this local instantaneous velocity can cause large path errors when the learned trajectory is curved. Mean Flow addresses this by training an average velocity over an interval. For two times $r<t$, define
+\begin{equation}
+    \bar{v}(z_t,r,t) = \frac{1}{t-r}\int_r^t v(z_\tau,\tau)d\tau.
+\end{equation}
+The model receives $(z_t,r,t)$ and predicts $u_\theta(z_t,r,t)$, an approximation to $\bar{v}(z_t,r,t)$. At inference, this average velocity is used to move directly across a larger interval, replacing a long sequence of small denoising or ODE steps.
+
+The useful training identity comes from differentiating $(t-r)\bar{v}$ with respect to $t$:
+\begin{equation}
+    \bar{v}(z_t,r,t)=v(z_t,t) - (t-r)\frac{d}{dt}\bar{v}(z_t,r,t).
+\end{equation}
+The total derivative contains the dependence of $\bar{v}$ on the current state and time, and can be written as a Jacobian-vector product,
+\begin{equation}
+    \frac{d}{dt}\bar{v}(z_t,r,t)
+    = \mathrm{jvp}\big(\bar{v},(z,r,t),(v,0,1)\big).
+\end{equation}
+Mean Flow uses this relation to train an average-velocity predictor for one-step generation~\citep{geng2025mean}. In our setting, the target action sample and noise sample provide a conditional straight-path velocity, but the nonlinear JVP expression should conceptually depend on the marginal velocity field. Directly substituting the conditional velocity inside the nonlinear term can destabilize training. We therefore use the improved Mean Flow correction~\citep{geng2025improved}:
+\begin{equation}
+    V_\theta(z_t,r,t) = u_\theta(z_t,r,t) + (t-r)\mathrm{JVP}_{\mathrm{sg}}\big(u_\theta; v_\theta\big).
+\end{equation}
+Here $v_\theta$ is obtained from the same network at the instantaneous case, and the stop-gradient JVP direction prevents the nonlinear term from destabilizing the target. The supervised target remains the conditional straight-path velocity available from the imitation action sample and noise sample, but the learned branch used at deployment is the average-flow predictor $u_\theta$. This is the mechanism that allows infer1, infer2, and infer3 policies to compete with infer100 diffusion-style baselines.
+
+\subsection{Attention Residual policy transformer}
+\label{sec:attnres_policy}
+
+We spell out AttnRes by first rewriting an ordinary residual stack. Let $x_l$ be the hidden state entering layer $l$, and let the layer output increment be
+\begin{equation}
+    y_l = f_l(x_{l-1}), \qquad x_l = x_{l-1}+y_l .
+\end{equation}
+With the convention $y_0=x_0$, recursively expanding the residual updates gives
+\begin{equation}
+    x_l = y_0+y_1+\cdots+y_l = \sum_{s=0}^{l} y_s .
+\end{equation}
+Therefore, the next residual block can be written as
+\begin{equation}
+    y_{l+1}=f_{l+1}(x_l)
+           = f_{l+1}\left(\sum_{s=0}^{l} y_s\right).
+    \label{eq:standard_residual_sum}
+\end{equation}
+This makes the implicit assumption clear: a standard residual network gives every previous increment a fixed coefficient of one before layer $l+1$ computes the next increment. If we normalize the coefficients only to emphasize their relative importance, this is equivalent to uniform depth aggregation,
+\begin{equation}
+    y_{l+1}=f_{l+1}\left((l+1)\sum_{s=0}^{l}\frac{1}{l+1}y_s\right),
+\end{equation}
+so the layer cannot choose which earlier residual features are most useful.
+
+AttnRes replaces this fixed aggregation with learned, layer-dependent weights. Instead of feeding $f_{l+1}$ the unweighted residual sum, we form
+\begin{equation}
+    \tilde{x}_l = \sum_{s=0}^{l} a_{l+1,s} y_s,
+    \qquad
+    a_{l+1,s}\ge 0,
+    \qquad
+    \sum_{s=0}^{l} a_{l+1,s}=1,
+    \label{eq:weighted_residual_sum}
+\end{equation}
+then compute
+\begin{equation}
+    y_{l+1}=f_{l+1}(\tilde{x}_l),
+    \qquad
+    x_{l+1}=x_l+y_{l+1}.
+\end{equation}
+The attention weights are obtained by scoring each previous residual increment with a learned query vector for the current layer:
+\begin{equation}
+    e_{l+1,s}=w_{l+1}^{\top}\mathrm{RMSNorm}(y_s),
+    \qquad
+    a_{l+1,s}=\frac{\exp(e_{l+1,s})}{\sum_{j=0}^{l}\exp(e_{l+1,j})}.
+    \label{eq:attnres_weights}
+\end{equation}
+Equations~\eqref{eq:standard_residual_sum}--\eqref{eq:attnres_weights} show the difference in one line: standard residuals use a fixed sum $\sum_s y_s$, whereas AttnRes uses an attention-weighted sum $\sum_s a_{l+1,s}y_s$ whose coefficients depend on the current layer. This keeps the residual pathway but lets each policy layer select earlier visual-state-action features rather than uniformly accumulating all previous increments. In our object-transfer ablations, the best settings apply AttnRes in the policy transformer; replacing residuals throughout the vision encoder underperforms, suggesting that the placement of learned residual aggregation is important.
+
+\subsection{Inference and control loop}
+\label{sec:inference_loop}
+
+At deployment, the iMF-AttnRes policy samples an action chunk with one to a few average-flow evaluations and then executes the chunk for \texttt{exec} control steps. This contrasts with diffusion-style baselines whose run names include infer100. For socket insertion, the strongest iMF-AttnRes policies use ph32/exec16 and infer2 or infer3. For object transfer, the strongest iMF-AttnRes run uses ph32/exec16 and infer1. The resulting control loop is simple: encode observations, run the AttnRes transformer, evaluate the iMF action generator a small number of times, execute the predicted chunk, and replan on the next observation window.
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-method-overview}}]
+\small Paper Banana prompt: create a clean 16:9 academic system diagram for iMF-AttnRes VLA. Show multi-view images, robot state, and optional language/task conditioning entering a VLA encoder; a policy transformer with AttnRes depth-wise residual aggregation; an improved Mean Flow action generator predicting average flow with one-to-few evaluations; and an exec-length action chunk controlling RoboIMI socket-insert and sim\_transfer robots. Use muted conference-paper colors, clear arrows, no decorative 3D, and labels for iMF, AttnRes, infer1/infer2/infer3, and exec16.
+\end{tcolorbox}
+\caption{Planned method overview figure. The current draft intentionally stores the Paper Banana generation prompt as a placeholder instead of generating an image.}
+\label{fig:method_overview}
+\end{figure}
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-imf-attnres-components}}]
+\small Paper Banana prompt: create a 16:9 technical diagram contrasting classical flow matching instantaneous velocity $dx_t/dt=v(x_t,t)$; Mean Flow average velocity over $[r,t]$ with the JVP correction term; improved Mean Flow training relation $V_\theta(z_t)=u_\theta(z_t)+(t-r)\mathrm{JVP}_{\mathrm{sg}}(u_\theta;v_\theta)$; and AttnRes weighted residual aggregation $a_{t+1,s}$ proportional to $\exp(w_{t+1}\cdot\mathrm{RMSNorm}(y_s))$. Use equation callouts, minimal arrows, and a final callout saying one-to-few-step action generation for robot control.
+\end{tcolorbox}
+\caption{Planned technical component figure. The prompt is retained as a placeholder for later Paper Banana rendering.}
+\label{fig:imf_attnres_components}
+\end{figure}
+
+\section{Experiments}
+\label{sec:experiments}
+
+\subsection{Setup and metrics}
+\label{sec:setup_metrics}
+
+We evaluate in two RoboIMI simulation environments. The socket peg task measures progress toward inserting a peg-like object into a socket. The sim\_transfer task measures object-transfer manipulation. Each reported row uses 100 rollouts. Metrics are copied directly from the rollout logs: average cumulative reward, median reward, mean maximum reward, maximum cumulative reward, count of nonzero-reward episodes, count of success-like episodes, inference FPS, control FPS, and average inference time when available. For socket insertion, the recorded success-like threshold is \texttt{max\_reward > 4}; for sim\_transfer, it is \texttt{max\_reward >= 4}. Hardware differs across runs, so speed comparisons are practical deployment measurements rather than perfectly normalized throughput benchmarks.
+
+\subsection{Socket peg insertion}
+\label{sec:socket_results}
+
+\Cref{tab:socket_results} summarizes the socket peg insertion results. The best iMF-AttnRes policy by average reward is the infer3 model, with average reward 1513.56 and median reward 1901.5. It exceeds both diffusion-style infer100 baselines: the ph16 baseline obtains average reward 861.53 and median reward 668.0, while the ph32/exec16 baseline obtains average reward 1019.39 and median reward 804.0. The latency gap is large. The iMF-AttnRes infer2 and infer3 policies require 14.409 ms and 15.122 ms per inference, while the two infer100 baselines require 338.291 ms and 397.912 ms.
+
+\begin{table*}[t]
+\centering
+\small
+\setlength{\tabcolsep}{3.5pt}
+\caption{Socket peg insertion over 100 rollouts. Success-like uses the recorded \texttt{max\_reward > 4} count.}
+\label{tab:socket_results}
+\begin{tabular}{llrrrrrr}
+\toprule
+Method & Setting & Avg. reward & Median & Avg. max & Nonzero & Success-like & Latency ms \\
+\midrule
+iMF-AttnRes & infer1, ph32, exec16, 50k & 1275.22 & 1490.5 & 3.12 & 84/100 & 0/100 & 16.186 \\
+Diffusion-style VLA & infer100, ph16, 150k & 861.53 & 668.0 & 2.58 & 97/100 & 2/100 & 338.291 \\
+Diffusion-style VLA & infer100, ph32, exec16, 150k & 1019.39 & 804.0 & 2.47 & 92/100 & 4/100 & 397.912 \\
+iMF-AttnRes & infer2, ph32, exec16, 150k & 1472.62 & 1825.0 & 3.29 & 83/100 & 5/100 & 14.409 \\
+iMF-AttnRes & infer3, ph32, exec16, 150k & \textbf{1513.56} & \textbf{1901.5} & 3.28 & 83/100 & 3/100 & 15.122 \\
+ACT & action chunking & 289.60 & 13.0 & 1.29 & 55/100 & 1/100 & 100.212 \\
+SmolVLA & 100k & 466.16 & 106.0 & 1.72 & 89/100 & 0/100 & \textbf{2.614} \\
+\bottomrule
+\end{tabular}
+\end{table*}
+
+The nonzero-reward count reveals an important caveat. The ph16 diffusion-style baseline has 97/100 nonzero episodes, higher than the iMF-AttnRes infer2 and infer3 policies at 83/100. Yet its average and median rewards are much lower. Thus, in this task, nonzero contact or partial progress is not sufficient; the iMF-AttnRes policies more often produce high-reward trajectories when they engage the task successfully. SmolVLA is the fastest method in raw latency and control FPS, but its reward is lower than iMF-AttnRes. ACT underperforms both iMF-AttnRes and the diffusion-style VLA baselines in this setup.
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-socket-reward-latency}}]
+\small Paper Banana prompt: create a 4:3 publication-quality reward-latency comparison for socket peg insertion. Plot methods from the socket table with average reward on the y-axis and average inference time or inverse latency on the x-axis. Highlight iMF-AttnRes infer2/infer3 at 1472.62/1513.56 average reward and 14.409/15.122 ms, diffusion-style infer100 baselines at 861.53/1019.39 average reward and 338.291/397.912 ms, ACT at 289.6 and 100.2116 ms, and SmolVLA at 466.16 and 2.614 ms. Use log-scale latency if helpful and label the speed-quality Pareto frontier.
+\end{tcolorbox}
+\caption{Planned socket reward-latency figure. The current draft contains the Paper Banana prompt placeholder instead of a rendered image.}
+\label{fig:socket_reward_latency}
+\end{figure}
+
+\subsection{Object transfer and ablations}
+\label{sec:sim_transfer_results}
+
+\Cref{tab:sim_transfer_results} reports the object-transfer results. The strongest iMF-AttnRes run reaches average reward 526.22 and 44/100 success-like episodes, exceeding the native diffusion-policy DiT/DDPM/ResNet baseline at average reward 319.2 and 29/100 success-like episodes. This is the clearest sim\_transfer evidence that iMF-AttnRes can improve both reward and success-like threshold count.
+
+\begin{table*}[t]
+\centering
+\small
+\setlength{\tabcolsep}{3.2pt}
+\caption{Object transfer / sim\_transfer over 100 rollouts. Success-like uses the recorded \texttt{max\_reward >= 4} count.}
+\label{tab:sim_transfer_results}
+\begin{tabular}{llrrrrrr}
+\toprule
+Method & Setting & Avg. reward & Median & Avg. max & Nonzero & Success-like & Inf. FPS \\
+\midrule
+Native Diffusion Policy & DiT + DDPM + ResNet & 319.20 & 6.0 & 1.68 & 55/100 & 29/100 & 32.09 \\
+Diffusion-style VLA & emb384, layer18 & 233.52 & 0.0 & 1.12 & 39/100 & 17/100 & 1.859 \\
+iMF multi-token ResNet18 & step34999, ph16, exec08 & 260.66 & 0.0 & 1.12 & 33/100 & 23/100 & \textbf{441.80} \\
+iMF full AttnRes vision & ph16, exec08, 50k & 228.42 & 0.0 & 0.94 & 31/100 & 16/100 & 55.997 \\
+iMF-AttnRes DiT only & ph16, exec16, 50k & 240.64 & 0.0 & 1.02 & 32/100 & 19/100 & 137.996 \\
+iMF-AttnRes DiT only & ph32, exec08, 50k & 163.28 & 0.0 & 0.76 & 26/100 & 12/100 & 85.638 \\
+iMF-AttnRes DiT only & ph32, exec32, 50k & 260.72 & 0.0 & 1.18 & 38/100 & 21/100 & 9.557 \\
+iMF-AttnRes DiT only & ph16, exec08, 50k & 229.56 & 0.0 & 1.18 & 41/100 & 18/100 & 69.984 \\
+iMF-AttnRes DiT only & ph08, exec08, 50k & 237.02 & 0.0 & 1.32 & 48/100 & 18/100 & 69.192 \\
+iMF-AttnRes DiT only & ph32, exec16, 50k & 49.88 & 0.0 & 0.32 & 13/100 & 4/100 & 138.903 \\
+iMF-AttnRes & infer1, ph32, exec16, 50k & \textbf{526.22} & \textbf{86.0} & \textbf{2.14} & \textbf{63/100} & \textbf{44/100} & 8.911 \\
+\bottomrule
+\end{tabular}
+\end{table*}
+
+The ablations are mixed and therefore useful. The ResNet18 multi-token iMF model is extremely fast at 441.80 inference FPS, but its average reward is 260.66, below the native diffusion-policy baseline. The full-AttnRes vision model reaches 228.42 average reward, suggesting that replacing residuals inside the vision encoder is not automatically helpful. Horizon and execution length are also sensitive: the ph32/exec16 DiT-only iMF variant reaches only 49.88 average reward despite high inference FPS. The strongest object-transfer run therefore combines iMF-AttnRes with the right horizon and execution setting rather than showing a universally dominant architectural change.
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-sim-transfer-ablation}}]
+\small Paper Banana prompt: create a 4:3 grouped bar chart for sim\_transfer ablations. Show average reward and success-like episode count for native diffusion policy, best sim-transfer iMF-AttnRes infer1, ResNet18 multi-token iMF, full-AttnRes vision, and selected horizon/execution variants. Emphasize that best iMF-AttnRes reaches 526.22 average reward and 44/100 success-like episodes versus native diffusion policy at 319.2 and 29/100, while several ablations underperform.
+\end{tcolorbox}
+\caption{Planned sim\_transfer ablation figure. The prompt is retained for later Paper Banana rendering.}
+\label{fig:sim_transfer_ablation}
+\end{figure}
+
+\subsection{Derived speed-quality comparisons}
+\label{sec:derived_comparisons}
+
+\Cref{tab:derived_comparisons} lists the derived comparisons used to summarize the tradeoff. On socket insertion, iMF-AttnRes infer3 improves average reward by 1.76$\times$ over the ph16 diffusion-style baseline and by 1.48$\times$ over the ph32 diffusion-style baseline. The same infer3 run improves inference FPS by 22.97$\times$ over the ph16 diffusion-style baseline. The infer2 run is 23.48$\times$ lower latency than the ph16 diffusion-style baseline and 28.45$\times$ higher inference FPS than the ph32 diffusion-style baseline. On object transfer, best iMF-AttnRes improves average reward by 1.65$\times$ and success-like episodes by +15 over native Diffusion Policy.
+
+\begin{table}[t]
+\centering
+\small
+\caption{Derived comparisons from the 100-rollout logs.}
+\label{tab:derived_comparisons}
+\begin{tabular}{llr}
+\toprule
+Comparison & Metric & Result \\
+\midrule
+Socket iMF infer3 vs ph16 diffusion & Avg. reward & 1.76$\times$ \\
+Socket iMF infer3 vs ph32 diffusion & Avg. reward & 1.48$\times$ \\
+Socket iMF infer3 vs ACT & Avg. reward & 5.23$\times$ \\
+Socket iMF infer3 vs SmolVLA & Avg. reward & 3.25$\times$ \\
+Socket iMF infer2 vs ph16 diffusion & Latency reduction & 23.48$\times$ \\
+Socket iMF infer3 vs ph16 diffusion & Inference FPS & 22.97$\times$ \\
+Socket iMF infer2 vs ph32 diffusion & Inference FPS & 28.45$\times$ \\
+Sim transfer iMF vs native diffusion & Avg. reward & 1.65$\times$ \\
+Sim transfer iMF vs native diffusion & Success-like episodes & +15 \\
+ResNet18 iMF vs layer18 baseline & Inference FPS & 237.65$\times$ \\
+\bottomrule
+\end{tabular}
+\end{table}
+
+\section{Limitations}
+\label{sec:limitations}
+
+All experiments are simulation rollouts. The data do not establish real-robot transfer, robustness to sensing changes, or safety under hardware execution. CoRL submissions should provide convincing robotics evidence; the present draft should therefore be viewed as a simulation-first manuscript that still needs real-robot or stronger transfer evidence before formal submission.
+
+The speed measurements are also not perfectly hardware-normalized. Runs were collected on RTX 5880 Ada, L20, and RTX 5090 machines as encoded by their run names. Large latency differences between infer100 diffusion-style policies and infer1--3 iMF-AttnRes policies are meaningful because they reflect algorithmic sampling cost, but exact FPS ratios may include hardware and implementation effects. Future experiments should rerun all main policies on the same GPU, with identical rollout parallelism and identical observation preprocessing.
+
+Finally, iMF-AttnRes is sensitive to design choices. In sim\_transfer, several iMF variants underperform the native diffusion-policy baseline, and full AttnRes in the vision encoder performs worse than more conservative policy-transformer AttnRes. This suggests that average-flow training is not a plug-in guarantee; horizon, execution length, visual tokenization, and residual placement must be tuned carefully.
+
+\section{Conclusion}
+\label{sec:conclusion}
+
+We presented a first simulation study of iMF-AttnRes for fast robot action generation. The method adapts improved Mean Flow to VLA imitation learning and uses Attention Residuals to replace fixed residual accumulation in the policy transformer. In socket peg insertion, iMF-AttnRes achieves higher average and median reward than diffusion-style infer100 baselines while reducing inference latency from hundreds of milliseconds to roughly 15 ms. In object transfer, the best iMF-AttnRes run improves average reward and success-like episode count over the native diffusion-policy baseline. At the same time, ablations show that the method is sensitive to horizon/execution settings and residual placement. The next step is controlled, hardware-normalized evaluation with real-robot transfer.
+
+\clearpage
+\bibliography{refs}
+
+\end{document}
diff --git a/workspace/final/refs.bib b/workspace/final/refs.bib
new file mode 100644
index 0000000..a892f5b
--- /dev/null
+++ b/workspace/final/refs.bib
@@ -0,0 +1,140 @@
+@article{chi2023diffusion,
+  title = {Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
+  author = {Chi, Cheng and Xu, Zhenjia and Feng, Siyuan and Cousineau, Eric and Du, Yilun and Burchfiel, Benjamin and Tedrake, Russ and Song, Shuran},
+  year = {2023},
+  journal = {arXiv preprint arXiv:2303.04137},
+  eprint = {2303.04137},
+  archivePrefix = {arXiv}
+}
+
+@inproceedings{ho2020denoising,
+  title = {Denoising Diffusion Probabilistic Models},
+  author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
+  year = {2020},
+  booktitle = {Advances in Neural Information Processing Systems}
+}
+
+@inproceedings{peebles2023scalable,
+  title = {Scalable Diffusion Models with Transformers},
+  author = {Peebles, William and Xie, Saining},
+  year = {2023},
+  booktitle = {IEEE/CVF International Conference on Computer Vision}
+}
+
+@inproceedings{lipman2023flow,
+  title = {Flow Matching for Generative Modeling},
+  author = {Lipman, Yaron and Chen, Ricky T. Q. and Ben-Hamu, Heli and Nickel, Maximilian and Le, Matt},
+  year = {2023},
+  booktitle = {International Conference on Learning Representations}
+}
+
+@article{liu2022flow,
+  title = {Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow},
+  author = {Liu, Xingchao and Gong, Chengyue and Liu, Qiang},
+  year = {2022},
+  journal = {arXiv preprint arXiv:2209.03003},
+  eprint = {2209.03003},
+  archivePrefix = {arXiv}
+}
+
+@article{geng2025mean,
+  title = {Mean Flows for One-step Generative Modeling},
+  author = {Geng, Zhengyang and Deng, Mingyang and Bai, Xingjian and Kolter, J. Zico and He, Kaiming},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2505.13447},
+  eprint = {2505.13447},
+  archivePrefix = {arXiv}
+}
+
+@article{geng2025improved,
+  title = {Improved Mean Flows: On the Challenges of Fastforward Generative Models},
+  author = {Geng, Zhengyang and Lu, Yiyang and Wu, Zongze and Shechtman, Eli and Kolter, J. Zico and He, Kaiming},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2512.02012},
+  eprint = {2512.02012},
+  archivePrefix = {arXiv}
+}
+
+@article{brohan2022rt,
+  title = {RT-1: Robotics Transformer for Real-World Control at Scale},
+  author = {Brohan, Anthony and Brown, Noah and Carbajal, Justice and Chebotar, Yevgen and Dabis, Joseph and Finn, Chelsea and Gopalakrishnan, Keerthana and Hausman, Karol and Herzog, Alexander and Hsu, Jasmine and others},
+  year = {2022},
+  journal = {arXiv preprint arXiv:2212.06817},
+  eprint = {2212.06817},
+  archivePrefix = {arXiv}
+}
+
+@inproceedings{brohan2023rt,
+  title = {RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control},
+  author = {Brohan, Anthony and Brown, Noah and Carbajal, Justice and Chebotar, Yevgen and Chen, Xi and Choromanski, Krzysztof and Ding, Tianli and Driess, Danny and Dubey, Avinava and Finn, Chelsea and others},
+  year = {2023},
+  booktitle = {Conference on Robot Learning}
+}
+
+@article{kim2024openvla,
+  title = {OpenVLA: An Open-Source Vision-Language-Action Model},
+  author = {Kim, Moo Jin and Pertsch, Karl and Karamcheti, Siddharth and Xiao, Ted and Balakrishna, Ashwin and Nair, Suraj and Rafailov, Rafael and Foster, Ethan and Lam, Grace and Sanketi, Pannag and others},
+  year = {2024},
+  journal = {arXiv preprint arXiv:2406.09246},
+  eprint = {2406.09246},
+  archivePrefix = {arXiv}
+}
+
+@article{shukor2025smolvla,
+  title = {SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},
+  author = {Shukor, Mustafa and Aubakirova, Dana and Capuano, Francesco and Kooijmans, Pepijn and Palma, Steven and Zouitine, Adil and Aractingi, Michel and Pascal, Caroline and Russi, Martino and Marafioti, Andres and others},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2506.01844},
+  eprint = {2506.01844},
+  archivePrefix = {arXiv}
+}
+
+@article{black2024pi0,
+  title = {{$\pi_0$}: A Vision-Language-Action Flow Model for General Robot Control},
+  author = {Black, Kevin and Brown, Noah and Driess, Danny and Esmail, Adnan and Equi, Michael and Finn, Chelsea and Fusai, Niccolo and Groom, Lachy and Hausman, Karol and Ichter, Brian and others},
+  year = {2024},
+  journal = {arXiv preprint arXiv:2410.24164},
+  eprint = {2410.24164},
+  archivePrefix = {arXiv}
+}
+
+@article{pertsch2025fast,
+  title = {FAST: Efficient Action Tokenization for Vision-Language-Action Models},
+  author = {Pertsch, Karl and Stachowicz, Kyle and Ichter, Brian and Driess, Danny and Nair, Suraj and Vuong, Quan and Mees, Oier and Finn, Chelsea and Levine, Sergey},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2501.09747},
+  eprint = {2501.09747},
+  archivePrefix = {arXiv}
+}
+
+@article{zhao2023learning,
+  title = {Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware},
+  author = {Zhao, Tony Z. and Kumar, Vikash and Levine, Sergey and Finn, Chelsea},
+  year = {2023},
+  journal = {arXiv preprint arXiv:2304.13705},
+  eprint = {2304.13705},
+  archivePrefix = {arXiv}
+}
+
+@inproceedings{he2016deep,
+  title = {Deep Residual Learning for Image Recognition},
+  author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
+  year = {2016},
+  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}
+}
+
+@inproceedings{vaswani2017attention,
+  title = {Attention Is All You Need},
+  author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
+  year = {2017},
+  booktitle = {Advances in Neural Information Processing Systems}
+}
+
+@article{kimi2026attention,
+  title = {Attention Residuals},
+  author = {{Kimi Team}},
+  year = {2026},
+  journal = {arXiv preprint arXiv:2603.15031},
+  eprint = {2603.15031},
+  archivePrefix = {arXiv}
+}
diff --git a/workspace/inputs/README.md b/workspace/inputs/README.md
new file mode 100644
index 0000000..50ac125
--- /dev/null
+++ b/workspace/inputs/README.md
@@ -0,0 +1,19 @@
+# Inputs
+
+The paper-orchestra pipeline expects the four files below in this
+directory before you start. Optional figures go in `figures/`.
+
+## Required
+
+- `idea.md`                  — Idea Summary (Sparse or Dense; see io-contract.md)
+- `experimental_log.md`      — Setup, raw numeric data, qualitative observations
+- `template.tex`             — LaTeX template for the target conference
+- `conference_guidelines.md` — Page limit, mandatory sections, formatting rules
+
+## Optional
+
+- `figures/`                 — Pre-existing figures (PNG/PDF). If empty, the
+                               plotting agent generates everything from scratch.
+
+See `skills/paper-orchestra/references/io-contract.md` in the repo for the
+exact schemas of each file.
diff --git a/workspace/inputs/conference_guidelines.md b/workspace/inputs/conference_guidelines.md
new file mode 100644
index 0000000..9c6475b
--- /dev/null
+++ b/workspace/inputs/conference_guidelines.md
@@ -0,0 +1,151 @@
+The Conference on Robot Learning (CoRL) is an annual international conference aiming to bring together the robotics and machine learning research communities. It focuses on the increasingly important role of learning in robotics and its interaction with other areas of robotics. The aim of CoRL 2026 is to publish significant original research at the intersection of robotics and machine learning. CoRL is a selective, single-track international conference addressing theory and practice of machine learning for robots. CoRL welcomes papers in areas such as:
+
+    Learning representations for robotic perception and control
+
+    Learning robot foundation models or general-purpose knowledge systems for robotics
+
+    Imitation learning for robotics, e.g. by behavioral cloning and/or inverse reinforcement learning
+
+    Reinforcement learning for control of physical robots
+
+    Model-based and model-free learning for robotic control and decision-making
+
+    Combination of learning- and planning-based approaches in robotics
+
+    Probabilistic learning and representation of uncertainty in robotics
+
+    Automatic robotic data generation for learning methods in robotics
+
+    Learning for Robot Task and Motion Planning
+
+    Learning for multimodal robot perception, sensor fusion, and robot vision
+
+    Learning for human-robot interaction and robot instruction by natural language, gestures as well as alternative devices
+
+    Learning for hardware design and optimization
+
+    Learning approaches to robot safety and alignment; and safety approaches applicable to learning-based robotic systems
+
+    Applications of robot learning in robot manipulation, navigation, locomotion, driving, flight, and other areas of robotics
+
+    Robot systems, hardware, and sensors for learning and data-driven approaches
+
+    Theoretical foundations of robot learning, including generalization theory and uncertainty quantification
+
+    Video models and latent world models for robot learning
+
+    Benchmarks and datasets for robot learning
+
+
+Submissions should focus on a core robotics problem and demonstrate the relevance of proposed models, algorithms, datasets, and benchmarks to robotics. Authors are encouraged to report real-robot experiments or provide convincing evidence that simulation experiments are transferable to real robots. Submissions without a robotics focus will be returned without review.
+
+All submissions should include a Limitations section, explicitly describing limiting assumptions, failure modes, and other limitations of the results and experiments and how these might be addressed in the future.
+
+Authors are also encouraged to submit video, code and data as supplementary materials.
+
+For paper submission format and timeline, see: Instruction for Authors 
+
+
+New This Year (details see below)
+Paper Submission Requirements and Instructions
+Submission Policy
+Reciprocal Reviewing Requirement
+Rebuttal Process
+Concurrent/contemporaneous Work
+Camera Ready Paper Submission
+Key Dates
+New This Year (details see below) 
+
+    An additional 9th page for camera-ready submissions to incorporate reviewer feedback (initial submission is 8 pages).
+
+Paper Submission Requirements and Instructions
+
+CoRL is double-blind, which means all papers must be anonymized. The accepted papers and reviews will be publicly accessible and de-anonymized after decisions are announced. Our aim is to have at least two reviewers per paper. Accepted papers will appear in the Proceedings of Machine Learning Research (formerly JMLR Workshop and Conference Proceedings).
+
+Please keep in mind that the deadlines are final, and we cannot make any accommodations for missing the abstract deadline or paper deadline. Authors may update the title and abstract on OpenReview after the abstract submission deadline. However, authors cannot be added or removed after this deadline, as this information is required to facilitate reviewer assignments. If you need to request an exception, please email our OpenReview Chair Carlo Sferrazza (csferrazza@austin.utexas.edu).
+
+    Papers may be submitted through OpenReview via the link here (https://openreview.net/group?id=robot-learning.org/CoRL/2026/Conference). All submissions should comply with the format and length indicated below: 
+
+    Page limit will be 8 pages for the main paper. Acknowledgments, References, and Appendix (optional) will not count towards the page limit. Please note that reviewers are not required to read the Appendix. The Appendix can be included with the main paper or with the Supplement.
+
+    Please use the LaTeX template: here (https://drive.google.com/file/d/1R9irIW0ImqeDHh5g8Ukm2XGy91sWxOsQ/view?usp=drive_link ).
+
+    Authors are encouraged to submit a supplementary file containing further details, which the reviewers may decide to consult. 
+
+    Authors are highly encouraged to submit a video not exceeding 250 MB (strict) and not more than 3 minutes in length (suggested), providing an overview of the work. 
+
+    All supplementary materials will be submitted through OpenReview as a single zip file.
+
+    All accepted papers will be presented in poster sessions, while selected papers will be invited for an oral spotlight presentation.
+
+    All submissions should include a Limitations section (counted toward the 8-page limit), explicitly describing limiting assumptions, failure modes, and other limitations of the results and experiments, and how these might be addressed in the future.
+
+    Authors are strongly encouraged to include all relevant details in the main paper and the appendix to ensure that future researchers can reproduce the methodology and results. 
+
+Submission Policy
+
+We will not accept papers that are identical or substantially similar to papers that have previously been published or accepted for publication in an archival venue, nor papers submitted in parallel to other conferences or archival venues. Archival venues include conferences and journals with formally published proceedings, but do not include non-archival workshops. Submission is permitted for papers that have previously appeared only as a technical report, e.g., in arXiv.
+
+Dual submission policy: Dual submission is not allowed by default. Submissions that are simultaneously under review at another archival venue will be desk-rejected without review. However, we recognize that some conferences may have decision timelines that slightly overlap with our review period (e.g., ICML decisions on May 1st). In such cases, authors must notify the Publication Chair before submission and clearly indicate the overlapping venue and relevant dates. Approval for such exceptions will be granted on a case-by-case basis. Failure to disclose dual submission may result in rejection and may be reported to both venues.
+
+Generative AI policies: We understand that generative AI is a useful tool in the paper writing process. In order to ensure that the use of generative AI does not pose an undue burden on the review process, we are implementing the following policies this year:
+
+    Papers that contain citations to references that do not exist will be desk-rejected after review by the Program Committee.
+
+    Authors must take full responsibility for the content in submitted papers. LLMs or other generative AI tools do not qualify for authorship. During the paper submission process, authors will be asked to briefly disclose how generative AI tools were used for research and paper writing.
+
+    Authors who submit multiple versions of substantially the same paper (e.g., different papers with the same method, but slightly different ablations, baselines, or algorithmic variations) will face desk rejection of all papers they have submitted, and potentially also be prevented from submitting papers to future iterations of the conference. We are aware that generative AI tools have been used for this purpose in recent conferences, and we will implement checks to flag such papers.
+
+With these policies, we aim to strike a balance between beneficial uses of generative AI (e.g., help with paper editing, figures, literature review, or brainstorming) and uses that degrade the overall quality and integrity of the peer-review process.
+Reciprocal Reviewing Requirement
+
+Qualified authors are required to review for CoRL, according to the policies below.
+
+    All submissions must include at least one author who agrees to serve as a CoRL reviewer. They are qualified if they have at least one accepted publication at a previous robotics (CoRL/ ICRA/ IROS/ RSS) or learning conference (ICLR/ NeurIPS/ ICML) or equivalent journal. If none of the authors are qualified under this definition or all authors are exempt (e.g., serving as AC or SAC), then they are exempt from this requirement.  The abstract submission form will allow submitters to designate an author to fulfill this requirement or to indicate that the submission is exempt from the requirement.
+
+    Additionally, every author with 3 or more submissions must agree to serve as a reviewer. Authors are exempt from the review requirement if they serve as an AC, an SAC, or another organizing chair for CoRL 2026.
+
+    Submissions that do not meet this reciprocal review requirement may be desk-rejected. Additionally, reviewers who fail to adequately participate in the review process (e.g., not submitting reviews on time or submitting highly insufficient or inappropriate reviews) may have their own submissions desk rejected. The program chairs may grant exceptions on a case-by-case basis.
+
+
+Rebuttal Process
+
+In order to focus reviewer effort, papers that receive all reviews in the “reject” range in the initial round will not proceed to the rebuttal phase.
+
+Authors of all other papers are invited to submit a 1-page rebuttal in pdf by the rebuttal deadline. You will not be able to give individual responses to the reviewer on OpenReview, nor will you be able to update the paper during the rebuttal process. The rebuttal should be focused on addressing any factual errors in the review.
+
+All rebuttals will be reviewed by the reviewer, the original AC, and, if applicable, external reviewers. The AC reserves the right to invite new reviewers if needed. The results of the current review(s) will be shared with the new reviewers in such cases.
+
+Concurrent/contemporaneous Work
+
+We consider papers contemporaneous if they are published within the last 4 months, so authors do not need to compare their own work to that paper. Authors are encouraged to cite and discuss all relevant papers, but they may be excused for not knowing about papers not published in peer-reviewed conference proceedings or journals, which include papers exclusively available on arXiv.
+
+Camera Ready Paper Submission
+
+New this year — additional page for incorporating reviewer feedback
+
+Congratulations again on getting your paper accepted to CoRL 2026!  The camera-ready version of your paper is due by Oct 12, 2026 (23:59 Anywhere on Earth). Please submit your camera-ready PDF and signed permission form through OpenReview. 
+
+Here are the instructions for preparing and submitting your camera-ready paper:
+
+1. The camera-ready paper should follow the template: 8-page main text + acknowledgment + references + appendix (optional). Note that this year, we are allowing an additional page to accommodate feedback from the review process. The appendix should be included at the end of the camera-ready PDF, rather than as a separate file. The template is available here. Please make sure to use “\usepackage[final]{corl_2026}.
+
+2. One important note: PMLR does not allow videos to be submitted as supplementary material.  If you have videos, code, datasets, and other supplementary materials, please host them on your own (e.g., YouTube, GitHub, etc). You should provide, in the main text of your paper, a link to this material or a link to your project website.
+
+3. Please sign the permission form for publication in PMLR (form available here). Please rename the pdf to <paperid_firstname_lastname>.pdf.
+
+Please ensure:
+
+1.    Paper length: 9 pages main text + Acknowledgement + References + Appendix (optional)
+
+2.    The author list is NOT anonymous.
+
+3.    Footer on the 1st page: "10th Conference on Robot Learning (CoRL 2026), Austin, Texas, USA."
+
+4.    Text density, fonts, and spacing should be the same as the provided template on the website.
+
+5.    The margin should be the same as the provided template on the website.
+
+6.    The OpenReview title is the same as the PDF title.
+
+Please contact Yoonchang Sung (yoonchang.sung@ntu.edu.sg) if you have any questions.
diff --git a/workspace/inputs/corl_2026.sty b/workspace/inputs/corl_2026.sty
new file mode 100644
index 0000000..1180459
--- /dev/null
+++ b/workspace/inputs/corl_2026.sty
@@ -0,0 +1,472 @@
+% File: corl_2026.sty 
+%
+% Latex templates for the Conference on Robot Learning (CoRL)
+%
+% This template is heavily inspired by the NeurIPS, ICML, ICLR and IEEE Transactions latex templates.
+% Hence we would like to thank: Roman Garnett and the previous mantainers of the NIPS style, Percy Liang and the previous mantainers of the ICML style, Hugo Larochelle for the ICLR style, and Michael Shell for the IEEE Transactions style.
+%
+% History:
+% 2017/04/16 - First revision by Roberto Calandra (roberto.calandra@berkeley.edu).
+%	       Main changes:
+%	       - The abstract is more compact compared to NeurIPS/ICML
+%	       - References are by default using natbib with squared numbers (e.g., [1])
+%	       - DOI fields from the bibtex are automatically converted to hyperlinks 
+%	       to the corresponding page
+%	       - acknowledgments are now a command, and the corresponding subsubsection is
+%	       automatically included only in the final version
+% 2017/06/12 - Modified to use corlabbrvnat.bst, which order the reference by order of appearance in the paper
+% 2017/06/13 - fixed typo
+% 2018/05/09 - Slightly modified for CoRL 2018 by Jun Morimoto (xmorimo@atr.jp)
+% 2019/01/28 - Slightly modified for CoRL 2019 by Jun Nakanishi (jnakanis@meijo-u.ac.jp)
+% 2020/02/02 - Slightly modified for CoRL 2020 by Cynthia Matuszek (cmat@umbc.edu)
+% 2020/08/19 - Added preprint option by Roberto Calandra (rcalandra@fb.com)
+% 2021/05/06 - Slightly modified for CoRL 2021 by Gerhard Neumann (gerhard.neumann@kit.edu)
+% 2022/03/09 - Slightly modified for CoRL 2022 by Minas Liarokapis (minas.liarokapis@auckland.ac.nz)
+% 2022/03/06 - Slightly modified for CoRL 2023 by Marc Toussaint (toussaint@tu-berlin.de)
+% 2024/03/26 - Slightly modified for CoRL 2024 by David Held (dheld@andrew.cmu.edu)
+% 2026/01/10 - Slightly modified for CoRL 2026 by Yoonchang Sung (yoonchang.sung@ntu.edu.sg)
+%
+% TODO: nohyperref is not working at the moment
+% 
+
+\NeedsTeXFormat{LaTeX2e}
+
+% Content to be changed from year to year
+\ProvidesPackage{corl_2026}[2026/08/15 CORL2026 submission/preprint/camera-ready style file]
+\newcommand{\@conferenceordinal}{10th}
+\newcommand{\@conferenceyear}{2026}
+\newcommand{\@conferencelocation}{Austin TX, USA}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Accepted options: [final,preprint,nonatbib,nohyperref]
+
+% Declare the final option, which creates camera-ready copy
+\newif\if@conferencefinal\@conferencefinalfalse
+\DeclareOption{final}{
+  \@conferencefinaltrue
+}
+
+% Declare the preprint option, which creates a camera-ready copy without the corl footnote
+\newif\if@preprinttype\@preprinttypefalse
+\DeclareOption{preprint}{
+  \@preprinttypetrue
+}
+
+% The natbib package is loaded by default. Declaring the nonatbib option, does not load natbib in case of package clash (users can pass options to natbib via \PassOptionsToPackage)
+\newif\if@natbib\@natbibtrue
+\DeclareOption{nonatbib}{
+  \@natbibfalse
+}
+
+% The hyperref package is loaded by default. Declaring the  nohyperref option, does not load the hyperref.
+\DeclareOption{nohyperref}{%
+  \gdef\nohyperref{1}
+}
+
+% Activate the options
+\ProcessOptions\relax
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Required packages:
+
+\RequirePackage{lineno}
+\RequirePackage{color}
+
+% Load natbib unless told otherwise
+\if@natbib
+  \RequirePackage[square,numbers]{natbib}
+  \bibliographystyle{corlabbrvnat}
+\fi
+
+% set page geometry
+\RequirePackage{hyperref}       % hyperlinks
+\RequirePackage[verbose=true,letterpaper]{geometry}
+\AtBeginDocument{
+  \newgeometry{
+    textheight=9in,
+    textwidth=5.5in,
+    top=1in,
+    headheight=12pt,
+    headsep=25pt,
+    footskip=30pt
+  }
+  \@ifpackageloaded{fullpage}
+    {\PackageWarning{corl_2026}{fullpage package not allowed! Overwriting formatting.}}
+    {}
+}
+
+
+\ifdefined\nohyperref\else\ifdefined\hypersetup
+  \definecolor{mydarkblue}{rgb}{0,0.08,0.45}
+  \hypersetup{ %
+    pdftitle={},
+    pdfauthor={},
+    pdfsubject={Proceedings of the \@conferenceordinal\/ Conference on Robot Learning (CoRL \@conferenceyear)},
+    pdfkeywords={},
+    pdfborder=0 0 0,
+    pdfpagemode=UseNone,
+    colorlinks=true,
+    linkcolor=mydarkblue,
+    citecolor=mydarkblue,
+    filecolor=mydarkblue,
+    urlcolor=mydarkblue,
+    pdfview=FitH}
+
+  \ifdefined\isaccepted \else
+    \hypersetup{pdfauthor={Anonymous Submission}}
+  \fi
+\fi\fi
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% 
+
+% fonts
+\renewcommand{\rmdefault}{ptm}
+\renewcommand{\sfdefault}{phv}
+
+% Create acknowledgments -- only if the option 'final' is activated
+\providecommand{\acknowledgments}{}
+\renewcommand{\acknowledgments}[1]{%
+\if@conferencefinal% 
+\subsubsection*{Acknowledgments} #1
+\fi
+\if@preprinttype% 
+\subsubsection*{Acknowledgments} #1
+\fi
+}
+
+
+% handle tweaks for camera-ready copy vs. submission copy
+\if@conferencefinal
+  \newcommand{\@noticestring}{%
+    \@conferenceordinal\/ Conference on Robot Learning
+    (CoRL \@conferenceyear), \@conferencelocation.%
+  }
+\else
+  \if@preprinttype
+    \newcommand{\@noticestring}{%
+      % Nothing here.
+    }
+  \else
+    \newcommand{\@noticestring}{%
+      Submitted to the \@conferenceordinal\/ Conference on Robot Learning (CoRL \@conferenceyear). Do not distribute.%
+    }
+
+    % line numbers for submission
+    \linenumbers
+
+    % fix incompatibilities between lineno and amsmath, if required, by
+    % transparently wrapping linenomath environments around amsmath
+    % environments
+    \AtBeginDocument{%
+     \@ifpackageloaded{amsmath}{%
+        \newcommand*\patchAmsMathEnvironmentForLineno[1]{%
+          \expandafter\let\csname old#1\expandafter\endcsname\csname #1\endcsname
+          \expandafter\let\csname oldend#1\expandafter\endcsname\csname end#1\endcsname
+          \renewenvironment{#1}%
+            {\linenomath\csname old#1\endcsname}%
+            {\csname oldend#1\endcsname\endlinenomath}%
+        }%
+        \newcommand*\patchBothAmsMathEnvironmentsForLineno[1]{%
+          \patchAmsMathEnvironmentForLineno{#1}%
+          \patchAmsMathEnvironmentForLineno{#1*}%
+        }%
+        \patchBothAmsMathEnvironmentsForLineno{equation}%
+        \patchBothAmsMathEnvironmentsForLineno{align}%
+        \patchBothAmsMathEnvironmentsForLineno{flalign}%
+        \patchBothAmsMathEnvironmentsForLineno{alignat}%
+        \patchBothAmsMathEnvironmentsForLineno{gather}%
+        \patchBothAmsMathEnvironmentsForLineno{multline}%
+      }{}
+    }
+  \fi
+\fi
+
+
+
+% The DOI will now automatically generate a URL link. Pretty cool!
+% + Fix for hyperref and DOI (https://www.tug.org/pipermail/tex-live/2012-August/032161.html)
+%-----------------------------
+\makeatletter
+\providecommand{\doi}[1]{%
+  \begingroup
+    \let\bibinfo\@secondoftwo
+    \urlstyle{rm}%
+    \href{http://dx.doi.org/#1}{%
+      doi:\discretionary{}{}{}%
+      \nolinkurl{#1}%
+    }%
+  \endgroup
+}
+% \makeatother
+% %-----------------------------
+
+\widowpenalty=10000
+\clubpenalty=10000
+\flushbottom
+\sloppy
+
+% font sizes with reduced leading
+\renewcommand{\normalsize}{%
+  \@setfontsize\normalsize\@xpt\@xipt
+  \abovedisplayskip      7\p@ \@plus 2\p@ \@minus 5\p@
+  \abovedisplayshortskip \z@ \@plus 3\p@
+  \belowdisplayskip      \abovedisplayskip
+  \belowdisplayshortskip 4\p@ \@plus 3\p@ \@minus 3\p@
+}
+\normalsize
+\renewcommand{\small}{%
+  \@setfontsize\small\@ixpt\@xpt
+  \abovedisplayskip      6\p@ \@plus 1.5\p@ \@minus 4\p@
+  \abovedisplayshortskip \z@  \@plus 2\p@
+  \belowdisplayskip      \abovedisplayskip
+  \belowdisplayshortskip 3\p@ \@plus 2\p@   \@minus 2\p@
+}
+\renewcommand{\footnotesize}{\@setfontsize\footnotesize\@ixpt\@xpt}
+\renewcommand{\scriptsize}{\@setfontsize\scriptsize\@viipt\@viiipt}
+\renewcommand{\tiny}{\@setfontsize\tiny\@vipt\@viipt}
+\renewcommand{\large}{\@setfontsize\large\@xiipt{14}}
+\renewcommand{\Large}{\@setfontsize\Large\@xivpt{16}}
+\renewcommand{\LARGE}{\@setfontsize\LARGE\@xviipt{20}}
+\renewcommand{\huge}{\@setfontsize\huge\@xxpt{23}}
+\renewcommand{\Huge}{\@setfontsize\Huge\@xxvpt{28}}
+
+% sections with less space
+\providecommand{\section}{}
+\renewcommand{\section}{%
+  \@startsection{section}{1}{\z@}%
+                {-2.0ex \@plus -0.5ex \@minus -0.2ex}%
+                { 1.5ex \@plus  0.3ex \@minus  0.2ex}%
+                {\large\bf\raggedright}%
+}
+\providecommand{\subsection}{}
+\renewcommand{\subsection}{%
+  \@startsection{subsection}{2}{\z@}%
+                {-1.8ex \@plus -0.5ex \@minus -0.2ex}%
+                { 0.8ex \@plus  0.2ex}%
+                {\normalsize\bf\raggedright}%
+}
+\providecommand{\subsubsection}{}
+\renewcommand{\subsubsection}{%
+  \@startsection{subsubsection}{3}{\z@}%
+                {-1.5ex \@plus -0.5ex \@minus -0.2ex}%
+                { 0.5ex \@plus  0.2ex}%
+                {\normalsize\bf\raggedright}%
+}
+\providecommand{\paragraph}{}
+\renewcommand{\paragraph}{%
+  \@startsection{paragraph}{4}{\z@}%
+                {1.5ex \@plus 0.5ex \@minus 0.2ex}%
+                {-1em}%
+                {\normalsize\bf}%
+}
+\providecommand{\subparagraph}{}
+\renewcommand{\subparagraph}{%
+  \@startsection{subparagraph}{5}{\z@}%
+                {1.5ex \@plus 0.5ex \@minus 0.2ex}%
+                {-1em}%
+                {\normalsize\bf}%
+}
+\providecommand{\subsubsubsection}{}
+\renewcommand{\subsubsubsection}{%
+  \vskip5pt{\noindent\normalsize\rm\raggedright}%
+}
+
+% float placement
+\renewcommand{\topfraction      }{0.85}
+\renewcommand{\bottomfraction   }{0.4}
+\renewcommand{\textfraction     }{0.1}
+\renewcommand{\floatpagefraction}{0.7}
+
+\newlength{\@nipsabovecaptionskip}\setlength{\@nipsabovecaptionskip}{7\p@}
+\newlength{\@nipsbelowcaptionskip}\setlength{\@nipsbelowcaptionskip}{\z@}
+
+\setlength{\abovecaptionskip}{\@nipsabovecaptionskip}
+\setlength{\belowcaptionskip}{\@nipsbelowcaptionskip}
+
+% swap above/belowcaptionskip lengths for tables
+\renewenvironment{table}
+  {\setlength{\abovecaptionskip}{\@nipsbelowcaptionskip}%
+   \setlength{\belowcaptionskip}{\@nipsabovecaptionskip}%
+   \@float{table}}
+  {\end@float}
+
+% footnote formatting
+\setlength{\footnotesep }{6.65\p@}
+\setlength{\skip\footins}{9\p@ \@plus 4\p@ \@minus 2\p@}
+\renewcommand{\footnoterule}{\kern-3\p@ \hrule width 12pc \kern 2.6\p@}
+\setcounter{footnote}{0}
+
+% paragraph formatting
+\setlength{\parindent}{\z@}
+\setlength{\parskip  }{5.5\p@}
+
+% list formatting
+\setlength{\topsep       }{4\p@ \@plus 1\p@   \@minus 2\p@}
+\setlength{\partopsep    }{1\p@ \@plus 0.5\p@ \@minus 0.5\p@}
+\setlength{\itemsep      }{2\p@ \@plus 1\p@   \@minus 0.5\p@}
+\setlength{\parsep       }{2\p@ \@plus 1\p@   \@minus 0.5\p@}
+\setlength{\leftmargin   }{3pc}
+\setlength{\leftmargini  }{\leftmargin}
+\setlength{\leftmarginii }{2em}
+\setlength{\leftmarginiii}{1.5em}
+\setlength{\leftmarginiv }{1.0em}
+\setlength{\leftmarginv  }{0.5em}
+\def\@listi  {\leftmargin\leftmargini}
+\def\@listii {\leftmargin\leftmarginii
+              \labelwidth\leftmarginii
+              \advance\labelwidth-\labelsep
+              \topsep  2\p@ \@plus 1\p@    \@minus 0.5\p@
+              \parsep  1\p@ \@plus 0.5\p@ \@minus 0.5\p@
+              \itemsep \parsep}
+\def\@listiii{\leftmargin\leftmarginiii
+              \labelwidth\leftmarginiii
+              \advance\labelwidth-\labelsep
+              \topsep    1\p@ \@plus 0.5\p@ \@minus 0.5\p@
+              \parsep    \z@
+              \partopsep 0.5\p@ \@plus 0\p@ \@minus 0.5\p@
+              \itemsep \topsep}
+\def\@listiv {\leftmargin\leftmarginiv
+              \labelwidth\leftmarginiv
+              \advance\labelwidth-\labelsep}
+\def\@listv  {\leftmargin\leftmarginv
+              \labelwidth\leftmarginv
+              \advance\labelwidth-\labelsep}
+\def\@listvi {\leftmargin\leftmarginvi
+              \labelwidth\leftmarginvi
+              \advance\labelwidth-\labelsep}
+
+% create title
+\providecommand{\maketitle}{}
+\renewcommand{\maketitle}{%
+  \par
+  \begingroup
+    \renewcommand{\thefootnote}{\fnsymbol{footnote}}
+    % for perfect author name centering
+    \renewcommand{\@makefnmark}{\hbox to \z@{$^{\@thefnmark}$\hss}}
+    % The footnote-mark was overlapping the footnote-text,
+    % added the following to fix this problem               (MK)
+    \long\def\@makefntext##1{%
+      \parindent 1em\noindent
+      \hbox to 1.8em{\hss $\m@th ^{\@thefnmark}$}##1
+    }
+    \thispagestyle{empty}
+    \@maketitle
+    \@thanks
+    \@notice
+  \endgroup
+  \let\maketitle\relax
+  \let\thanks\relax
+}
+
+% rules for title box at top of first page
+\newcommand{\@toptitlebar}{
+  \hrule height 4\p@
+  \vskip 0.25in
+  \vskip -\parskip%
+}
+\newcommand{\@bottomtitlebar}{
+  \vskip 0.29in
+  \vskip -\parskip
+  \hrule height 1\p@
+  \vskip 0.09in%
+}
+
+%% keywords as first class citizens
+\def\keywords#1{%
+%  \ifdefined\isaccepted \else
+%    \par {\bf Keywords:} #1%
+%  \fi
+%  \ifdefined\nohyperref\else\ifdefined\hypersetup
+%    \hypersetup{pdfkeywords={#1}}
+%  \fi\fi
+ \ifdefined\isaccepted \else
+    \begin{quote}
+    \textbf{Keywords:} #1%
+    \end{quote}
+ \fi
+  \ifdefined\nohyperref\else\ifdefined\hypersetup
+    \hypersetup{pdfkeywords={#1}}
+  \fi\fi
+}
+
+
+% create title (includes both anonymized and non-anonymized versions)
+\providecommand{\@maketitle}{}
+\renewcommand{\@maketitle}{%
+  \vbox{%
+    \hsize\textwidth
+    \linewidth\hsize
+    \vskip 0.1in
+%     \@toptitlebar
+    \centering
+    {\LARGE\bf \@title\par}
+%     \@bottomtitlebar
+    \if@conferencefinal
+      \def\And{%
+        \end{tabular}\hfil\linebreak[0]\hfil%
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+      }
+      \def\AND{%
+        \end{tabular}\hfil\linebreak[4]\hfil%
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+      }
+      \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\@author\end{tabular}%
+    \else
+      \if@preprinttype
+        \def\And{%
+          \end{tabular}\hfil\linebreak[0]\hfil%
+          \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+        }
+        \def\AND{%
+          \end{tabular}\hfil\linebreak[4]\hfil%
+          \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+        }
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\@author\end{tabular}%
+      \else
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}
+          Anonymous Author(s) \\
+          Affiliation \\
+          Address \\
+          \texttt{email} \\
+        \end{tabular}%
+      \fi
+    \fi
+    \vskip 0.3in \@minus 0.1in
+  }
+}
+
+% add conference notice to bottom of first page
+\newcommand{\ftype@noticebox}{8}
+\newcommand{\@notice}{%
+  % give a bit of extra room back to authors on first page
+  \enlargethispage{2\baselineskip}%
+  \@float{noticebox}[b]%
+    \footnotesize\@noticestring%
+  \end@float%
+}
+
+% abstract styling
+\renewenvironment{abstract}%
+{%
+%   \vskip 0.075in%
+%   \centerline%
+%   {\large\bf Abstract}%
+%   \vspace{0.5ex}%
+  \begin{quote}%
+  \textbf{Abstract:}%
+}
+{
+  \par%
+  \vskip 1ex%
+%   \ifdefined\keywords
+%   \textbf{Keywords:}%
+%   \@keywords%
+%   \else
+%   \fi
+  \end{quote}%
+}
+
+\endinput
diff --git a/workspace/inputs/corl_2026_template_submission.zip b/workspace/inputs/corl_2026_template_submission.zip
new file mode 100644
index 0000000..feb701b
Binary files /dev/null and b/workspace/inputs/corl_2026_template_submission.zip differ
diff --git a/workspace/inputs/corlabbrvnat.bst b/workspace/inputs/corlabbrvnat.bst
new file mode 100644
index 0000000..eab7c3e
--- /dev/null
+++ b/workspace/inputs/corlabbrvnat.bst
@@ -0,0 +1,1442 @@
+%% File: `corlabbrvnat.bst'
+%% A modification of `abbrvnat.bst' to order the references by the order
+%% of appearance in the paper.
+%%
+%% Modified by Roberto Calandra 12/06/2017
+%% email: roberto.calandra@berkeley.edu
+%%
+%% -------------------
+%%
+%% Copyright 1993-2007 Patrick W Daly
+%% Max-Planck-Institut f\"ur Sonnensystemforschung
+%% Max-Planck-Str. 2
+%% D-37191 Katlenburg-Lindau
+%% Germany
+%% E-mail: daly@mps.mpg.de
+%%
+%% This program can be redistributed and/or modified under the terms
+%% of the LaTeX Project Public License Distributed from CTAN
+%% archives in directory macros/latex/base/lppl.txt; either
+%% version 1 of the License, or any later version.
+%%
+ % Version and source file information:
+ % \ProvidesFile{natbst.mbs}[2007/11/26 1.93 (PWD)]
+ %
+ % BibTeX `plainnat' family
+ %   version 0.99b for BibTeX versions 0.99a or later,
+ %   for LaTeX versions 2.09 and 2e.
+ %
+ % For use with the `natbib.sty' package; emulates the corresponding
+ %   member of the `plain' family, but with author-year citations.
+ %
+ % With version 6.0 of `natbib.sty', it may also be used for numerical
+ %   citations, while retaining the commands \citeauthor, \citefullauthor,
+ %   and \citeyear to print the corresponding information.
+ %
+ % For version 7.0 of `natbib.sty', the KEY field replaces missing
+ %   authors/editors, and the date is left blank in \bibitem.
+ %
+ % Includes field EID for the sequence/citation number of electronic journals
+ %  which is used instead of page numbers.
+ %
+ % Includes fields ISBN and ISSN.
+ %
+ % Includes field URL for Internet addresses.
+ %
+ % Includes field DOI for Digital Object Idenfifiers.
+ %
+ % Works best with the url.sty package of Donald Arseneau.
+ %
+ % Works with identical authors and year are further sorted by
+ %   citation key, to preserve any natural sequence.
+ %
+ENTRY
+  { address
+    author
+    booktitle
+    chapter
+    doi
+    eid
+    edition
+    editor
+    howpublished
+    institution
+    isbn
+    issn
+    journal
+    key
+    month
+    note
+    number
+    organization
+    pages
+    publisher
+    school
+    series
+    title
+    type
+    url
+    volume
+    year
+  }
+  {}
+  { label extra.label sort.label short.list }
+
+INTEGERS { output.state before.all mid.sentence after.sentence after.block }
+
+FUNCTION {init.state.consts}
+{ #0 'before.all :=
+  #1 'mid.sentence :=
+  #2 'after.sentence :=
+  #3 'after.block :=
+}
+
+STRINGS { s t }
+
+FUNCTION {output.nonnull}
+{ 's :=
+  output.state mid.sentence =
+    { ", " * write$ }
+    { output.state after.block =
+        { add.period$ write$
+          newline$
+          "\newblock " write$
+        }
+        { output.state before.all =
+            'write$
+            { add.period$ " " * write$ }
+          if$
+        }
+      if$
+      mid.sentence 'output.state :=
+    }
+  if$
+  s
+}
+
+FUNCTION {output}
+{ duplicate$ empty$
+    'pop$
+    'output.nonnull
+  if$
+}
+
+FUNCTION {output.check}
+{ 't :=
+  duplicate$ empty$
+    { pop$ "empty " t * " in " * cite$ * warning$ }
+    'output.nonnull
+  if$
+}
+
+FUNCTION {fin.entry}
+{ add.period$
+  write$
+  newline$
+}
+
+FUNCTION {new.block}
+{ output.state before.all =
+    'skip$
+    { after.block 'output.state := }
+  if$
+}
+
+FUNCTION {new.sentence}
+{ output.state after.block =
+    'skip$
+    { output.state before.all =
+        'skip$
+        { after.sentence 'output.state := }
+      if$
+    }
+  if$
+}
+
+FUNCTION {not}
+{   { #0 }
+    { #1 }
+  if$
+}
+
+FUNCTION {and}
+{   'skip$
+    { pop$ #0 }
+  if$
+}
+
+FUNCTION {or}
+{   { pop$ #1 }
+    'skip$
+  if$
+}
+
+FUNCTION {new.block.checka}
+{ empty$
+    'skip$
+    'new.block
+  if$
+}
+
+FUNCTION {new.block.checkb}
+{ empty$
+  swap$ empty$
+  and
+    'skip$
+    'new.block
+  if$
+}
+
+FUNCTION {new.sentence.checka}
+{ empty$
+    'skip$
+    'new.sentence
+  if$
+}
+
+FUNCTION {new.sentence.checkb}
+{ empty$
+  swap$ empty$
+  and
+    'skip$
+    'new.sentence
+  if$
+}
+
+FUNCTION {field.or.null}
+{ duplicate$ empty$
+    { pop$ "" }
+    'skip$
+  if$
+}
+
+FUNCTION {emphasize}
+{ duplicate$ empty$
+    { pop$ "" }
+    { "\emph{" swap$ * "}" * }
+  if$
+}
+
+INTEGERS { nameptr namesleft numnames }
+
+FUNCTION {format.names}
+{ 's :=
+  #1 'nameptr :=
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    { s nameptr "{f.~}{vv~}{ll}{, jj}" format.name$ 't :=
+      nameptr #1 >
+        { namesleft #1 >
+            { ", " * t * }
+            { numnames #2 >
+                { "," * }
+                'skip$
+              if$
+              t "others" =
+                { " et~al." * }
+                { " and " * t * }
+              if$
+            }
+          if$
+        }
+        't
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {format.key}
+{ empty$
+    { key field.or.null }
+    { "" }
+  if$
+}
+
+FUNCTION {format.authors}
+{ author empty$
+    { "" }
+    { author format.names }
+  if$
+}
+
+FUNCTION {format.editors}
+{ editor empty$
+    { "" }
+    { editor format.names
+      editor num.names$ #1 >
+        { ", editors" * }
+        { ", editor" * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.isbn}
+{ isbn empty$
+    { "" }
+    { new.block "ISBN " isbn * }
+  if$
+}
+
+FUNCTION {format.issn}
+{ issn empty$
+    { "" }
+    { new.block "ISSN " issn * }
+  if$
+}
+
+FUNCTION {format.url}
+{ url empty$
+    { "" }
+    { new.block "URL \url{" url * "}" * }
+  if$
+}
+
+FUNCTION {format.doi}
+{ doi empty$
+    { "" }
+    { new.block "\doi{" doi * "}" * }
+  if$
+}
+
+FUNCTION {format.title}
+{ title empty$
+    { "" }
+    { title "t" change.case$ }
+  if$
+}
+
+FUNCTION {format.full.names}
+{'s :=
+  #1 'nameptr :=
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    { s nameptr
+      "{vv~}{ll}" format.name$ 't :=
+      nameptr #1 >
+        {
+          namesleft #1 >
+            { ", " * t * }
+            {
+              numnames #2 >
+                { "," * }
+                'skip$
+              if$
+              t "others" =
+                { " et~al." * }
+                { " and " * t * }
+              if$
+            }
+          if$
+        }
+        't
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {author.editor.full}
+{ author empty$
+    { editor empty$
+        { "" }
+        { editor format.full.names }
+      if$
+    }
+    { author format.full.names }
+  if$
+}
+
+FUNCTION {author.full}
+{ author empty$
+    { "" }
+    { author format.full.names }
+  if$
+}
+
+FUNCTION {editor.full}
+{ editor empty$
+    { "" }
+    { editor format.full.names }
+  if$
+}
+
+FUNCTION {make.full.names}
+{ type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.full
+    { type$ "proceedings" =
+        'editor.full
+        'author.full
+      if$
+    }
+  if$
+}
+
+FUNCTION {output.bibitem}
+{ newline$
+  "\bibitem[" write$
+  label write$
+  ")" make.full.names duplicate$ short.list =
+     { pop$ }
+     { * }
+   if$
+  "]{" * write$
+  cite$ write$
+  "}" write$
+  newline$
+  ""
+  before.all 'output.state :=
+}
+
+FUNCTION {n.dashify}
+{ 't :=
+  ""
+    { t empty$ not }
+    { t #1 #1 substring$ "-" =
+        { t #1 #2 substring$ "--" = not
+            { "--" *
+              t #2 global.max$ substring$ 't :=
+            }
+            {   { t #1 #1 substring$ "-" = }
+                { "-" *
+                  t #2 global.max$ substring$ 't :=
+                }
+              while$
+            }
+          if$
+        }
+        { t #1 #1 substring$ *
+          t #2 global.max$ substring$ 't :=
+        }
+      if$
+    }
+  while$
+}
+
+FUNCTION {format.date}
+{ year duplicate$ empty$
+    { "empty year in " cite$ * warning$
+       pop$ "" }
+    'skip$
+  if$
+  month empty$
+    'skip$
+    { month
+      " " * swap$ *
+    }
+  if$
+  extra.label *
+}
+
+FUNCTION {format.btitle}
+{ title emphasize
+}
+
+FUNCTION {tie.or.space.connect}
+{ duplicate$ text.length$ #3 <
+    { "~" }
+    { " " }
+  if$
+  swap$ * *
+}
+
+FUNCTION {either.or.check}
+{ empty$
+    'pop$
+    { "can't use both " swap$ * " fields in " * cite$ * warning$ }
+  if$
+}
+
+FUNCTION {format.bvolume}
+{ volume empty$
+    { "" }
+    { "volume" volume tie.or.space.connect
+      series empty$
+        'skip$
+        { " of " * series emphasize * }
+      if$
+      "volume and number" number either.or.check
+    }
+  if$
+}
+
+FUNCTION {format.number.series}
+{ volume empty$
+    { number empty$
+        { series field.or.null }
+        { output.state mid.sentence =
+            { "number" }
+            { "Number" }
+          if$
+          number tie.or.space.connect
+          series empty$
+            { "there's a number but no series in " cite$ * warning$ }
+            { " in " * series * }
+          if$
+        }
+      if$
+    }
+    { "" }
+  if$
+}
+
+FUNCTION {format.edition}
+{ edition empty$
+    { "" }
+    { output.state mid.sentence =
+        { edition "l" change.case$ " edition" * }
+        { edition "t" change.case$ " edition" * }
+      if$
+    }
+  if$
+}
+
+INTEGERS { multiresult }
+
+FUNCTION {multi.page.check}
+{ 't :=
+  #0 'multiresult :=
+    { multiresult not
+      t empty$ not
+      and
+    }
+    { t #1 #1 substring$
+      duplicate$ "-" =
+      swap$ duplicate$ "," =
+      swap$ "+" =
+      or or
+        { #1 'multiresult := }
+        { t #2 global.max$ substring$ 't := }
+      if$
+    }
+  while$
+  multiresult
+}
+
+FUNCTION {format.pages}
+{ pages empty$
+    { "" }
+    { pages multi.page.check
+        { "pages" pages n.dashify tie.or.space.connect }
+        { "page" pages tie.or.space.connect }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.eid}
+{ eid empty$
+    { "" }
+    { "art." eid tie.or.space.connect }
+  if$
+}
+
+FUNCTION {format.vol.num.pages}
+{ volume field.or.null
+  number empty$
+    'skip$
+    { "\penalty0 (" number * ")" * *
+      volume empty$
+        { "there's a number but no volume in " cite$ * warning$ }
+        'skip$
+      if$
+    }
+  if$
+  pages empty$
+    'skip$
+    { duplicate$ empty$
+        { pop$ format.pages }
+        { ":\penalty0 " * pages n.dashify * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.vol.num.eid}
+{ volume field.or.null
+  number empty$
+    'skip$
+    { "\penalty0 (" number * ")" * *
+      volume empty$
+        { "there's a number but no volume in " cite$ * warning$ }
+        'skip$
+      if$
+    }
+  if$
+  eid empty$
+    'skip$
+    { duplicate$ empty$
+        { pop$ format.eid }
+        { ":\penalty0 " * eid * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.chapter.pages}
+{ chapter empty$
+    'format.pages
+    { type empty$
+        { "chapter" }
+        { type "l" change.case$ }
+      if$
+      chapter tie.or.space.connect
+      pages empty$
+        'skip$
+        { ", " * format.pages * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.in.ed.booktitle}
+{ booktitle empty$
+    { "" }
+    { editor empty$
+        { "In " booktitle emphasize * }
+        { "In " format.editors * ", " * booktitle emphasize * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {empty.misc.check}
+{ author empty$ title empty$ howpublished empty$
+  month empty$ year empty$ note empty$
+  and and and and and
+  key empty$ not and
+    { "all relevant fields are empty in " cite$ * warning$ }
+    'skip$
+  if$
+}
+
+FUNCTION {format.thesis.type}
+{ type empty$
+    'skip$
+    { pop$
+      type "t" change.case$
+    }
+  if$
+}
+
+FUNCTION {format.tr.number}
+{ type empty$
+    { "Technical Report" }
+    'type
+  if$
+  number empty$
+    { "t" change.case$ }
+    { number tie.or.space.connect }
+  if$
+}
+
+FUNCTION {format.article.crossref}
+{ key empty$
+    { journal empty$
+        { "need key or journal for " cite$ * " to crossref " * crossref *
+          warning$
+          ""
+        }
+        { "In \emph{" journal * "}" * }
+      if$
+    }
+    { "In " }
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {format.book.crossref}
+{ volume empty$
+    { "empty volume in " cite$ * "'s crossref of " * crossref * warning$
+      "In "
+    }
+    { "Volume" volume tie.or.space.connect
+      " of " *
+    }
+  if$
+  editor empty$
+  editor field.or.null author field.or.null =
+  or
+    { key empty$
+        { series empty$
+            { "need editor, key, or series for " cite$ * " to crossref " *
+              crossref * warning$
+              "" *
+            }
+            { "\emph{" * series * "}" * }
+          if$
+        }
+        'skip$
+      if$
+    }
+    'skip$
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {format.incoll.inproc.crossref}
+{ editor empty$
+  editor field.or.null author field.or.null =
+  or
+    { key empty$
+        { booktitle empty$
+            { "need editor, key, or booktitle for " cite$ * " to crossref " *
+              crossref * warning$
+              ""
+            }
+            { "In \emph{" booktitle * "}" * }
+          if$
+        }
+        { "In " }
+      if$
+    }
+    { "In " }
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {article}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { journal emphasize "journal" output.check
+      eid empty$
+        { format.vol.num.pages output }
+        { format.vol.num.eid output }
+      if$
+      format.date "year" output.check
+    }
+    { format.article.crossref output.nonnull
+      eid empty$
+        { format.pages output }
+        { format.eid output }
+      if$
+    }
+  if$
+  format.issn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {book}
+{ output.bibitem
+  author empty$
+    { format.editors "author and editor" output.check
+      editor format.key output
+    }
+    { format.authors output.nonnull
+      crossref missing$
+        { "author and editor" editor either.or.check }
+        'skip$
+      if$
+    }
+  if$
+  new.block
+  format.btitle "title" output.check
+  crossref missing$
+    { format.bvolume output
+      new.block
+      format.number.series output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+    }
+    { new.block
+      format.book.crossref output.nonnull
+    }
+  if$
+  format.edition output
+  format.date "year" output.check
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {booklet}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  new.block
+  format.title "title" output.check
+  howpublished address new.block.checkb
+  howpublished output
+  address output
+  format.date output
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {inbook}
+{ output.bibitem
+  author empty$
+    { format.editors "author and editor" output.check
+      editor format.key output
+    }
+    { format.authors output.nonnull
+      crossref missing$
+        { "author and editor" editor either.or.check }
+        'skip$
+      if$
+    }
+  if$
+  new.block
+  format.btitle "title" output.check
+  crossref missing$
+    { format.bvolume output
+      format.chapter.pages "chapter and pages" output.check
+      new.block
+      format.number.series output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+    }
+    { format.chapter.pages "chapter and pages" output.check
+      new.block
+      format.book.crossref output.nonnull
+    }
+  if$
+  format.edition output
+  format.date "year" output.check
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {incollection}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { format.in.ed.booktitle "booktitle" output.check
+      format.bvolume output
+      format.number.series output
+      format.chapter.pages output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+      format.edition output
+      format.date "year" output.check
+    }
+    { format.incoll.inproc.crossref output.nonnull
+      format.chapter.pages output
+    }
+  if$
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {inproceedings}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { format.in.ed.booktitle "booktitle" output.check
+      format.bvolume output
+      format.number.series output
+      format.pages output
+      address empty$
+        { organization publisher new.sentence.checkb
+          organization output
+          publisher output
+          format.date "year" output.check
+        }
+        { address output.nonnull
+          format.date "year" output.check
+          new.sentence
+          organization output
+          publisher output
+        }
+      if$
+    }
+    { format.incoll.inproc.crossref output.nonnull
+      format.pages output
+    }
+  if$
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {conference} { inproceedings }
+
+FUNCTION {manual}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  new.block
+  format.btitle "title" output.check
+  organization address new.block.checkb
+  organization output
+  address output
+  format.edition output
+  format.date output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {mastersthesis}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  "Master's thesis" format.thesis.type output.nonnull
+  school "school" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {misc}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  title howpublished new.block.checkb
+  format.title output
+  howpublished new.block.checka
+  howpublished output
+  format.date output
+  format.issn output
+  format.url output
+  new.block
+  note output
+  fin.entry
+  empty.misc.check
+}
+
+FUNCTION {phdthesis}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.btitle "title" output.check
+  new.block
+  "PhD thesis" format.thesis.type output.nonnull
+  school "school" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {proceedings}
+{ output.bibitem
+  format.editors output
+  editor format.key output
+  new.block
+  format.btitle "title" output.check
+  format.bvolume output
+  format.number.series output
+  address output
+  format.date "year" output.check
+  new.sentence
+  organization output
+  publisher output
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {techreport}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  format.tr.number output.nonnull
+  institution "institution" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {unpublished}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  note "note" output.check
+  format.date output
+  format.url output
+  fin.entry
+}
+
+FUNCTION {default.type} { misc }
+
+
+MACRO {jan} {"Jan."}
+
+MACRO {feb} {"Feb."}
+
+MACRO {mar} {"Mar."}
+
+MACRO {apr} {"Apr."}
+
+MACRO {may} {"May"}
+
+MACRO {jun} {"June"}
+
+MACRO {jul} {"July"}
+
+MACRO {aug} {"Aug."}
+
+MACRO {sep} {"Sept."}
+
+MACRO {oct} {"Oct."}
+
+MACRO {nov} {"Nov."}
+
+MACRO {dec} {"Dec."}
+
+
+
+MACRO {acmcs} {"ACM Comput. Surv."}
+
+MACRO {acta} {"Acta Inf."}
+
+MACRO {cacm} {"Commun. ACM"}
+
+MACRO {ibmjrd} {"IBM J. Res. Dev."}
+
+MACRO {ibmsj} {"IBM Syst.~J."}
+
+MACRO {ieeese} {"IEEE Trans. Softw. Eng."}
+
+MACRO {ieeetc} {"IEEE Trans. Comput."}
+
+MACRO {ieeetcad}
+ {"IEEE Trans. Comput.-Aided Design Integrated Circuits"}
+
+MACRO {ipl} {"Inf. Process. Lett."}
+
+MACRO {jacm} {"J.~ACM"}
+
+MACRO {jcss} {"J.~Comput. Syst. Sci."}
+
+MACRO {scp} {"Sci. Comput. Programming"}
+
+MACRO {sicomp} {"SIAM J. Comput."}
+
+MACRO {tocs} {"ACM Trans. Comput. Syst."}
+
+MACRO {tods} {"ACM Trans. Database Syst."}
+
+MACRO {tog} {"ACM Trans. Gr."}
+
+MACRO {toms} {"ACM Trans. Math. Softw."}
+
+MACRO {toois} {"ACM Trans. Office Inf. Syst."}
+
+MACRO {toplas} {"ACM Trans. Prog. Lang. Syst."}
+
+MACRO {tcs} {"Theoretical Comput. Sci."}
+
+
+READ
+
+FUNCTION {sortify}
+{ purify$
+  "l" change.case$
+}
+
+INTEGERS { len }
+
+FUNCTION {chop.word}
+{ 's :=
+  'len :=
+  s #1 len substring$ =
+    { s len #1 + global.max$ substring$ }
+    's
+  if$
+}
+
+FUNCTION {format.lab.names}
+{ 's :=
+  s #1 "{vv~}{ll}" format.name$
+  s num.names$ duplicate$
+  #2 >
+    { pop$ " et~al." * }
+    { #2 <
+        'skip$
+        { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" =
+            { " et~al." * }
+            { " and " * s #2 "{vv~}{ll}" format.name$ * }
+          if$
+        }
+      if$
+    }
+  if$
+}
+
+FUNCTION {author.key.label}
+{ author empty$
+    { key empty$
+        { cite$ #1 #3 substring$ }
+        'key
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {author.editor.key.label}
+{ author empty$
+    { editor empty$
+        { key empty$
+            { cite$ #1 #3 substring$ }
+            'key
+          if$
+        }
+        { editor format.lab.names }
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {author.key.organization.label}
+{ author empty$
+    { key empty$
+        { organization empty$
+            { cite$ #1 #3 substring$ }
+            { "The " #4 organization chop.word #3 text.prefix$ }
+          if$
+        }
+        'key
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {editor.key.organization.label}
+{ editor empty$
+    { key empty$
+        { organization empty$
+            { cite$ #1 #3 substring$ }
+            { "The " #4 organization chop.word #3 text.prefix$ }
+          if$
+        }
+        'key
+      if$
+    }
+    { editor format.lab.names }
+  if$
+}
+
+FUNCTION {calc.short.authors}
+{ type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.key.label
+    { type$ "proceedings" =
+        'editor.key.organization.label
+        { type$ "manual" =
+            'author.key.organization.label
+            'author.key.label
+          if$
+        }
+      if$
+    }
+  if$
+  'short.list :=
+}
+
+FUNCTION {calc.label}
+{ calc.short.authors
+  short.list
+  "("
+  *
+  year duplicate$ empty$
+  short.list key field.or.null = or
+     { pop$ "" }
+     'skip$
+  if$
+  *
+  'label :=
+}
+
+FUNCTION {sort.format.names}
+{ 's :=
+  #1 'nameptr :=
+  ""
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    {
+      s nameptr "{vv{ } }{ll{ }}{  f{ }}{  jj{ }}" format.name$ 't :=
+      nameptr #1 >
+        {
+          "   "  *
+          namesleft #1 = t "others" = and
+            { "zzzzz" * }
+            { numnames #2 > nameptr #2 = and
+                { "zz" * year field.or.null * "   " * }
+                'skip$
+              if$
+              t sortify *
+            }
+          if$
+        }
+        { t sortify * }
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {sort.format.title}
+{ 't :=
+  "A " #2
+    "An " #3
+      "The " #4 t chop.word
+    chop.word
+  chop.word
+  sortify
+  #1 global.max$ substring$
+}
+
+FUNCTION {author.sort}
+{ author empty$
+    { key empty$
+        { "to sort, need author or key in " cite$ * warning$
+          ""
+        }
+        { key sortify }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {author.editor.sort}
+{ author empty$
+    { editor empty$
+        { key empty$
+            { "to sort, need author, editor, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { editor sort.format.names }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {author.organization.sort}
+{ author empty$
+    { organization empty$
+        { key empty$
+            { "to sort, need author, organization, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { "The " #4 organization chop.word sortify }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {editor.organization.sort}
+{ editor empty$
+    { organization empty$
+        { key empty$
+            { "to sort, need editor, organization, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { "The " #4 organization chop.word sortify }
+      if$
+    }
+    { editor sort.format.names }
+  if$
+}
+
+
+FUNCTION {presort}
+{ calc.label
+  label sortify
+  "    "
+  *
+  type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.sort
+    { type$ "proceedings" =
+        'editor.organization.sort
+        { type$ "manual" =
+            'author.organization.sort
+            'author.sort
+          if$
+        }
+      if$
+    }
+  if$
+  "    "
+  *
+  year field.or.null sortify
+  *
+  "    "
+  *
+  cite$
+  *
+  #1 entry.max$ substring$
+  'sort.label :=
+  sort.label *
+  #1 entry.max$ substring$
+  'sort.key$ :=
+}
+
+ITERATE {presort}
+
+% SORT
+
+STRINGS { longest.label last.label next.extra }
+
+INTEGERS { longest.label.width last.extra.num number.label }
+
+FUNCTION {initialize.longest.label}
+{ "" 'longest.label :=
+  #0 int.to.chr$ 'last.label :=
+  "" 'next.extra :=
+  #0 'longest.label.width :=
+  #0 'last.extra.num :=
+  #0 'number.label :=
+}
+
+FUNCTION {forward.pass}
+{ last.label label =
+    { last.extra.num #1 + 'last.extra.num :=
+      last.extra.num int.to.chr$ 'extra.label :=
+    }
+    { "a" chr.to.int$ 'last.extra.num :=
+      "" 'extra.label :=
+      label 'last.label :=
+    }
+  if$
+  number.label #1 + 'number.label :=
+}
+
+FUNCTION {reverse.pass}
+{ next.extra "b" =
+    { "a" 'extra.label := }
+    'skip$
+  if$
+  extra.label 'next.extra :=
+  extra.label
+  duplicate$ empty$
+    'skip$
+    { "{\natexlab{" swap$ * "}}" * }
+  if$
+  'extra.label :=
+  label extra.label * 'label :=
+}
+
+EXECUTE {initialize.longest.label}
+
+ITERATE {forward.pass}
+
+REVERSE {reverse.pass}
+
+FUNCTION {bib.sort.order}
+{ sort.label  'sort.key$ :=
+}
+
+ITERATE {bib.sort.order}
+
+% SORT
+
+FUNCTION {begin.bib}
+{   preamble$ empty$
+    'skip$
+    { preamble$ write$ newline$ }
+  if$
+  "\begin{thebibliography}{" number.label int.to.str$ * "}" *
+  write$ newline$
+  "\providecommand{\natexlab}[1]{#1}"
+  write$ newline$
+  "\providecommand{\url}[1]{\texttt{#1}}"
+  write$ newline$
+  "\expandafter\ifx\csname urlstyle\endcsname\relax"
+  write$ newline$
+  "  \providecommand{\doi}[1]{doi: #1}\else"
+  write$ newline$
+  "  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi"
+  write$ newline$
+}
+
+EXECUTE {begin.bib}
+
+EXECUTE {init.state.consts}
+
+ITERATE {call.type$}
+
+FUNCTION {end.bib}
+{ newline$
+  "\end{thebibliography}" write$ newline$
+}
+
+EXECUTE {end.bib}
diff --git a/workspace/inputs/example.bib b/workspace/inputs/example.bib
new file mode 100644
index 0000000..5c573da
--- /dev/null
+++ b/workspace/inputs/example.bib
@@ -0,0 +1,20 @@
+% This file was created with JabRef 2.10.
+% Encoding: UTF-8
+
+
+@Article{Gauss1857,
+  Title                    = {Theory of the motion of the heavenly bodies moving about the sun in conic sections},
+  Author                   = {Carl Friedrich Gauss and Charles Henry Davis},
+  Journal                  = {Gauss's Theoria Motus},
+  Year                     = {1857},
+  Number                   = {1},
+  Pages                    = {5--23},
+  Volume                   = {76}
+}
+
+@Book{Lagrange1788,
+  title		= {M{\'e}canique Analytique},
+  author	= {Joseph-Louis Lagrange},
+  publisher	= {Desaint, Paris},
+  year		= {1788}
+}
diff --git a/workspace/inputs/experimental_log.md b/workspace/inputs/experimental_log.md
new file mode 100644
index 0000000..ebc103e
--- /dev/null
+++ b/workspace/inputs/experimental_log.md
@@ -0,0 +1,92 @@
+# Experimental Log: iMF-AttnRes for Fast Vision-Language-Action Imitation Learning in RoboIMI
+
+## 1. Experimental Setup
+
+We evaluate imitation-learning policies in the RoboIMI simulated manipulation benchmark. The study focuses on two environments:
+
+- **socket_peg / socket-insert**: a socket peg insertion task.
+- **sim_transfer / object transfer**: a block/object transfer manipulation task.
+
+The central method is an iMF-AttnRes VLA policy that combines improved Mean Flow (iMF) training with Attention Residuals (AttnRes). The comparison set includes diffusion-policy-style DiT baselines, ACT, SmolVLA, and several ablations of the iMF-AttnRes architecture and horizon/execution settings. Each reported result is based on **100 simulation rollouts** unless otherwise stated.
+
+Reported metrics:
+
+- `avg_reward`: mean cumulative episode reward over 100 rollouts.
+- `median_reward`: median cumulative episode reward.
+- `avg_max_reward`: mean maximum per-episode reward statistic reported by the rollout evaluator.
+- `max_reward`: highest cumulative episode reward among 100 rollouts.
+- `nonzero_reward`: number of episodes with nonzero reward.
+- `success_like`: number of episodes whose max reward crosses the environment threshold (`max_reward > 4` for socket-insert; `max_reward >= 4` for sim_transfer, as recorded in the logs).
+- `avg_inference_fps`, `avg_control_fps`, and `avg_inference_time_ms`: rollout speed measurements. Some sim_transfer logs report FPS but not inference time in milliseconds.
+
+Hardware varies across runs and is encoded in run names: 5880g0/5880g1 denotes RTX 5880 Ada runs, l20g* denotes L20 runs, and 5090 denotes RTX 5090 runs. Because hardware differs across experiments, speed comparisons should be interpreted as practical rollout measurements rather than perfectly hardware-normalized measurements.
+
+## 2. Raw Numeric Data
+
+### Table 1: Socket peg insertion, 100-rollout performance and speed
+
+| run_name | method_family | horizon_or_steps | avg_reward | median_reward | avg_max_reward | max_reward | nonzero_reward | success_like | avg_inference_fps | avg_control_fps | avg_inference_time_ms |
+|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| socket-insert-imf-attnres-ph32-exec16-emb384-l12-infer1-unfreeze-step50k-roll1x10-5880g01-20260506-195806 | iMF-AttnRes | infer1, ph32, exec16, 50k | 1275.22 | 1490.5 | 3.12 | 2250.0 | 84/100 | 0/100 | 64.226 | 5.863 | 16.186 |
+| socket-insert-no-pretrain-ph16-emb384-l18-infer100-unfreeze-step150k-roll1x10-5090-20260506-193753 | Diffusion-style VLA | infer100, ph16, 150k | 861.53 | 668.0 | 2.58 | 2241.0 | 97/100 | 2/100 | 2.956 | 2.591 | 338.291 |
+| socket-insert-no-pretrain-ph32-exec16-emb384-l18-infer100-unfreeze-step150k-roll1x10-5880g1-20260507-092227 | Diffusion-style VLA | infer100, ph32, exec16, 150k | 1019.39 | 804.0 | 2.47 | 2385.0 | 92/100 | 4/100 | 2.516 | 1.928 | 397.912 |
+| socket-insert-imf-attnres-ph32-exec16-emb384-l12-infer2-drop005-unfreeze-step150k-roll1x10-5880g01-20260508-165022 | iMF-AttnRes | infer2, ph32, exec16, 150k | 1472.62 | 1825.0 | 3.29 | 2365.0 | 83/100 | 5/100 | 71.579 | 7.181 | 14.409 |
+| socket-insert-imf-attnres-ph32-exec16-emb384-l12-infer3-drop005-unfreeze-step150k-roll1x10-5880g01-20260509-172423 | iMF-AttnRes | infer3, ph32, exec16, 150k | 1513.56 | 1901.5 | 3.28 | 2285.0 | 83/100 | 3/100 | 67.891 | 7.247 | 15.122 |
+| act-socket-peg-224-20260508-170237 | ACT | action chunking | 289.6 | 13.0 | 1.29 | 2091.0 | 55/100 | 1/100 | 10.5171 | 2.2782 | 100.2116 |
+| smolvla_socket_peg_bs80_100k_20260511_145727 | SmolVLA | 100k | 466.16 | 106.0 | 1.72 | 4.0 | 89/100 | 0/100 | 382.53 | 16.84 | 2.614 |
+
+### Table 2: Object transfer / sim_transfer, 100-rollout performance and speed
+
+| run_name | method_family | horizon_or_steps | avg_reward | median_reward | avg_max_reward | max_reward | nonzero_reward | success_like | avg_inference_fps | avg_control_fps | avg_inference_time_ms |
+|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
+| diffusion_policy_native_dit_ddpm_resnet_best | Diffusion Policy native DiT + DDPM + ResNet | best checkpoint | 319.2 | 6.0 | 1.68 | 1416.0 | 55/100 | 29/100 | 32.09 | 16.70 | n/a |
+| embed384_layer18_best_checkpoint | Diffusion-style VLA | emb384, layer18 | 233.52 | 0.0 | 1.12 | 1436.0 | 39/100 | 17/100 | 1.859 | 1.649 | n/a |
+| resnet18-multitoken-imf-emb256-l16-ph16-ex08-roll10-l20g2-20260406-112815 | iMF multi-token ResNet18 | step34999, ph16, exec08 | 260.66 | 0.0 | 1.12 | 1422.0 | 33/100 | 23/100 | 441.80 | 13.37 | n/a |
+| imf-p2-full-attnres-vision-ph16-ex08-emb384-l12-b40-lr1p25e4-ms50k-l20g3-20260405-002424 | iMF full AttnRes vision | ph16, exec08, 50k | 228.42 | 0.0 | 0.94 | 1482.0 | 31/100 | 16/100 | 55.997 | 4.301 | n/a |
+| imf-p1-ph16-ex16-emb384-l12-ms50k-l20g0-20260404-131223 | iMF-AttnRes DiT only | ph16, exec16, 50k | 240.64 | 0.0 | 1.02 | 1428.0 | 32/100 | 19/100 | 137.996 | 4.523 | n/a |
+| imf-p1-ph32-ex08-emb384-l12-ms50k-l20g1-20260404-131223 | iMF-AttnRes DiT only | ph32, exec08, 50k | 163.28 | 0.0 | 0.76 | 1566.0 | 26/100 | 12/100 | 85.638 | 4.317 | n/a |
+| imf-p1-ph32-ex32-emb384-l12-ms50k-5090-20260404-13122 | iMF-AttnRes DiT only | ph32, exec32, 50k | 260.72 | 0.0 | 1.18 | 1342.0 | 38/100 | 21/100 | 9.557 | 3.299 | n/a |
+| imf-p1-ph16-ex08-emb384-l12-ms50k-5880g1-20260404-131223 | iMF-AttnRes DiT only | ph16, exec08, 50k | 229.56 | 0.0 | 1.18 | 1564.0 | 41/100 | 18/100 | 69.984 | 8.053 | n/a |
+| imf-p1-ph08-ex08-emb384-l12-ms50k-5880g0-20260404-131223 | iMF-AttnRes DiT only | ph08, exec08, 50k | 237.02 | 0.0 | 1.32 | 1400.0 | 48/100 | 18/100 | 69.192 | 8.173 | n/a |
+| imf-p1-ph32-ex16-emb384-l12-ms50k-l20g2-20260404-131223 | iMF-AttnRes DiT only | ph32, exec16, 50k | 49.88 | 0.0 | 0.32 | 1284.0 | 13/100 | 4/100 | 138.903 | 4.482 | n/a |
+| sim-transfer-imf-attnres-ph32-exec16-emb384-l12-infer1-unfreeze-step50k-roll5x5-20260403-14130 | iMF-AttnRes | infer1, ph32, exec16, 50k | 526.22 | 86.0 | 2.14 | 1460.0 | 63/100 | 44/100 | 8.911 | 3.193 | n/a |
+
+### Table 3: Derived comparisons used in the paper draft
+
+| comparison | metric | numerator_run | denominator_run | numerator_value | denominator_value | derived_ratio_or_delta |
+|---|---|---|---|---:|---:|---:|
+| socket best iMF vs ph16 diffusion baseline | avg_reward ratio | iMF infer3 150k | diffusion ph16 infer100 150k | 1513.56 | 861.53 | 1.76x |
+| socket best iMF vs ph32 diffusion baseline | avg_reward ratio | iMF infer3 150k | diffusion ph32 infer100 150k | 1513.56 | 1019.39 | 1.48x |
+| socket best iMF vs ACT | avg_reward ratio | iMF infer3 150k | ACT | 1513.56 | 289.6 | 5.23x |
+| socket best iMF vs SmolVLA | avg_reward ratio | iMF infer3 150k | SmolVLA | 1513.56 | 466.16 | 3.25x |
+| socket iMF infer2 vs ph16 diffusion baseline | inference time reduction | iMF infer2 150k | diffusion ph16 infer100 150k | 14.409 | 338.291 | 23.48x lower latency |
+| socket iMF infer3 vs ph16 diffusion baseline | inference fps ratio | iMF infer3 150k | diffusion ph16 infer100 150k | 67.891 | 2.956 | 22.97x |
+| socket iMF infer2 vs ph32 diffusion baseline | inference fps ratio | iMF infer2 150k | diffusion ph32 infer100 150k | 71.579 | 2.516 | 28.45x |
+| sim_transfer best iMF vs native diffusion policy | avg_reward ratio | sim-transfer iMF infer1 | native diffusion policy | 526.22 | 319.2 | 1.65x |
+| sim_transfer best iMF vs native diffusion policy | success_like delta | sim-transfer iMF infer1 | native diffusion policy | 44 | 29 | +15 episodes |
+| sim_transfer ResNet18 multitoken iMF vs embed384 layer18 baseline | inference fps ratio | ResNet18 multitoken iMF | embed384 layer18 | 441.80 | 1.859 | 237.65x |
+
+## 3. Qualitative Observations
+
+Socket peg insertion shows the clearest quality-speed tradeoff improvement. The iMF-AttnRes variants with ph32/exec16 and only 1--3 inference evaluations achieve substantially higher average and median rewards than the diffusion-style infer100 baselines, while reducing latency from hundreds of milliseconds to roughly 14--16 ms. The infer2 and infer3 variants are the strongest socket policies by average reward, with the infer3 run obtaining avg_reward 1513.56 and median_reward 1901.5. The infer2 run has the fastest iMF-AttnRes socket latency among the high-reward variants at 14.409 ms.
+
+The socket experiments also reveal that nonzero reward alone is not a sufficient quality metric. The diffusion-style ph16 baseline has 97/100 nonzero episodes but much lower median reward than the best iMF-AttnRes runs. Conversely, iMF-AttnRes has 83/100 nonzero episodes but much higher median and average reward, suggesting that when it engages successfully with the task it produces stronger progress toward insertion.
+
+SmolVLA is extremely fast in inference FPS and control FPS, but its reward is lower than iMF-AttnRes in these experiments. ACT is slower than SmolVLA and underperforms iMF-AttnRes in average and median reward.
+
+In sim_transfer, the best recorded iMF-AttnRes run, `sim-transfer-imf-attnres-ph32-exec16-emb384-l12-infer1-unfreeze-step50k-roll5x5-20260403-14130`, reaches avg_reward 526.22 and 44/100 success-like episodes, exceeding the native diffusion-policy DiT/DDPM/ResNet baseline with avg_reward 319.2 and 29/100 success-like episodes. However, several iMF ablations underperform the native diffusion-policy baseline on average reward, indicating that the method is sensitive to horizon/execution choices, vision-token design, and whether AttnRes is applied only in the policy transformer or throughout the vision stack.
+
+For sim_transfer, the ResNet18 multi-token iMF variant is a particularly fast policy at 441.80 inference FPS but does not match the best iMF-AttnRes reward. The full-AttnRes vision variant underperforms the more conservative DiT-only AttnRes settings, suggesting that replacing residual pathways inside the vision encoder may be harmful or require different training hyperparameters.
+
+### Iteration History
+
+1. Early sim_transfer experiments compared diffusion-style baselines against initial iMF-AttnRes policy variants and explored policy horizon/execution length combinations.
+2. Subsequent ablations tested multi-token ResNet18 visual encoding, full AttnRes in the vision backbone, and DiT-only AttnRes. These results showed that iMF speedups are robust but reward quality is sensitive to model and horizon choices.
+3. Later socket-insert experiments scaled to 100-rollout evaluation and compared iMF-AttnRes against diffusion-style VLA, ACT, and SmolVLA. These experiments provided the strongest evidence for the paper's main claim: iMF-AttnRes can substantially reduce inference latency while improving or preserving manipulation reward.
+
+## 4. Caveats for Paper Writing
+
+- Do not claim real-robot validation; all reported experiments are simulation rollouts.
+- Hardware differs across runs, so speed measurements are useful but not fully hardware-normalized.
+- Success-like episode counts use thresholds as recorded by each environment's evaluation script: socket-insert logs report `max_reward > 4`, whereas sim_transfer logs report `max_reward >= 4`.
+- The reported data are sufficient for a first paper draft but should be supplemented later with controlled hardware-normalized speed measurements and more seeds if the paper is prepared for formal submission.
diff --git a/workspace/inputs/idea.md b/workspace/inputs/idea.md
new file mode 100644
index 0000000..171113f
--- /dev/null
+++ b/workspace/inputs/idea.md
@@ -0,0 +1,73 @@
+本文的主要idea是使用imf和attnres来加速流匹配的推理速度，同时保持相近的推理质量，imf来自于计算机视觉中的he kaiming的研究Improved mean flows: On the challenges of fastforward generative models,之前是用于流匹配图像生成模型的改进，其前述相关研究如下
+## MeanFlow [@geng2025meanflow]
+Diffusion的生成模型是基于一步一步的方式生成的，这显然不够快，对flow matching中的核心ODE公式
+$$\frac{dx_t}{dt} = v(x_t, t)$$
+这里提一下在看这篇文章时突然产生的对微分算子$d$的理解，$x_t$表示的是某一时刻的具体位置，我们想知道这一时刻的瞬时速度，要根据速度的计算公式进行计算，速度的计算公式是位移时间比，但$x_t$表示的是某一时刻的具体位置，不是一段时间的位移，因此我们使用微分算子表示在$x_t$附近取很小的一段位移除以很小的一段时间，这里一定注意加入微分算子前后的含义变化，对$t$来说加入微分算子前表示的是具体的时刻$t$，而加入后表示的是一段时间$t$。如果取的足够小，通过这种方式计算出来的一小段内的速度就约等于瞬时速度，这是微分算子的作用。
+回到ODE公式本身，可以看出这个公式其实计算的是每一时刻的瞬时速度，而原本的flow matching需要多步采样就来自这个时刻的瞬时性，我们得到瞬时速度后是通过一种近似采样的方式向前移动，是把这个瞬时速度视为一段时间内的平均速度，这也是为什么一开始的模型采样步数多效果就好，因为采样步数太少会导致这种估计非常不准确，使得路径偏移严重。为了解决这种问题，Rectified Flow可译为重整流直接将路径假设为一条直线，注意此处的直线是一种假设，我们只是希望模型能够学会按照这条直线去走，为了能够让模型学会尽可能的按照直线去走，我们在计算损失时让模型预测的速度尽可能接近直线路径的速率，另一方面还通过重整修正，让训练好的但预测路径还不那么直的模型继续学习更直的路线（通过让第一次训练好的模型从任意起点出发得到一个目标点后学习二者间的直线来重整）。
+尽管如此，实际实验中发现，模型学习的路线仍然不可能是我们预期中的直线，因此，这种假设可能本身存在一定的问题，真实的分布转移路径可能并不直，那是否有办法让模型可以学习到真实的平均速度而不是基于路径直线这一假设的平均速度呢？
+回到平均速度的定义，平均速度是位移除以时间，那么任意两个时间点之间的平均速度是
+$$\bar{v}(z_t, r, t) = \frac{1}{t-r} \int_r^t v(z_\tau, \tau)d\tau$$
+我们训练模型去拟合这个平均速度，但写出损失函数后可以发现，我们并不知道这个平均速度的真实值（因为没有假设条件了，因此路径可能是任意的）。但MeanFlow提出，可以通过构建平均速度和瞬时速度的关系来获得真实的平均速度。推导如下
+$$\begin{aligned} & \bar{v}(z_t, r, t) = \frac{1}{t-r} \int_r^t v(z_\tau, \tau) d\tau \\ \Leftrightarrow \quad & (t - r)\bar{v}(z_t, r, t) = \int_r^t v(z_\tau, \tau) d\tau \\ \Leftrightarrow \quad & \frac{d}{dt} \left[ (t - r)\bar{v}(z_t, r, t) \right] = \frac{d}{dt} \int_r^t v(z_\tau, \tau) d\tau \\ \Leftrightarrow \quad & \bar{v}(z_t, r, t) + (t - r)\frac{d}{dt}\bar{v}(z_t, r, t) = v(z_t, t) \\ \Leftrightarrow \quad & \bar{v}(z_t, r, t) = v(z_t, t) - (t - r)\frac{d}{dt}\bar{v}(z_t, r, t) \end{aligned} \tag{1}$$
+其中
+$$\frac{d}{dt}\bar{v}(z_t, r, t) = \frac{d\bar{v}}{dz} \cdot \frac{dz}{dt} + \frac{d\bar{v}}{dr} \cdot \frac{dr}{dt} + \frac{d\bar{v}}{dt} \cdot \frac{dt}{dt} = \frac{d\bar{v}}{dz} \cdot v(z_t, t) + \frac{d\bar{v}}{dt}$$
+$$= jvp(\bar{v}, (z, r, t), (v, 0, 1))$$
+最后可得
+$$\bar{v}(z_t, r, t) = v(z_t, t) - (t - r)jvp(\bar{v}, (z, r, t), (v, 0, 1)) \tag{2}$$
+这个表达式是自举的，即右式中包含左式，左右相关。式中的瞬时速度$v$我们用条件速度$\epsilon-x_0$代替，结合如下的损失函数
+$$L = \mathbb{E}\|\bar{v}_\theta(z_t, r, t) - sg(v(z_t, t) - (t - r)jvp(\bar{v}_\theta, (z, r, t), (v, 0, 1)))\|_2^2$$
+我们可以发现，这个损失函数表示的是一种自我纠正，即我们已知起始和终止位置的情况下的条件瞬时速度始终是$v$，模型预测的平均速度是空间中的宏观流，表示在$z$位置从时间$r$到$t$，平均的流向和平均流向的变化率是什么样的，我们用这个特定样本的瞬时速度校正这个平均速度应该是什么样的，和模型预测的平均速度的差值，最终希望的是模型在这一点的平均速度能够逆推出该特定样本的特定瞬时速度。
+我们此时会考虑这样一个问题，既然我们想要让模型预测平均速度，为何不直接让模型去拟合直线平均速度$\epsilon-x$，而是费劲进行表达式的推导呢。
+原因在于上式中的瞬时速度，从模型最终推理的角度来说，应该是边缘速度场，我们现在考虑的是真实的生成过程中的瞬时速度和平均速度的关系，而边缘速度场不可能是平坦的，因此不能将平均速度场简单的认为和条件速度一致。训练算法如下
+此处模型学会的平均速度不再是一个固定值，而是虽然变化但其变化能符合我们特定样本的条件瞬时速度的平均速度。
+尽管如此，这部分理解起来仍然怪怪的，并不是十分有说服力。这大概也是后续improved meanflow中提出改进的原因。
+可得到训练算法：
+![](https://pic1.imgdb.cn/item/69ab8e9959f896a650d454ac.png)
+其中和flow matching的主要区别在于加入了雅可比向量积的计算，引入了约16%的额外计算量，但得到的好处是生成时只需一步生成。
+
+## iMF [@gengImprovedMeanFlows2025]
+本篇文章是对MeanFlow的改进工作，MeanFlow的推导过程中对平均速度表达式中的瞬时速度都直接使用条件速度替代，实际上原表达式中的所有瞬时速度都应该是边际速度（边缘概率）而不应该是条件速度，在Flow Matching训练时使用条件速度的原因是条件速度的期望和边际速度二者的梯度是相同的，仅相差常数。
+但在式2中，jvp内的瞬时速度如果使用条件速度，jvp表达式是一个复杂的非线性表达式，此处的瞬时速度本应该使用边际速度，但这里使用条件速度进行替代，而非线性表达式的特性导致该表达式的期望的梯度并不和使用边际速度时的梯度相同，这就导致训练不稳定。
+因此可以对式1进行简单变形，得到瞬时速度和平均速度之间的关系，如下
+$$\mathbf{v}(z_t) = \mathbf{u}(z_t) + (t - r)\frac{d}{dt}\mathbf{u}(z_t) $$
+$$\mathbf{V}_\theta(z_t) \triangleq \mathbf{u}_\theta(z_t) + (t - r)JVP_{sg}(\mathbf{u}_\theta; \mathbf{v}_\theta) \quad (12)$$
+这里$v$和$t$使用同一个网络预测，时刻记住meanflow中预测平均速度的网络$u$的输入是$t,r,z_t$三个参数。当$r=t$时，网络预测的就相当于瞬时速度，因此使用同一个网络预测两遍分别预测$v$和$t$，根据式12得到修正后的瞬时速度，让这个修正后的瞬时速度尽可能接近在直线路径假设下的条件速度$\epsilon-x$。
+这里我们已经如果能让网络直接预测$v_\theta$为什么还要通过式12来计算这个瞬时速度呢，这里要结合我们的训练过程考虑，我们的最终目的仍然和MeanFlow一致，即让网络可以预测平均速度，这里我们看似是在计算瞬时速度的损失，实际上该瞬时速度是由式12的右式得来的，而且jvp不参与梯度计算即其中模型预测的瞬时速度$v_\theta$不会产生梯度更新，梯度更新仅发生在前面的$u_\theta(z_t)$中，因此我们实际上是在要求网络学会预测平均速度$u_\theta$。
+通过这种改进，我们可以充分利用原本的Flow Matching的训练稳定性。
+作者还发现，通过去掉adaLN-zero，但使用将同一个条件token重复多次并作为序列的一部分的形式来进行建模，可以达到和使用adaLN-zero一样的效果。
+![](https://pic1.imgdb.cn/item/69c4de9d2fb41b9ea32f5f12.png)
+但之前都是用于计算机视觉，我们将其用于机器人的模仿学习和动作生成，之前的类似研究是 Mean-Flow based One-Step Vision-Language-Action 为CVPR2026。但这篇文章只使用了meanflow，我们的improved meanflow改进了meanflow中存在的一些理论问题，同时获得更稳定的训练效果，除此以外我们增加了attnres用于解决模型训练过程中的梯度消失和深层网络特征非常相似的问题。attnres的理论来源于大模型训练过程Residual connections [12] with PreNorm [60] are standard in modern LLMs, yet they accumulate
+all layer outputs with fixed unit weights. This uniform aggregation causes uncontrolled hidden-state
+growth with depth, progressively diluting each layer’s contribution [27]. We propose Attention
+Residuals (AttnRes), which replaces this fixed accumulation with softmax attention over preceding
+layer outputs, allowing each layer to selectively aggregate earlier representations with learned, input-
+dependent weights. To address the memory and communication overhead of attending over all
+preceding layer outputs for large-scale model training, we introduce Block AttnRes, which partitions
+layers into blocks and attends over block-level representations, reducing the memory footprint while
+preserving most of the gains of full AttnRes. Combined with cache-based pipeline communication
+and a two-phase computation strategy, Block AttnRes becomes a practical drop-in replacement for
+standard residual connections with minimal overhead.
+Scaling law experiments confirm that the improvement is consistent across model sizes, and ablations
+validate the benefit of content-dependent depth-wise selection. We further integrate AttnRes into
+the Kimi Linear architecture [69] (48B total / 3B activated parameters) and pre-train on 1.4T tokens,
+where AttnRes mitigates PreNorm dilution, yielding more uniform output magnitudes and gradient
+distribution across depth, and improves downstream performance across all evaluated tasks 。我的概述如下 这里我们换另外一种写法，它能让我们看出更深刻的东西。先记 $\boldsymbol{y}_t = \boldsymbol{f}_t(\boldsymbol{x}_{t-1})$，那么有 $\boldsymbol{x}_t = \boldsymbol{x}_{t-1} + \boldsymbol{y}_t$，约定 $\boldsymbol{y}_0 = \boldsymbol{x}_0$，那么易得 $\boldsymbol{x}_t = \boldsymbol{y}_0 + \boldsymbol{y}_1 + \cdots + \boldsymbol{y}_t$，于是它可以等价地写成
+$$
+\boldsymbol{y}_{t+1} = \boldsymbol{f}_{t+1}(\boldsymbol{y}_0 + \boldsymbol{y}_1 + \cdots + \boldsymbol{y}_t) \tag{2}
+$$
+即从 $\boldsymbol{y}$ 的视角看，Residuals是将 $\boldsymbol{y}_0, \boldsymbol{y}_1, \cdots, \boldsymbol{y}_t$ 等权求和作为 $\boldsymbol{f}_{t+1}$ 的输入来得到 $\boldsymbol{y}_{t+1}$，那么一个自然的推广就是换成加权求和：
+$$
+\boldsymbol{y}_{t+1} = \boldsymbol{f}_{t+1}\left(\sum_{s=0}^t a_{t+1,s} \boldsymbol{y}_s\right) \qquad \text{where} \quad a_{t,s} \ge 0, \quad \sum_{s=0}^t a_{t+1,s} = 1 \tag{3}
+$$
+此时我们可以发现Hyper-connection就是这种加权求和的一种表现形式。但Hyper-connection中的$H$都由$tanh$激活后连乘得来，这导致该值有爆炸或者坍缩的风险。
+Deepseek的mHC通过引入对H的交替归一化使其满足双随机性，双随机性即矩阵的所有行列和都为1,而且双随机性具有乘法的封闭性，即双随机矩阵相乘还是双随机矩阵，这样使得H的连乘不会出现爆炸和坍缩的问题。
+回过头去考虑式3,既然是加权求和，那么一个权重的设置方法就是使用注意力机制，让权重$a$等于注意力分数，于是就有了AttnRes（苏神的作品）。其形式数学上看起来并不困难（RoPE也是这样）。
+$$
+a_{t+1,s} \propto \exp(\boldsymbol{w}_{t+1} \cdot \boldsymbol{y}_s) \tag{6}
+$$
+其中 $\boldsymbol{w}_t$ 是一个可训练的向量参数，即直接以一个数据无关的静态向量为Q、而K、V都是 $\boldsymbol{y}_s$ 去做Softmax Attention，这便是第一版AttnRes。随后通过一些实验得到给K多加个RMSNorm的操作，能取得比较稳定的收益，这构成了终版的AttnRes形式
+$$
+a_{t+1,s} \propto \exp(\boldsymbol{w}_{t+1} \cdot \text{RMSNorm}(\boldsymbol{y}_s)) \tag{7}
+$$
+
+我们研究用于对比的模型是Diffusion Policy，和ACT，在我自己的roboimi仿真环境下 有sim_transfer和pocket_insert两个仿真环境，在这个两个仿真环境下我使用imf-attnres和diffusion policy进行了很多实验。之前的实验都是使用swanlab记录的，实验以socket-insert开头 在runs目录下 可以看到之前的实验记录 
diff --git a/workspace/inputs/template.tex b/workspace/inputs/template.tex
new file mode 100644
index 0000000..26273e6
--- /dev/null
+++ b/workspace/inputs/template.tex
@@ -0,0 +1,145 @@
+\documentclass{article}
+
+\usepackage{corl_2026} % Use this for the initial submission.
+% \usepackage[final]{corl_2026} % Uncomment for the camera-ready ``final'' version.
+% \usepackage[preprint]{corl_2026} % Uncomment for pre-prints (e.g., arxiv); This is like ``final'', but will remove the CORL footnote.
+
+\title{Formatting Instructions for CoRL 2026 Camera-Ready}
+
+% The \author macro works with any number of authors. There are two
+% commands used to separate the names and addresses of multiple
+% authors: \And and \AND.
+%
+% Using \And between authors leaves it to LaTeX to determine where to
+% break the lines. Using \AND forces a line break at that point. So,
+% if LaTeX puts 3 of 4 authors names on the first line, and the last
+% on the second line, try using \AND instead of \And before the third
+% author name.
+
+% NOTE: authors will be visible only in the camera-ready and preprint versions (i.e., when using the option 'final' or 'preprint'). 
+% 	For the initial submission the authors will be anonymized.
+
+\author{
+  Jane E.~Doe\\
+  Department of Electrical Engineering and Computer Sciences\\
+  University of California Berkeley 
+  United States\\
+  \texttt{janedoe@berkeley.edu} \\
+  %% examples of more authors
+  %% \And
+  %% Coauthor \\
+  %% Affiliation \\
+  %% Address \\
+  %% \texttt{email} \\
+  %% \AND
+  %% Coauthor \\
+  %% Affiliation \\
+  %% Address \\
+  %% \texttt{email} \\
+  %% \And
+  %% Coauthor \\
+  %% Affiliation \\
+  %% Address \\
+  %% \texttt{email} \\
+  %% \And
+  %% Coauthor \\
+  %% Affiliation \\
+  %% Address \\
+  %% \texttt{email} \\
+}
+
+
+\begin{document}
+\maketitle
+
+%===============================================================================
+
+\begin{abstract}
+    The purpose of this document is to provide both the basic paper template and submission guidelines. Abstracts should be a single paragraph, between 4--6 sentences long, ideally. Gross violations will trigger corrections at the camera-ready phase.
+\end{abstract}
+
+% Two or three meaningful keywords should be added here
+\keywords{CoRL, Robots, Learning} 
+
+%===============================================================================
+
+\section{Introduction}
+	
+    Submission to CoRL 2026 will be entirely electronic, via a web site (not email). Information about the submission process and \LaTeX{} templates are available on the conference web site at \url{https://corl.org/}. For camera ready submission, use the \texttt{final} option for the \texttt{\textbackslash usepackage} command. 
+
+%===============================================================================
+
+\section{Citations}
+\label{sec:citations}
+
+	Citations can be made using either \textbackslash citep\{\} or \textbackslash citet\{\}, depending from the appropriateness. To avoid the citation moving to the next line, it is often a good practice to replace the space before with a tilde (\~{}) character.
+	Example 1: ``CoRL is the best conference ever~\citep{Gauss1857}.''
+	Example 2: ``\citet{Lagrange1788} proved, both theoretically and numerically, that CoRL is the best conference ever.''
+	
+%===============================================================================
+
+\section{Experimental Results}
+\label{sec:result}
+
+	Nam dui ligula, fringilla a, euismod sodales, sollicitudin vel, wisi. Morbi auctor lorem non justo.
+	Nam lacus libero, pretium at, lobortis vitae, ultricies et, tellus. Donec aliquet, tortor sed accumsan
+	bibendum, erat ligula aliquet magna, vitae ornare odio metus a mi. Morbi ac orci et nisl hendrerit
+	mollis. 
+
+	Suspendisse ut massa. Cras nec ante. Pellentesque a nulla. Cum sociis natoque penatibus
+	et magnis dis parturient montes, nascetur ridiculus mus. Aliquam tincidunt urna. Nulla ullamcorper
+	vestibulum turpis. Pellentesque cursus luctus mauris.
+	
+	Nulla malesuada porttitor diam. Donec felis erat, congue non, volutpat at, tincidunt tristique, libero.
+	Vivamus viverra fermentum felis. Donec nonummy pellentesque ante. Phasellus adipiscing semper 	elit. 
+	Proin fermentum massa ac quam. Sed diam turpis, molestie vitae, placerat a, molestie nec, leo.
+	Maecenas lacinia. Nam ipsum ligula, eleifend at, accumsan nec, suscipit a, ipsum. Morbi blandit
+	ligula feugiat magna. Nunc eleifend consequat lorem. Sed lacinia nulla vitae enim. Pellentesque tin-
+	cidunt purus vel magna. Integer non enim. Praesent euismod nunc eu purus. Donec bibendum quam
+	in tellus. Nullam cursus pulvinar lectus. Donec et mi. Nam vulputate metus eu enim. Vestibulum
+	pellentesque felis eu massa.
+	Quisque ullamcorper placerat ipsum. Cras nibh. Morbi vel justo vitae lacus tincidunt ultrices. 
+	
+	Lorem
+	ipsum dolor sit amet, consectetuer adipiscing elit. In hac habitasse platea dictumst. Integer tempus
+	convallis augue. Etiam facilisis. Nunc elementum fermentum wisi. Aenean placerat. Ut imperdiet,
+	enim sed gravida sollicitudin, felis odio placerat quam, ac pulvinar elit purus eget enim. Nunc vitae
+	tortor. Proin tempus nibh sit amet nisl. Vivamus quis tortor vitae risus porta vehicula.
+
+%===============================================================================
+
+\section{Conclusion}
+\label{sec:conclusion}
+
+	Nam dui ligula, fringilla a, euismod sodales, sollicitudin vel, wisi. Morbi auctor lorem non justo.
+	Nam lacus libero, pretium at, lobortis vitae, ultricies et, tellus. Donec aliquet, tortor sed accumsan
+	bibendum, erat ligula aliquet magna, vitae ornare odio metus a mi. Morbi ac orci et nisl hendrerit
+	mollis. Suspendisse ut massa. Cras nec ante. Pellentesque a nulla. Cum sociis natoque penatibus
+	et magnis dis parturient montes, nascetur ridiculus mus. Aliquam tincidunt urna. Nulla ullamcorper
+	vestibulum turpis. Pellentesque cursus luctus mauris.
+	Nulla malesuada porttitor diam. Donec felis erat, congue non, volutpat at, tincidunt tristique, libero.
+	Vivamus viverra fermentum felis. Donec nonummy pellentesque ante. Phasellus adipiscing semper
+	elit. Proin fermentum massa ac quam. Sed diam turpis, molestie vitae, placerat a, molestie nec, leo.
+	Maecenas lacinia. Nam ipsum ligula, eleifend at, accumsan nec, suscipit a, ipsum. Morbi blandit
+	ligula feugiat magna. Nunc eleifend consequat lorem. Sed lacinia nulla vitae enim. Pellentesque tin-
+	cidunt purus vel magna. Integer non enim. Praesent euismod nunc eu purus. Donec bibendum quam
+	in tellus. Nullam cursus pulvinar lectus. Donec et mi. Nam vulputate metus eu enim. Vestibulum
+	pellentesque felis eu massa.
+	Quisque ullamcorper placerat ipsum. Cras nibh. Morbi vel justo vitae lacus tincidunt ultrices. Lorem
+	ipsum dolor sit amet, consectetuer adipiscing elit. In hac habitasse platea dictumst. Integer tempus
+	convallis augue. Etiam facilisis. Nunc elementum fermentum wisi. Aenean placerat. Ut imperdiet,
+	enim sed gravida sollicitudin, felis odio placerat quam, ac pulvinar elit purus eget enim. Nunc vitae
+	tortor. Proin tempus nibh sit amet nisl. Vivamus quis tortor vitae risus porta vehicula.
+
+%===============================================================================
+
+\clearpage
+% The acknowledgments are automatically included only in the final and preprint versions of the paper.
+\acknowledgments{If a paper is accepted, the final camera-ready version will (and probably should) include acknowledgments. All acknowledgments go at the end of the paper, including thanks to reviewers who gave useful comments, to colleagues who contributed to the ideas, and to funding agencies and corporate sponsors that provided financial support.}
+
+%===============================================================================
+
+% no \bibliographystyle is required, since the corl style is automatically used.
+\bibliography{example}  % .bib
+
+\end{document}
diff --git a/workspace/inputs/template_unzipped/corl_2026_template_submission/corl_2026.sty b/workspace/inputs/template_unzipped/corl_2026_template_submission/corl_2026.sty
new file mode 100644
index 0000000..1180459
--- /dev/null
+++ b/workspace/inputs/template_unzipped/corl_2026_template_submission/corl_2026.sty
@@ -0,0 +1,472 @@
+% File: corl_2026.sty 
+%
+% Latex templates for the Conference on Robot Learning (CoRL)
+%
+% This template is heavily inspired by the NeurIPS, ICML, ICLR and IEEE Transactions latex templates.
+% Hence we would like to thank: Roman Garnett and the previous mantainers of the NIPS style, Percy Liang and the previous mantainers of the ICML style, Hugo Larochelle for the ICLR style, and Michael Shell for the IEEE Transactions style.
+%
+% History:
+% 2017/04/16 - First revision by Roberto Calandra (roberto.calandra@berkeley.edu).
+%	       Main changes:
+%	       - The abstract is more compact compared to NeurIPS/ICML
+%	       - References are by default using natbib with squared numbers (e.g., [1])
+%	       - DOI fields from the bibtex are automatically converted to hyperlinks 
+%	       to the corresponding page
+%	       - acknowledgments are now a command, and the corresponding subsubsection is
+%	       automatically included only in the final version
+% 2017/06/12 - Modified to use corlabbrvnat.bst, which order the reference by order of appearance in the paper
+% 2017/06/13 - fixed typo
+% 2018/05/09 - Slightly modified for CoRL 2018 by Jun Morimoto (xmorimo@atr.jp)
+% 2019/01/28 - Slightly modified for CoRL 2019 by Jun Nakanishi (jnakanis@meijo-u.ac.jp)
+% 2020/02/02 - Slightly modified for CoRL 2020 by Cynthia Matuszek (cmat@umbc.edu)
+% 2020/08/19 - Added preprint option by Roberto Calandra (rcalandra@fb.com)
+% 2021/05/06 - Slightly modified for CoRL 2021 by Gerhard Neumann (gerhard.neumann@kit.edu)
+% 2022/03/09 - Slightly modified for CoRL 2022 by Minas Liarokapis (minas.liarokapis@auckland.ac.nz)
+% 2022/03/06 - Slightly modified for CoRL 2023 by Marc Toussaint (toussaint@tu-berlin.de)
+% 2024/03/26 - Slightly modified for CoRL 2024 by David Held (dheld@andrew.cmu.edu)
+% 2026/01/10 - Slightly modified for CoRL 2026 by Yoonchang Sung (yoonchang.sung@ntu.edu.sg)
+%
+% TODO: nohyperref is not working at the moment
+% 
+
+\NeedsTeXFormat{LaTeX2e}
+
+% Content to be changed from year to year
+\ProvidesPackage{corl_2026}[2026/08/15 CORL2026 submission/preprint/camera-ready style file]
+\newcommand{\@conferenceordinal}{10th}
+\newcommand{\@conferenceyear}{2026}
+\newcommand{\@conferencelocation}{Austin TX, USA}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Accepted options: [final,preprint,nonatbib,nohyperref]
+
+% Declare the final option, which creates camera-ready copy
+\newif\if@conferencefinal\@conferencefinalfalse
+\DeclareOption{final}{
+  \@conferencefinaltrue
+}
+
+% Declare the preprint option, which creates a camera-ready copy without the corl footnote
+\newif\if@preprinttype\@preprinttypefalse
+\DeclareOption{preprint}{
+  \@preprinttypetrue
+}
+
+% The natbib package is loaded by default. Declaring the nonatbib option, does not load natbib in case of package clash (users can pass options to natbib via \PassOptionsToPackage)
+\newif\if@natbib\@natbibtrue
+\DeclareOption{nonatbib}{
+  \@natbibfalse
+}
+
+% The hyperref package is loaded by default. Declaring the  nohyperref option, does not load the hyperref.
+\DeclareOption{nohyperref}{%
+  \gdef\nohyperref{1}
+}
+
+% Activate the options
+\ProcessOptions\relax
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% Required packages:
+
+\RequirePackage{lineno}
+\RequirePackage{color}
+
+% Load natbib unless told otherwise
+\if@natbib
+  \RequirePackage[square,numbers]{natbib}
+  \bibliographystyle{corlabbrvnat}
+\fi
+
+% set page geometry
+\RequirePackage{hyperref}       % hyperlinks
+\RequirePackage[verbose=true,letterpaper]{geometry}
+\AtBeginDocument{
+  \newgeometry{
+    textheight=9in,
+    textwidth=5.5in,
+    top=1in,
+    headheight=12pt,
+    headsep=25pt,
+    footskip=30pt
+  }
+  \@ifpackageloaded{fullpage}
+    {\PackageWarning{corl_2026}{fullpage package not allowed! Overwriting formatting.}}
+    {}
+}
+
+
+\ifdefined\nohyperref\else\ifdefined\hypersetup
+  \definecolor{mydarkblue}{rgb}{0,0.08,0.45}
+  \hypersetup{ %
+    pdftitle={},
+    pdfauthor={},
+    pdfsubject={Proceedings of the \@conferenceordinal\/ Conference on Robot Learning (CoRL \@conferenceyear)},
+    pdfkeywords={},
+    pdfborder=0 0 0,
+    pdfpagemode=UseNone,
+    colorlinks=true,
+    linkcolor=mydarkblue,
+    citecolor=mydarkblue,
+    filecolor=mydarkblue,
+    urlcolor=mydarkblue,
+    pdfview=FitH}
+
+  \ifdefined\isaccepted \else
+    \hypersetup{pdfauthor={Anonymous Submission}}
+  \fi
+\fi\fi
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% 
+
+% fonts
+\renewcommand{\rmdefault}{ptm}
+\renewcommand{\sfdefault}{phv}
+
+% Create acknowledgments -- only if the option 'final' is activated
+\providecommand{\acknowledgments}{}
+\renewcommand{\acknowledgments}[1]{%
+\if@conferencefinal% 
+\subsubsection*{Acknowledgments} #1
+\fi
+\if@preprinttype% 
+\subsubsection*{Acknowledgments} #1
+\fi
+}
+
+
+% handle tweaks for camera-ready copy vs. submission copy
+\if@conferencefinal
+  \newcommand{\@noticestring}{%
+    \@conferenceordinal\/ Conference on Robot Learning
+    (CoRL \@conferenceyear), \@conferencelocation.%
+  }
+\else
+  \if@preprinttype
+    \newcommand{\@noticestring}{%
+      % Nothing here.
+    }
+  \else
+    \newcommand{\@noticestring}{%
+      Submitted to the \@conferenceordinal\/ Conference on Robot Learning (CoRL \@conferenceyear). Do not distribute.%
+    }
+
+    % line numbers for submission
+    \linenumbers
+
+    % fix incompatibilities between lineno and amsmath, if required, by
+    % transparently wrapping linenomath environments around amsmath
+    % environments
+    \AtBeginDocument{%
+     \@ifpackageloaded{amsmath}{%
+        \newcommand*\patchAmsMathEnvironmentForLineno[1]{%
+          \expandafter\let\csname old#1\expandafter\endcsname\csname #1\endcsname
+          \expandafter\let\csname oldend#1\expandafter\endcsname\csname end#1\endcsname
+          \renewenvironment{#1}%
+            {\linenomath\csname old#1\endcsname}%
+            {\csname oldend#1\endcsname\endlinenomath}%
+        }%
+        \newcommand*\patchBothAmsMathEnvironmentsForLineno[1]{%
+          \patchAmsMathEnvironmentForLineno{#1}%
+          \patchAmsMathEnvironmentForLineno{#1*}%
+        }%
+        \patchBothAmsMathEnvironmentsForLineno{equation}%
+        \patchBothAmsMathEnvironmentsForLineno{align}%
+        \patchBothAmsMathEnvironmentsForLineno{flalign}%
+        \patchBothAmsMathEnvironmentsForLineno{alignat}%
+        \patchBothAmsMathEnvironmentsForLineno{gather}%
+        \patchBothAmsMathEnvironmentsForLineno{multline}%
+      }{}
+    }
+  \fi
+\fi
+
+
+
+% The DOI will now automatically generate a URL link. Pretty cool!
+% + Fix for hyperref and DOI (https://www.tug.org/pipermail/tex-live/2012-August/032161.html)
+%-----------------------------
+\makeatletter
+\providecommand{\doi}[1]{%
+  \begingroup
+    \let\bibinfo\@secondoftwo
+    \urlstyle{rm}%
+    \href{http://dx.doi.org/#1}{%
+      doi:\discretionary{}{}{}%
+      \nolinkurl{#1}%
+    }%
+  \endgroup
+}
+% \makeatother
+% %-----------------------------
+
+\widowpenalty=10000
+\clubpenalty=10000
+\flushbottom
+\sloppy
+
+% font sizes with reduced leading
+\renewcommand{\normalsize}{%
+  \@setfontsize\normalsize\@xpt\@xipt
+  \abovedisplayskip      7\p@ \@plus 2\p@ \@minus 5\p@
+  \abovedisplayshortskip \z@ \@plus 3\p@
+  \belowdisplayskip      \abovedisplayskip
+  \belowdisplayshortskip 4\p@ \@plus 3\p@ \@minus 3\p@
+}
+\normalsize
+\renewcommand{\small}{%
+  \@setfontsize\small\@ixpt\@xpt
+  \abovedisplayskip      6\p@ \@plus 1.5\p@ \@minus 4\p@
+  \abovedisplayshortskip \z@  \@plus 2\p@
+  \belowdisplayskip      \abovedisplayskip
+  \belowdisplayshortskip 3\p@ \@plus 2\p@   \@minus 2\p@
+}
+\renewcommand{\footnotesize}{\@setfontsize\footnotesize\@ixpt\@xpt}
+\renewcommand{\scriptsize}{\@setfontsize\scriptsize\@viipt\@viiipt}
+\renewcommand{\tiny}{\@setfontsize\tiny\@vipt\@viipt}
+\renewcommand{\large}{\@setfontsize\large\@xiipt{14}}
+\renewcommand{\Large}{\@setfontsize\Large\@xivpt{16}}
+\renewcommand{\LARGE}{\@setfontsize\LARGE\@xviipt{20}}
+\renewcommand{\huge}{\@setfontsize\huge\@xxpt{23}}
+\renewcommand{\Huge}{\@setfontsize\Huge\@xxvpt{28}}
+
+% sections with less space
+\providecommand{\section}{}
+\renewcommand{\section}{%
+  \@startsection{section}{1}{\z@}%
+                {-2.0ex \@plus -0.5ex \@minus -0.2ex}%
+                { 1.5ex \@plus  0.3ex \@minus  0.2ex}%
+                {\large\bf\raggedright}%
+}
+\providecommand{\subsection}{}
+\renewcommand{\subsection}{%
+  \@startsection{subsection}{2}{\z@}%
+                {-1.8ex \@plus -0.5ex \@minus -0.2ex}%
+                { 0.8ex \@plus  0.2ex}%
+                {\normalsize\bf\raggedright}%
+}
+\providecommand{\subsubsection}{}
+\renewcommand{\subsubsection}{%
+  \@startsection{subsubsection}{3}{\z@}%
+                {-1.5ex \@plus -0.5ex \@minus -0.2ex}%
+                { 0.5ex \@plus  0.2ex}%
+                {\normalsize\bf\raggedright}%
+}
+\providecommand{\paragraph}{}
+\renewcommand{\paragraph}{%
+  \@startsection{paragraph}{4}{\z@}%
+                {1.5ex \@plus 0.5ex \@minus 0.2ex}%
+                {-1em}%
+                {\normalsize\bf}%
+}
+\providecommand{\subparagraph}{}
+\renewcommand{\subparagraph}{%
+  \@startsection{subparagraph}{5}{\z@}%
+                {1.5ex \@plus 0.5ex \@minus 0.2ex}%
+                {-1em}%
+                {\normalsize\bf}%
+}
+\providecommand{\subsubsubsection}{}
+\renewcommand{\subsubsubsection}{%
+  \vskip5pt{\noindent\normalsize\rm\raggedright}%
+}
+
+% float placement
+\renewcommand{\topfraction      }{0.85}
+\renewcommand{\bottomfraction   }{0.4}
+\renewcommand{\textfraction     }{0.1}
+\renewcommand{\floatpagefraction}{0.7}
+
+\newlength{\@nipsabovecaptionskip}\setlength{\@nipsabovecaptionskip}{7\p@}
+\newlength{\@nipsbelowcaptionskip}\setlength{\@nipsbelowcaptionskip}{\z@}
+
+\setlength{\abovecaptionskip}{\@nipsabovecaptionskip}
+\setlength{\belowcaptionskip}{\@nipsbelowcaptionskip}
+
+% swap above/belowcaptionskip lengths for tables
+\renewenvironment{table}
+  {\setlength{\abovecaptionskip}{\@nipsbelowcaptionskip}%
+   \setlength{\belowcaptionskip}{\@nipsabovecaptionskip}%
+   \@float{table}}
+  {\end@float}
+
+% footnote formatting
+\setlength{\footnotesep }{6.65\p@}
+\setlength{\skip\footins}{9\p@ \@plus 4\p@ \@minus 2\p@}
+\renewcommand{\footnoterule}{\kern-3\p@ \hrule width 12pc \kern 2.6\p@}
+\setcounter{footnote}{0}
+
+% paragraph formatting
+\setlength{\parindent}{\z@}
+\setlength{\parskip  }{5.5\p@}
+
+% list formatting
+\setlength{\topsep       }{4\p@ \@plus 1\p@   \@minus 2\p@}
+\setlength{\partopsep    }{1\p@ \@plus 0.5\p@ \@minus 0.5\p@}
+\setlength{\itemsep      }{2\p@ \@plus 1\p@   \@minus 0.5\p@}
+\setlength{\parsep       }{2\p@ \@plus 1\p@   \@minus 0.5\p@}
+\setlength{\leftmargin   }{3pc}
+\setlength{\leftmargini  }{\leftmargin}
+\setlength{\leftmarginii }{2em}
+\setlength{\leftmarginiii}{1.5em}
+\setlength{\leftmarginiv }{1.0em}
+\setlength{\leftmarginv  }{0.5em}
+\def\@listi  {\leftmargin\leftmargini}
+\def\@listii {\leftmargin\leftmarginii
+              \labelwidth\leftmarginii
+              \advance\labelwidth-\labelsep
+              \topsep  2\p@ \@plus 1\p@    \@minus 0.5\p@
+              \parsep  1\p@ \@plus 0.5\p@ \@minus 0.5\p@
+              \itemsep \parsep}
+\def\@listiii{\leftmargin\leftmarginiii
+              \labelwidth\leftmarginiii
+              \advance\labelwidth-\labelsep
+              \topsep    1\p@ \@plus 0.5\p@ \@minus 0.5\p@
+              \parsep    \z@
+              \partopsep 0.5\p@ \@plus 0\p@ \@minus 0.5\p@
+              \itemsep \topsep}
+\def\@listiv {\leftmargin\leftmarginiv
+              \labelwidth\leftmarginiv
+              \advance\labelwidth-\labelsep}
+\def\@listv  {\leftmargin\leftmarginv
+              \labelwidth\leftmarginv
+              \advance\labelwidth-\labelsep}
+\def\@listvi {\leftmargin\leftmarginvi
+              \labelwidth\leftmarginvi
+              \advance\labelwidth-\labelsep}
+
+% create title
+\providecommand{\maketitle}{}
+\renewcommand{\maketitle}{%
+  \par
+  \begingroup
+    \renewcommand{\thefootnote}{\fnsymbol{footnote}}
+    % for perfect author name centering
+    \renewcommand{\@makefnmark}{\hbox to \z@{$^{\@thefnmark}$\hss}}
+    % The footnote-mark was overlapping the footnote-text,
+    % added the following to fix this problem               (MK)
+    \long\def\@makefntext##1{%
+      \parindent 1em\noindent
+      \hbox to 1.8em{\hss $\m@th ^{\@thefnmark}$}##1
+    }
+    \thispagestyle{empty}
+    \@maketitle
+    \@thanks
+    \@notice
+  \endgroup
+  \let\maketitle\relax
+  \let\thanks\relax
+}
+
+% rules for title box at top of first page
+\newcommand{\@toptitlebar}{
+  \hrule height 4\p@
+  \vskip 0.25in
+  \vskip -\parskip%
+}
+\newcommand{\@bottomtitlebar}{
+  \vskip 0.29in
+  \vskip -\parskip
+  \hrule height 1\p@
+  \vskip 0.09in%
+}
+
+%% keywords as first class citizens
+\def\keywords#1{%
+%  \ifdefined\isaccepted \else
+%    \par {\bf Keywords:} #1%
+%  \fi
+%  \ifdefined\nohyperref\else\ifdefined\hypersetup
+%    \hypersetup{pdfkeywords={#1}}
+%  \fi\fi
+ \ifdefined\isaccepted \else
+    \begin{quote}
+    \textbf{Keywords:} #1%
+    \end{quote}
+ \fi
+  \ifdefined\nohyperref\else\ifdefined\hypersetup
+    \hypersetup{pdfkeywords={#1}}
+  \fi\fi
+}
+
+
+% create title (includes both anonymized and non-anonymized versions)
+\providecommand{\@maketitle}{}
+\renewcommand{\@maketitle}{%
+  \vbox{%
+    \hsize\textwidth
+    \linewidth\hsize
+    \vskip 0.1in
+%     \@toptitlebar
+    \centering
+    {\LARGE\bf \@title\par}
+%     \@bottomtitlebar
+    \if@conferencefinal
+      \def\And{%
+        \end{tabular}\hfil\linebreak[0]\hfil%
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+      }
+      \def\AND{%
+        \end{tabular}\hfil\linebreak[4]\hfil%
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+      }
+      \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\@author\end{tabular}%
+    \else
+      \if@preprinttype
+        \def\And{%
+          \end{tabular}\hfil\linebreak[0]\hfil%
+          \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+        }
+        \def\AND{%
+          \end{tabular}\hfil\linebreak[4]\hfil%
+          \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\ignorespaces%
+        }
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}\@author\end{tabular}%
+      \else
+        \begin{tabular}[t]{c}\bf\rule{\z@}{24\p@}
+          Anonymous Author(s) \\
+          Affiliation \\
+          Address \\
+          \texttt{email} \\
+        \end{tabular}%
+      \fi
+    \fi
+    \vskip 0.3in \@minus 0.1in
+  }
+}
+
+% add conference notice to bottom of first page
+\newcommand{\ftype@noticebox}{8}
+\newcommand{\@notice}{%
+  % give a bit of extra room back to authors on first page
+  \enlargethispage{2\baselineskip}%
+  \@float{noticebox}[b]%
+    \footnotesize\@noticestring%
+  \end@float%
+}
+
+% abstract styling
+\renewenvironment{abstract}%
+{%
+%   \vskip 0.075in%
+%   \centerline%
+%   {\large\bf Abstract}%
+%   \vspace{0.5ex}%
+  \begin{quote}%
+  \textbf{Abstract:}%
+}
+{
+  \par%
+  \vskip 1ex%
+%   \ifdefined\keywords
+%   \textbf{Keywords:}%
+%   \@keywords%
+%   \else
+%   \fi
+  \end{quote}%
+}
+
+\endinput
diff --git a/workspace/inputs/template_unzipped/corl_2026_template_submission/corlabbrvnat.bst b/workspace/inputs/template_unzipped/corl_2026_template_submission/corlabbrvnat.bst
new file mode 100644
index 0000000..eab7c3e
--- /dev/null
+++ b/workspace/inputs/template_unzipped/corl_2026_template_submission/corlabbrvnat.bst
@@ -0,0 +1,1442 @@
+%% File: `corlabbrvnat.bst'
+%% A modification of `abbrvnat.bst' to order the references by the order
+%% of appearance in the paper.
+%%
+%% Modified by Roberto Calandra 12/06/2017
+%% email: roberto.calandra@berkeley.edu
+%%
+%% -------------------
+%%
+%% Copyright 1993-2007 Patrick W Daly
+%% Max-Planck-Institut f\"ur Sonnensystemforschung
+%% Max-Planck-Str. 2
+%% D-37191 Katlenburg-Lindau
+%% Germany
+%% E-mail: daly@mps.mpg.de
+%%
+%% This program can be redistributed and/or modified under the terms
+%% of the LaTeX Project Public License Distributed from CTAN
+%% archives in directory macros/latex/base/lppl.txt; either
+%% version 1 of the License, or any later version.
+%%
+ % Version and source file information:
+ % \ProvidesFile{natbst.mbs}[2007/11/26 1.93 (PWD)]
+ %
+ % BibTeX `plainnat' family
+ %   version 0.99b for BibTeX versions 0.99a or later,
+ %   for LaTeX versions 2.09 and 2e.
+ %
+ % For use with the `natbib.sty' package; emulates the corresponding
+ %   member of the `plain' family, but with author-year citations.
+ %
+ % With version 6.0 of `natbib.sty', it may also be used for numerical
+ %   citations, while retaining the commands \citeauthor, \citefullauthor,
+ %   and \citeyear to print the corresponding information.
+ %
+ % For version 7.0 of `natbib.sty', the KEY field replaces missing
+ %   authors/editors, and the date is left blank in \bibitem.
+ %
+ % Includes field EID for the sequence/citation number of electronic journals
+ %  which is used instead of page numbers.
+ %
+ % Includes fields ISBN and ISSN.
+ %
+ % Includes field URL for Internet addresses.
+ %
+ % Includes field DOI for Digital Object Idenfifiers.
+ %
+ % Works best with the url.sty package of Donald Arseneau.
+ %
+ % Works with identical authors and year are further sorted by
+ %   citation key, to preserve any natural sequence.
+ %
+ENTRY
+  { address
+    author
+    booktitle
+    chapter
+    doi
+    eid
+    edition
+    editor
+    howpublished
+    institution
+    isbn
+    issn
+    journal
+    key
+    month
+    note
+    number
+    organization
+    pages
+    publisher
+    school
+    series
+    title
+    type
+    url
+    volume
+    year
+  }
+  {}
+  { label extra.label sort.label short.list }
+
+INTEGERS { output.state before.all mid.sentence after.sentence after.block }
+
+FUNCTION {init.state.consts}
+{ #0 'before.all :=
+  #1 'mid.sentence :=
+  #2 'after.sentence :=
+  #3 'after.block :=
+}
+
+STRINGS { s t }
+
+FUNCTION {output.nonnull}
+{ 's :=
+  output.state mid.sentence =
+    { ", " * write$ }
+    { output.state after.block =
+        { add.period$ write$
+          newline$
+          "\newblock " write$
+        }
+        { output.state before.all =
+            'write$
+            { add.period$ " " * write$ }
+          if$
+        }
+      if$
+      mid.sentence 'output.state :=
+    }
+  if$
+  s
+}
+
+FUNCTION {output}
+{ duplicate$ empty$
+    'pop$
+    'output.nonnull
+  if$
+}
+
+FUNCTION {output.check}
+{ 't :=
+  duplicate$ empty$
+    { pop$ "empty " t * " in " * cite$ * warning$ }
+    'output.nonnull
+  if$
+}
+
+FUNCTION {fin.entry}
+{ add.period$
+  write$
+  newline$
+}
+
+FUNCTION {new.block}
+{ output.state before.all =
+    'skip$
+    { after.block 'output.state := }
+  if$
+}
+
+FUNCTION {new.sentence}
+{ output.state after.block =
+    'skip$
+    { output.state before.all =
+        'skip$
+        { after.sentence 'output.state := }
+      if$
+    }
+  if$
+}
+
+FUNCTION {not}
+{   { #0 }
+    { #1 }
+  if$
+}
+
+FUNCTION {and}
+{   'skip$
+    { pop$ #0 }
+  if$
+}
+
+FUNCTION {or}
+{   { pop$ #1 }
+    'skip$
+  if$
+}
+
+FUNCTION {new.block.checka}
+{ empty$
+    'skip$
+    'new.block
+  if$
+}
+
+FUNCTION {new.block.checkb}
+{ empty$
+  swap$ empty$
+  and
+    'skip$
+    'new.block
+  if$
+}
+
+FUNCTION {new.sentence.checka}
+{ empty$
+    'skip$
+    'new.sentence
+  if$
+}
+
+FUNCTION {new.sentence.checkb}
+{ empty$
+  swap$ empty$
+  and
+    'skip$
+    'new.sentence
+  if$
+}
+
+FUNCTION {field.or.null}
+{ duplicate$ empty$
+    { pop$ "" }
+    'skip$
+  if$
+}
+
+FUNCTION {emphasize}
+{ duplicate$ empty$
+    { pop$ "" }
+    { "\emph{" swap$ * "}" * }
+  if$
+}
+
+INTEGERS { nameptr namesleft numnames }
+
+FUNCTION {format.names}
+{ 's :=
+  #1 'nameptr :=
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    { s nameptr "{f.~}{vv~}{ll}{, jj}" format.name$ 't :=
+      nameptr #1 >
+        { namesleft #1 >
+            { ", " * t * }
+            { numnames #2 >
+                { "," * }
+                'skip$
+              if$
+              t "others" =
+                { " et~al." * }
+                { " and " * t * }
+              if$
+            }
+          if$
+        }
+        't
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {format.key}
+{ empty$
+    { key field.or.null }
+    { "" }
+  if$
+}
+
+FUNCTION {format.authors}
+{ author empty$
+    { "" }
+    { author format.names }
+  if$
+}
+
+FUNCTION {format.editors}
+{ editor empty$
+    { "" }
+    { editor format.names
+      editor num.names$ #1 >
+        { ", editors" * }
+        { ", editor" * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.isbn}
+{ isbn empty$
+    { "" }
+    { new.block "ISBN " isbn * }
+  if$
+}
+
+FUNCTION {format.issn}
+{ issn empty$
+    { "" }
+    { new.block "ISSN " issn * }
+  if$
+}
+
+FUNCTION {format.url}
+{ url empty$
+    { "" }
+    { new.block "URL \url{" url * "}" * }
+  if$
+}
+
+FUNCTION {format.doi}
+{ doi empty$
+    { "" }
+    { new.block "\doi{" doi * "}" * }
+  if$
+}
+
+FUNCTION {format.title}
+{ title empty$
+    { "" }
+    { title "t" change.case$ }
+  if$
+}
+
+FUNCTION {format.full.names}
+{'s :=
+  #1 'nameptr :=
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    { s nameptr
+      "{vv~}{ll}" format.name$ 't :=
+      nameptr #1 >
+        {
+          namesleft #1 >
+            { ", " * t * }
+            {
+              numnames #2 >
+                { "," * }
+                'skip$
+              if$
+              t "others" =
+                { " et~al." * }
+                { " and " * t * }
+              if$
+            }
+          if$
+        }
+        't
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {author.editor.full}
+{ author empty$
+    { editor empty$
+        { "" }
+        { editor format.full.names }
+      if$
+    }
+    { author format.full.names }
+  if$
+}
+
+FUNCTION {author.full}
+{ author empty$
+    { "" }
+    { author format.full.names }
+  if$
+}
+
+FUNCTION {editor.full}
+{ editor empty$
+    { "" }
+    { editor format.full.names }
+  if$
+}
+
+FUNCTION {make.full.names}
+{ type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.full
+    { type$ "proceedings" =
+        'editor.full
+        'author.full
+      if$
+    }
+  if$
+}
+
+FUNCTION {output.bibitem}
+{ newline$
+  "\bibitem[" write$
+  label write$
+  ")" make.full.names duplicate$ short.list =
+     { pop$ }
+     { * }
+   if$
+  "]{" * write$
+  cite$ write$
+  "}" write$
+  newline$
+  ""
+  before.all 'output.state :=
+}
+
+FUNCTION {n.dashify}
+{ 't :=
+  ""
+    { t empty$ not }
+    { t #1 #1 substring$ "-" =
+        { t #1 #2 substring$ "--" = not
+            { "--" *
+              t #2 global.max$ substring$ 't :=
+            }
+            {   { t #1 #1 substring$ "-" = }
+                { "-" *
+                  t #2 global.max$ substring$ 't :=
+                }
+              while$
+            }
+          if$
+        }
+        { t #1 #1 substring$ *
+          t #2 global.max$ substring$ 't :=
+        }
+      if$
+    }
+  while$
+}
+
+FUNCTION {format.date}
+{ year duplicate$ empty$
+    { "empty year in " cite$ * warning$
+       pop$ "" }
+    'skip$
+  if$
+  month empty$
+    'skip$
+    { month
+      " " * swap$ *
+    }
+  if$
+  extra.label *
+}
+
+FUNCTION {format.btitle}
+{ title emphasize
+}
+
+FUNCTION {tie.or.space.connect}
+{ duplicate$ text.length$ #3 <
+    { "~" }
+    { " " }
+  if$
+  swap$ * *
+}
+
+FUNCTION {either.or.check}
+{ empty$
+    'pop$
+    { "can't use both " swap$ * " fields in " * cite$ * warning$ }
+  if$
+}
+
+FUNCTION {format.bvolume}
+{ volume empty$
+    { "" }
+    { "volume" volume tie.or.space.connect
+      series empty$
+        'skip$
+        { " of " * series emphasize * }
+      if$
+      "volume and number" number either.or.check
+    }
+  if$
+}
+
+FUNCTION {format.number.series}
+{ volume empty$
+    { number empty$
+        { series field.or.null }
+        { output.state mid.sentence =
+            { "number" }
+            { "Number" }
+          if$
+          number tie.or.space.connect
+          series empty$
+            { "there's a number but no series in " cite$ * warning$ }
+            { " in " * series * }
+          if$
+        }
+      if$
+    }
+    { "" }
+  if$
+}
+
+FUNCTION {format.edition}
+{ edition empty$
+    { "" }
+    { output.state mid.sentence =
+        { edition "l" change.case$ " edition" * }
+        { edition "t" change.case$ " edition" * }
+      if$
+    }
+  if$
+}
+
+INTEGERS { multiresult }
+
+FUNCTION {multi.page.check}
+{ 't :=
+  #0 'multiresult :=
+    { multiresult not
+      t empty$ not
+      and
+    }
+    { t #1 #1 substring$
+      duplicate$ "-" =
+      swap$ duplicate$ "," =
+      swap$ "+" =
+      or or
+        { #1 'multiresult := }
+        { t #2 global.max$ substring$ 't := }
+      if$
+    }
+  while$
+  multiresult
+}
+
+FUNCTION {format.pages}
+{ pages empty$
+    { "" }
+    { pages multi.page.check
+        { "pages" pages n.dashify tie.or.space.connect }
+        { "page" pages tie.or.space.connect }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.eid}
+{ eid empty$
+    { "" }
+    { "art." eid tie.or.space.connect }
+  if$
+}
+
+FUNCTION {format.vol.num.pages}
+{ volume field.or.null
+  number empty$
+    'skip$
+    { "\penalty0 (" number * ")" * *
+      volume empty$
+        { "there's a number but no volume in " cite$ * warning$ }
+        'skip$
+      if$
+    }
+  if$
+  pages empty$
+    'skip$
+    { duplicate$ empty$
+        { pop$ format.pages }
+        { ":\penalty0 " * pages n.dashify * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.vol.num.eid}
+{ volume field.or.null
+  number empty$
+    'skip$
+    { "\penalty0 (" number * ")" * *
+      volume empty$
+        { "there's a number but no volume in " cite$ * warning$ }
+        'skip$
+      if$
+    }
+  if$
+  eid empty$
+    'skip$
+    { duplicate$ empty$
+        { pop$ format.eid }
+        { ":\penalty0 " * eid * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.chapter.pages}
+{ chapter empty$
+    'format.pages
+    { type empty$
+        { "chapter" }
+        { type "l" change.case$ }
+      if$
+      chapter tie.or.space.connect
+      pages empty$
+        'skip$
+        { ", " * format.pages * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.in.ed.booktitle}
+{ booktitle empty$
+    { "" }
+    { editor empty$
+        { "In " booktitle emphasize * }
+        { "In " format.editors * ", " * booktitle emphasize * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {empty.misc.check}
+{ author empty$ title empty$ howpublished empty$
+  month empty$ year empty$ note empty$
+  and and and and and
+  key empty$ not and
+    { "all relevant fields are empty in " cite$ * warning$ }
+    'skip$
+  if$
+}
+
+FUNCTION {format.thesis.type}
+{ type empty$
+    'skip$
+    { pop$
+      type "t" change.case$
+    }
+  if$
+}
+
+FUNCTION {format.tr.number}
+{ type empty$
+    { "Technical Report" }
+    'type
+  if$
+  number empty$
+    { "t" change.case$ }
+    { number tie.or.space.connect }
+  if$
+}
+
+FUNCTION {format.article.crossref}
+{ key empty$
+    { journal empty$
+        { "need key or journal for " cite$ * " to crossref " * crossref *
+          warning$
+          ""
+        }
+        { "In \emph{" journal * "}" * }
+      if$
+    }
+    { "In " }
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {format.book.crossref}
+{ volume empty$
+    { "empty volume in " cite$ * "'s crossref of " * crossref * warning$
+      "In "
+    }
+    { "Volume" volume tie.or.space.connect
+      " of " *
+    }
+  if$
+  editor empty$
+  editor field.or.null author field.or.null =
+  or
+    { key empty$
+        { series empty$
+            { "need editor, key, or series for " cite$ * " to crossref " *
+              crossref * warning$
+              "" *
+            }
+            { "\emph{" * series * "}" * }
+          if$
+        }
+        'skip$
+      if$
+    }
+    'skip$
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {format.incoll.inproc.crossref}
+{ editor empty$
+  editor field.or.null author field.or.null =
+  or
+    { key empty$
+        { booktitle empty$
+            { "need editor, key, or booktitle for " cite$ * " to crossref " *
+              crossref * warning$
+              ""
+            }
+            { "In \emph{" booktitle * "}" * }
+          if$
+        }
+        { "In " }
+      if$
+    }
+    { "In " }
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {article}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { journal emphasize "journal" output.check
+      eid empty$
+        { format.vol.num.pages output }
+        { format.vol.num.eid output }
+      if$
+      format.date "year" output.check
+    }
+    { format.article.crossref output.nonnull
+      eid empty$
+        { format.pages output }
+        { format.eid output }
+      if$
+    }
+  if$
+  format.issn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {book}
+{ output.bibitem
+  author empty$
+    { format.editors "author and editor" output.check
+      editor format.key output
+    }
+    { format.authors output.nonnull
+      crossref missing$
+        { "author and editor" editor either.or.check }
+        'skip$
+      if$
+    }
+  if$
+  new.block
+  format.btitle "title" output.check
+  crossref missing$
+    { format.bvolume output
+      new.block
+      format.number.series output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+    }
+    { new.block
+      format.book.crossref output.nonnull
+    }
+  if$
+  format.edition output
+  format.date "year" output.check
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {booklet}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  new.block
+  format.title "title" output.check
+  howpublished address new.block.checkb
+  howpublished output
+  address output
+  format.date output
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {inbook}
+{ output.bibitem
+  author empty$
+    { format.editors "author and editor" output.check
+      editor format.key output
+    }
+    { format.authors output.nonnull
+      crossref missing$
+        { "author and editor" editor either.or.check }
+        'skip$
+      if$
+    }
+  if$
+  new.block
+  format.btitle "title" output.check
+  crossref missing$
+    { format.bvolume output
+      format.chapter.pages "chapter and pages" output.check
+      new.block
+      format.number.series output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+    }
+    { format.chapter.pages "chapter and pages" output.check
+      new.block
+      format.book.crossref output.nonnull
+    }
+  if$
+  format.edition output
+  format.date "year" output.check
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {incollection}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { format.in.ed.booktitle "booktitle" output.check
+      format.bvolume output
+      format.number.series output
+      format.chapter.pages output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+      format.edition output
+      format.date "year" output.check
+    }
+    { format.incoll.inproc.crossref output.nonnull
+      format.chapter.pages output
+    }
+  if$
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {inproceedings}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { format.in.ed.booktitle "booktitle" output.check
+      format.bvolume output
+      format.number.series output
+      format.pages output
+      address empty$
+        { organization publisher new.sentence.checkb
+          organization output
+          publisher output
+          format.date "year" output.check
+        }
+        { address output.nonnull
+          format.date "year" output.check
+          new.sentence
+          organization output
+          publisher output
+        }
+      if$
+    }
+    { format.incoll.inproc.crossref output.nonnull
+      format.pages output
+    }
+  if$
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {conference} { inproceedings }
+
+FUNCTION {manual}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  new.block
+  format.btitle "title" output.check
+  organization address new.block.checkb
+  organization output
+  address output
+  format.edition output
+  format.date output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {mastersthesis}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  "Master's thesis" format.thesis.type output.nonnull
+  school "school" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {misc}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  title howpublished new.block.checkb
+  format.title output
+  howpublished new.block.checka
+  howpublished output
+  format.date output
+  format.issn output
+  format.url output
+  new.block
+  note output
+  fin.entry
+  empty.misc.check
+}
+
+FUNCTION {phdthesis}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.btitle "title" output.check
+  new.block
+  "PhD thesis" format.thesis.type output.nonnull
+  school "school" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {proceedings}
+{ output.bibitem
+  format.editors output
+  editor format.key output
+  new.block
+  format.btitle "title" output.check
+  format.bvolume output
+  format.number.series output
+  address output
+  format.date "year" output.check
+  new.sentence
+  organization output
+  publisher output
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {techreport}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  format.tr.number output.nonnull
+  institution "institution" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {unpublished}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  note "note" output.check
+  format.date output
+  format.url output
+  fin.entry
+}
+
+FUNCTION {default.type} { misc }
+
+
+MACRO {jan} {"Jan."}
+
+MACRO {feb} {"Feb."}
+
+MACRO {mar} {"Mar."}
+
+MACRO {apr} {"Apr."}
+
+MACRO {may} {"May"}
+
+MACRO {jun} {"June"}
+
+MACRO {jul} {"July"}
+
+MACRO {aug} {"Aug."}
+
+MACRO {sep} {"Sept."}
+
+MACRO {oct} {"Oct."}
+
+MACRO {nov} {"Nov."}
+
+MACRO {dec} {"Dec."}
+
+
+
+MACRO {acmcs} {"ACM Comput. Surv."}
+
+MACRO {acta} {"Acta Inf."}
+
+MACRO {cacm} {"Commun. ACM"}
+
+MACRO {ibmjrd} {"IBM J. Res. Dev."}
+
+MACRO {ibmsj} {"IBM Syst.~J."}
+
+MACRO {ieeese} {"IEEE Trans. Softw. Eng."}
+
+MACRO {ieeetc} {"IEEE Trans. Comput."}
+
+MACRO {ieeetcad}
+ {"IEEE Trans. Comput.-Aided Design Integrated Circuits"}
+
+MACRO {ipl} {"Inf. Process. Lett."}
+
+MACRO {jacm} {"J.~ACM"}
+
+MACRO {jcss} {"J.~Comput. Syst. Sci."}
+
+MACRO {scp} {"Sci. Comput. Programming"}
+
+MACRO {sicomp} {"SIAM J. Comput."}
+
+MACRO {tocs} {"ACM Trans. Comput. Syst."}
+
+MACRO {tods} {"ACM Trans. Database Syst."}
+
+MACRO {tog} {"ACM Trans. Gr."}
+
+MACRO {toms} {"ACM Trans. Math. Softw."}
+
+MACRO {toois} {"ACM Trans. Office Inf. Syst."}
+
+MACRO {toplas} {"ACM Trans. Prog. Lang. Syst."}
+
+MACRO {tcs} {"Theoretical Comput. Sci."}
+
+
+READ
+
+FUNCTION {sortify}
+{ purify$
+  "l" change.case$
+}
+
+INTEGERS { len }
+
+FUNCTION {chop.word}
+{ 's :=
+  'len :=
+  s #1 len substring$ =
+    { s len #1 + global.max$ substring$ }
+    's
+  if$
+}
+
+FUNCTION {format.lab.names}
+{ 's :=
+  s #1 "{vv~}{ll}" format.name$
+  s num.names$ duplicate$
+  #2 >
+    { pop$ " et~al." * }
+    { #2 <
+        'skip$
+        { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" =
+            { " et~al." * }
+            { " and " * s #2 "{vv~}{ll}" format.name$ * }
+          if$
+        }
+      if$
+    }
+  if$
+}
+
+FUNCTION {author.key.label}
+{ author empty$
+    { key empty$
+        { cite$ #1 #3 substring$ }
+        'key
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {author.editor.key.label}
+{ author empty$
+    { editor empty$
+        { key empty$
+            { cite$ #1 #3 substring$ }
+            'key
+          if$
+        }
+        { editor format.lab.names }
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {author.key.organization.label}
+{ author empty$
+    { key empty$
+        { organization empty$
+            { cite$ #1 #3 substring$ }
+            { "The " #4 organization chop.word #3 text.prefix$ }
+          if$
+        }
+        'key
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {editor.key.organization.label}
+{ editor empty$
+    { key empty$
+        { organization empty$
+            { cite$ #1 #3 substring$ }
+            { "The " #4 organization chop.word #3 text.prefix$ }
+          if$
+        }
+        'key
+      if$
+    }
+    { editor format.lab.names }
+  if$
+}
+
+FUNCTION {calc.short.authors}
+{ type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.key.label
+    { type$ "proceedings" =
+        'editor.key.organization.label
+        { type$ "manual" =
+            'author.key.organization.label
+            'author.key.label
+          if$
+        }
+      if$
+    }
+  if$
+  'short.list :=
+}
+
+FUNCTION {calc.label}
+{ calc.short.authors
+  short.list
+  "("
+  *
+  year duplicate$ empty$
+  short.list key field.or.null = or
+     { pop$ "" }
+     'skip$
+  if$
+  *
+  'label :=
+}
+
+FUNCTION {sort.format.names}
+{ 's :=
+  #1 'nameptr :=
+  ""
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    {
+      s nameptr "{vv{ } }{ll{ }}{  f{ }}{  jj{ }}" format.name$ 't :=
+      nameptr #1 >
+        {
+          "   "  *
+          namesleft #1 = t "others" = and
+            { "zzzzz" * }
+            { numnames #2 > nameptr #2 = and
+                { "zz" * year field.or.null * "   " * }
+                'skip$
+              if$
+              t sortify *
+            }
+          if$
+        }
+        { t sortify * }
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {sort.format.title}
+{ 't :=
+  "A " #2
+    "An " #3
+      "The " #4 t chop.word
+    chop.word
+  chop.word
+  sortify
+  #1 global.max$ substring$
+}
+
+FUNCTION {author.sort}
+{ author empty$
+    { key empty$
+        { "to sort, need author or key in " cite$ * warning$
+          ""
+        }
+        { key sortify }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {author.editor.sort}
+{ author empty$
+    { editor empty$
+        { key empty$
+            { "to sort, need author, editor, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { editor sort.format.names }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {author.organization.sort}
+{ author empty$
+    { organization empty$
+        { key empty$
+            { "to sort, need author, organization, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { "The " #4 organization chop.word sortify }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {editor.organization.sort}
+{ editor empty$
+    { organization empty$
+        { key empty$
+            { "to sort, need editor, organization, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { "The " #4 organization chop.word sortify }
+      if$
+    }
+    { editor sort.format.names }
+  if$
+}
+
+
+FUNCTION {presort}
+{ calc.label
+  label sortify
+  "    "
+  *
+  type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.sort
+    { type$ "proceedings" =
+        'editor.organization.sort
+        { type$ "manual" =
+            'author.organization.sort
+            'author.sort
+          if$
+        }
+      if$
+    }
+  if$
+  "    "
+  *
+  year field.or.null sortify
+  *
+  "    "
+  *
+  cite$
+  *
+  #1 entry.max$ substring$
+  'sort.label :=
+  sort.label *
+  #1 entry.max$ substring$
+  'sort.key$ :=
+}
+
+ITERATE {presort}
+
+% SORT
+
+STRINGS { longest.label last.label next.extra }
+
+INTEGERS { longest.label.width last.extra.num number.label }
+
+FUNCTION {initialize.longest.label}
+{ "" 'longest.label :=
+  #0 int.to.chr$ 'last.label :=
+  "" 'next.extra :=
+  #0 'longest.label.width :=
+  #0 'last.extra.num :=
+  #0 'number.label :=
+}
+
+FUNCTION {forward.pass}
+{ last.label label =
+    { last.extra.num #1 + 'last.extra.num :=
+      last.extra.num int.to.chr$ 'extra.label :=
+    }
+    { "a" chr.to.int$ 'last.extra.num :=
+      "" 'extra.label :=
+      label 'last.label :=
+    }
+  if$
+  number.label #1 + 'number.label :=
+}
+
+FUNCTION {reverse.pass}
+{ next.extra "b" =
+    { "a" 'extra.label := }
+    'skip$
+  if$
+  extra.label 'next.extra :=
+  extra.label
+  duplicate$ empty$
+    'skip$
+    { "{\natexlab{" swap$ * "}}" * }
+  if$
+  'extra.label :=
+  label extra.label * 'label :=
+}
+
+EXECUTE {initialize.longest.label}
+
+ITERATE {forward.pass}
+
+REVERSE {reverse.pass}
+
+FUNCTION {bib.sort.order}
+{ sort.label  'sort.key$ :=
+}
+
+ITERATE {bib.sort.order}
+
+% SORT
+
+FUNCTION {begin.bib}
+{   preamble$ empty$
+    'skip$
+    { preamble$ write$ newline$ }
+  if$
+  "\begin{thebibliography}{" number.label int.to.str$ * "}" *
+  write$ newline$
+  "\providecommand{\natexlab}[1]{#1}"
+  write$ newline$
+  "\providecommand{\url}[1]{\texttt{#1}}"
+  write$ newline$
+  "\expandafter\ifx\csname urlstyle\endcsname\relax"
+  write$ newline$
+  "  \providecommand{\doi}[1]{doi: #1}\else"
+  write$ newline$
+  "  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi"
+  write$ newline$
+}
+
+EXECUTE {begin.bib}
+
+EXECUTE {init.state.consts}
+
+ITERATE {call.type$}
+
+FUNCTION {end.bib}
+{ newline$
+  "\end{thebibliography}" write$ newline$
+}
+
+EXECUTE {end.bib}
diff --git a/workspace/inputs/template_unzipped/corl_2026_template_submission/example.bib b/workspace/inputs/template_unzipped/corl_2026_template_submission/example.bib
new file mode 100644
index 0000000..5c573da
--- /dev/null
+++ b/workspace/inputs/template_unzipped/corl_2026_template_submission/example.bib
@@ -0,0 +1,20 @@
+% This file was created with JabRef 2.10.
+% Encoding: UTF-8
+
+
+@Article{Gauss1857,
+  Title                    = {Theory of the motion of the heavenly bodies moving about the sun in conic sections},
+  Author                   = {Carl Friedrich Gauss and Charles Henry Davis},
+  Journal                  = {Gauss's Theoria Motus},
+  Year                     = {1857},
+  Number                   = {1},
+  Pages                    = {5--23},
+  Volume                   = {76}
+}
+
+@Book{Lagrange1788,
+  title		= {M{\'e}canique Analytique},
+  author	= {Joseph-Louis Lagrange},
+  publisher	= {Desaint, Paris},
+  year		= {1788}
+}
diff --git a/workspace/inputs/template_unzipped/corl_2026_template_submission/example.tex b/workspace/inputs/template_unzipped/corl_2026_template_submission/example.tex
new file mode 100644
index 0000000..26273e6
--- /dev/null
+++ b/workspace/inputs/template_unzipped/corl_2026_template_submission/example.tex
@@ -0,0 +1,145 @@
+\documentclass{article}
+
+\usepackage{corl_2026} % Use this for the initial submission.
+% \usepackage[final]{corl_2026} % Uncomment for the camera-ready ``final'' version.
+% \usepackage[preprint]{corl_2026} % Uncomment for pre-prints (e.g., arxiv); This is like ``final'', but will remove the CORL footnote.
+
+\title{Formatting Instructions for CoRL 2026 Camera-Ready}
+
+% The \author macro works with any number of authors. There are two
+% commands used to separate the names and addresses of multiple
+% authors: \And and \AND.
+%
+% Using \And between authors leaves it to LaTeX to determine where to
+% break the lines. Using \AND forces a line break at that point. So,
+% if LaTeX puts 3 of 4 authors names on the first line, and the last
+% on the second line, try using \AND instead of \And before the third
+% author name.
+
+% NOTE: authors will be visible only in the camera-ready and preprint versions (i.e., when using the option 'final' or 'preprint'). 
+% 	For the initial submission the authors will be anonymized.
+
+\author{
+  Jane E.~Doe\\
+  Department of Electrical Engineering and Computer Sciences\\
+  University of California Berkeley 
+  United States\\
+  \texttt{janedoe@berkeley.edu} \\
+  %% examples of more authors
+  %% \And
+  %% Coauthor \\
+  %% Affiliation \\
+  %% Address \\
+  %% \texttt{email} \\
+  %% \AND
+  %% Coauthor \\
+  %% Affiliation \\
+  %% Address \\
+  %% \texttt{email} \\
+  %% \And
+  %% Coauthor \\
+  %% Affiliation \\
+  %% Address \\
+  %% \texttt{email} \\
+  %% \And
+  %% Coauthor \\
+  %% Affiliation \\
+  %% Address \\
+  %% \texttt{email} \\
+}
+
+
+\begin{document}
+\maketitle
+
+%===============================================================================
+
+\begin{abstract}
+    The purpose of this document is to provide both the basic paper template and submission guidelines. Abstracts should be a single paragraph, between 4--6 sentences long, ideally. Gross violations will trigger corrections at the camera-ready phase.
+\end{abstract}
+
+% Two or three meaningful keywords should be added here
+\keywords{CoRL, Robots, Learning} 
+
+%===============================================================================
+
+\section{Introduction}
+	
+    Submission to CoRL 2026 will be entirely electronic, via a web site (not email). Information about the submission process and \LaTeX{} templates are available on the conference web site at \url{https://corl.org/}. For camera ready submission, use the \texttt{final} option for the \texttt{\textbackslash usepackage} command. 
+
+%===============================================================================
+
+\section{Citations}
+\label{sec:citations}
+
+	Citations can be made using either \textbackslash citep\{\} or \textbackslash citet\{\}, depending from the appropriateness. To avoid the citation moving to the next line, it is often a good practice to replace the space before with a tilde (\~{}) character.
+	Example 1: ``CoRL is the best conference ever~\citep{Gauss1857}.''
+	Example 2: ``\citet{Lagrange1788} proved, both theoretically and numerically, that CoRL is the best conference ever.''
+	
+%===============================================================================
+
+\section{Experimental Results}
+\label{sec:result}
+
+	Nam dui ligula, fringilla a, euismod sodales, sollicitudin vel, wisi. Morbi auctor lorem non justo.
+	Nam lacus libero, pretium at, lobortis vitae, ultricies et, tellus. Donec aliquet, tortor sed accumsan
+	bibendum, erat ligula aliquet magna, vitae ornare odio metus a mi. Morbi ac orci et nisl hendrerit
+	mollis. 
+
+	Suspendisse ut massa. Cras nec ante. Pellentesque a nulla. Cum sociis natoque penatibus
+	et magnis dis parturient montes, nascetur ridiculus mus. Aliquam tincidunt urna. Nulla ullamcorper
+	vestibulum turpis. Pellentesque cursus luctus mauris.
+	
+	Nulla malesuada porttitor diam. Donec felis erat, congue non, volutpat at, tincidunt tristique, libero.
+	Vivamus viverra fermentum felis. Donec nonummy pellentesque ante. Phasellus adipiscing semper 	elit. 
+	Proin fermentum massa ac quam. Sed diam turpis, molestie vitae, placerat a, molestie nec, leo.
+	Maecenas lacinia. Nam ipsum ligula, eleifend at, accumsan nec, suscipit a, ipsum. Morbi blandit
+	ligula feugiat magna. Nunc eleifend consequat lorem. Sed lacinia nulla vitae enim. Pellentesque tin-
+	cidunt purus vel magna. Integer non enim. Praesent euismod nunc eu purus. Donec bibendum quam
+	in tellus. Nullam cursus pulvinar lectus. Donec et mi. Nam vulputate metus eu enim. Vestibulum
+	pellentesque felis eu massa.
+	Quisque ullamcorper placerat ipsum. Cras nibh. Morbi vel justo vitae lacus tincidunt ultrices. 
+	
+	Lorem
+	ipsum dolor sit amet, consectetuer adipiscing elit. In hac habitasse platea dictumst. Integer tempus
+	convallis augue. Etiam facilisis. Nunc elementum fermentum wisi. Aenean placerat. Ut imperdiet,
+	enim sed gravida sollicitudin, felis odio placerat quam, ac pulvinar elit purus eget enim. Nunc vitae
+	tortor. Proin tempus nibh sit amet nisl. Vivamus quis tortor vitae risus porta vehicula.
+
+%===============================================================================
+
+\section{Conclusion}
+\label{sec:conclusion}
+
+	Nam dui ligula, fringilla a, euismod sodales, sollicitudin vel, wisi. Morbi auctor lorem non justo.
+	Nam lacus libero, pretium at, lobortis vitae, ultricies et, tellus. Donec aliquet, tortor sed accumsan
+	bibendum, erat ligula aliquet magna, vitae ornare odio metus a mi. Morbi ac orci et nisl hendrerit
+	mollis. Suspendisse ut massa. Cras nec ante. Pellentesque a nulla. Cum sociis natoque penatibus
+	et magnis dis parturient montes, nascetur ridiculus mus. Aliquam tincidunt urna. Nulla ullamcorper
+	vestibulum turpis. Pellentesque cursus luctus mauris.
+	Nulla malesuada porttitor diam. Donec felis erat, congue non, volutpat at, tincidunt tristique, libero.
+	Vivamus viverra fermentum felis. Donec nonummy pellentesque ante. Phasellus adipiscing semper
+	elit. Proin fermentum massa ac quam. Sed diam turpis, molestie vitae, placerat a, molestie nec, leo.
+	Maecenas lacinia. Nam ipsum ligula, eleifend at, accumsan nec, suscipit a, ipsum. Morbi blandit
+	ligula feugiat magna. Nunc eleifend consequat lorem. Sed lacinia nulla vitae enim. Pellentesque tin-
+	cidunt purus vel magna. Integer non enim. Praesent euismod nunc eu purus. Donec bibendum quam
+	in tellus. Nullam cursus pulvinar lectus. Donec et mi. Nam vulputate metus eu enim. Vestibulum
+	pellentesque felis eu massa.
+	Quisque ullamcorper placerat ipsum. Cras nibh. Morbi vel justo vitae lacus tincidunt ultrices. Lorem
+	ipsum dolor sit amet, consectetuer adipiscing elit. In hac habitasse platea dictumst. Integer tempus
+	convallis augue. Etiam facilisis. Nunc elementum fermentum wisi. Aenean placerat. Ut imperdiet,
+	enim sed gravida sollicitudin, felis odio placerat quam, ac pulvinar elit purus eget enim. Nunc vitae
+	tortor. Proin tempus nibh sit amet nisl. Vivamus quis tortor vitae risus porta vehicula.
+
+%===============================================================================
+
+\clearpage
+% The acknowledgments are automatically included only in the final and preprint versions of the paper.
+\acknowledgments{If a paper is accepted, the final camera-ready version will (and probably should) include acknowledgments. All acknowledgments go at the end of the paper, including thanks to reviewers who gave useful comments, to colleagues who contributed to the ideas, and to funding agencies and corporate sponsors that provided financial support.}
+
+%===============================================================================
+
+% no \bibliographystyle is required, since the corl style is automatically used.
+\bibliography{example}  % .bib
+
+\end{document}
diff --git a/workspace/metrics.json b/workspace/metrics.json
new file mode 100644
index 0000000..9ee595c
--- /dev/null
+++ b/workspace/metrics.json
@@ -0,0 +1,398 @@
+{
+  "tables": [
+    {
+      "label": "Socket peg insertion, 100-rollout performance and speed",
+      "headers": [
+        "run_name",
+        "method_family",
+        "horizon_or_steps",
+        "avg_reward",
+        "median_reward",
+        "avg_max_reward",
+        "max_reward",
+        "nonzero_reward",
+        "success_like",
+        "avg_inference_fps",
+        "avg_control_fps",
+        "avg_inference_time_ms"
+      ],
+      "rows": [
+        [
+          "socket-insert-imf-attnres-ph32-exec16-emb384-l12-infer1-unfreeze-step50k-roll1x10-5880g01-20260506-195806",
+          "iMF-AttnRes",
+          "infer1, ph32, exec16, 50k",
+          "1275.22",
+          "1490.5",
+          "3.12",
+          "2250.0",
+          "84/100",
+          "0/100",
+          "64.226",
+          "5.863",
+          "16.186"
+        ],
+        [
+          "socket-insert-no-pretrain-ph16-emb384-l18-infer100-unfreeze-step150k-roll1x10-5090-20260506-193753",
+          "Diffusion-style VLA",
+          "infer100, ph16, 150k",
+          "861.53",
+          "668.0",
+          "2.58",
+          "2241.0",
+          "97/100",
+          "2/100",
+          "2.956",
+          "2.591",
+          "338.291"
+        ],
+        [
+          "socket-insert-no-pretrain-ph32-exec16-emb384-l18-infer100-unfreeze-step150k-roll1x10-5880g1-20260507-092227",
+          "Diffusion-style VLA",
+          "infer100, ph32, exec16, 150k",
+          "1019.39",
+          "804.0",
+          "2.47",
+          "2385.0",
+          "92/100",
+          "4/100",
+          "2.516",
+          "1.928",
+          "397.912"
+        ],
+        [
+          "socket-insert-imf-attnres-ph32-exec16-emb384-l12-infer2-drop005-unfreeze-step150k-roll1x10-5880g01-20260508-165022",
+          "iMF-AttnRes",
+          "infer2, ph32, exec16, 150k",
+          "1472.62",
+          "1825.0",
+          "3.29",
+          "2365.0",
+          "83/100",
+          "5/100",
+          "71.579",
+          "7.181",
+          "14.409"
+        ],
+        [
+          "socket-insert-imf-attnres-ph32-exec16-emb384-l12-infer3-drop005-unfreeze-step150k-roll1x10-5880g01-20260509-172423",
+          "iMF-AttnRes",
+          "infer3, ph32, exec16, 150k",
+          "1513.56",
+          "1901.5",
+          "3.28",
+          "2285.0",
+          "83/100",
+          "3/100",
+          "67.891",
+          "7.247",
+          "15.122"
+        ],
+        [
+          "act-socket-peg-224-20260508-170237",
+          "ACT",
+          "action chunking",
+          "289.6",
+          "13.0",
+          "1.29",
+          "2091.0",
+          "55/100",
+          "1/100",
+          "10.5171",
+          "2.2782",
+          "100.2116"
+        ],
+        [
+          "smolvla_socket_peg_bs80_100k_20260511_145727",
+          "SmolVLA",
+          "100k",
+          "466.16",
+          "106.0",
+          "1.72",
+          "4.0",
+          "89/100",
+          "0/100",
+          "382.53",
+          "16.84",
+          "2.614"
+        ]
+      ]
+    },
+    {
+      "label": "Object transfer / sim_transfer, 100-rollout performance and speed",
+      "headers": [
+        "run_name",
+        "method_family",
+        "horizon_or_steps",
+        "avg_reward",
+        "median_reward",
+        "avg_max_reward",
+        "max_reward",
+        "nonzero_reward",
+        "success_like",
+        "avg_inference_fps",
+        "avg_control_fps",
+        "avg_inference_time_ms"
+      ],
+      "rows": [
+        [
+          "diffusion_policy_native_dit_ddpm_resnet_best",
+          "Diffusion Policy native DiT + DDPM + ResNet",
+          "best checkpoint",
+          "319.2",
+          "6.0",
+          "1.68",
+          "1416.0",
+          "55/100",
+          "29/100",
+          "32.09",
+          "16.70",
+          "n/a"
+        ],
+        [
+          "embed384_layer18_best_checkpoint",
+          "Diffusion-style VLA",
+          "emb384, layer18",
+          "233.52",
+          "0.0",
+          "1.12",
+          "1436.0",
+          "39/100",
+          "17/100",
+          "1.859",
+          "1.649",
+          "n/a"
+        ],
+        [
+          "resnet18-multitoken-imf-emb256-l16-ph16-ex08-roll10-l20g2-20260406-112815",
+          "iMF multi-token ResNet18",
+          "step34999, ph16, exec08",
+          "260.66",
+          "0.0",
+          "1.12",
+          "1422.0",
+          "33/100",
+          "23/100",
+          "441.80",
+          "13.37",
+          "n/a"
+        ],
+        [
+          "imf-p2-full-attnres-vision-ph16-ex08-emb384-l12-b40-lr1p25e4-ms50k-l20g3-20260405-002424",
+          "iMF full AttnRes vision",
+          "ph16, exec08, 50k",
+          "228.42",
+          "0.0",
+          "0.94",
+          "1482.0",
+          "31/100",
+          "16/100",
+          "55.997",
+          "4.301",
+          "n/a"
+        ],
+        [
+          "imf-p1-ph16-ex16-emb384-l12-ms50k-l20g0-20260404-131223",
+          "iMF-AttnRes DiT only",
+          "ph16, exec16, 50k",
+          "240.64",
+          "0.0",
+          "1.02",
+          "1428.0",
+          "32/100",
+          "19/100",
+          "137.996",
+          "4.523",
+          "n/a"
+        ],
+        [
+          "imf-p1-ph32-ex08-emb384-l12-ms50k-l20g1-20260404-131223",
+          "iMF-AttnRes DiT only",
+          "ph32, exec08, 50k",
+          "163.28",
+          "0.0",
+          "0.76",
+          "1566.0",
+          "26/100",
+          "12/100",
+          "85.638",
+          "4.317",
+          "n/a"
+        ],
+        [
+          "imf-p1-ph32-ex32-emb384-l12-ms50k-5090-20260404-13122",
+          "iMF-AttnRes DiT only",
+          "ph32, exec32, 50k",
+          "260.72",
+          "0.0",
+          "1.18",
+          "1342.0",
+          "38/100",
+          "21/100",
+          "9.557",
+          "3.299",
+          "n/a"
+        ],
+        [
+          "imf-p1-ph16-ex08-emb384-l12-ms50k-5880g1-20260404-131223",
+          "iMF-AttnRes DiT only",
+          "ph16, exec08, 50k",
+          "229.56",
+          "0.0",
+          "1.18",
+          "1564.0",
+          "41/100",
+          "18/100",
+          "69.984",
+          "8.053",
+          "n/a"
+        ],
+        [
+          "imf-p1-ph08-ex08-emb384-l12-ms50k-5880g0-20260404-131223",
+          "iMF-AttnRes DiT only",
+          "ph08, exec08, 50k",
+          "237.02",
+          "0.0",
+          "1.32",
+          "1400.0",
+          "48/100",
+          "18/100",
+          "69.192",
+          "8.173",
+          "n/a"
+        ],
+        [
+          "imf-p1-ph32-ex16-emb384-l12-ms50k-l20g2-20260404-131223",
+          "iMF-AttnRes DiT only",
+          "ph32, exec16, 50k",
+          "49.88",
+          "0.0",
+          "0.32",
+          "1284.0",
+          "13/100",
+          "4/100",
+          "138.903",
+          "4.482",
+          "n/a"
+        ],
+        [
+          "sim-transfer-imf-attnres-ph32-exec16-emb384-l12-infer1-unfreeze-step50k-roll5x5-20260403-14130",
+          "iMF-AttnRes",
+          "infer1, ph32, exec16, 50k",
+          "526.22",
+          "86.0",
+          "2.14",
+          "1460.0",
+          "63/100",
+          "44/100",
+          "8.911",
+          "3.193",
+          "n/a"
+        ]
+      ]
+    },
+    {
+      "label": "Derived comparisons used in the paper draft",
+      "headers": [
+        "comparison",
+        "metric",
+        "numerator_run",
+        "denominator_run",
+        "numerator_value",
+        "denominator_value",
+        "derived_ratio_or_delta"
+      ],
+      "rows": [
+        [
+          "socket best iMF vs ph16 diffusion baseline",
+          "avg_reward ratio",
+          "iMF infer3 150k",
+          "diffusion ph16 infer100 150k",
+          "1513.56",
+          "861.53",
+          "1.76x"
+        ],
+        [
+          "socket best iMF vs ph32 diffusion baseline",
+          "avg_reward ratio",
+          "iMF infer3 150k",
+          "diffusion ph32 infer100 150k",
+          "1513.56",
+          "1019.39",
+          "1.48x"
+        ],
+        [
+          "socket best iMF vs ACT",
+          "avg_reward ratio",
+          "iMF infer3 150k",
+          "ACT",
+          "1513.56",
+          "289.6",
+          "5.23x"
+        ],
+        [
+          "socket best iMF vs SmolVLA",
+          "avg_reward ratio",
+          "iMF infer3 150k",
+          "SmolVLA",
+          "1513.56",
+          "466.16",
+          "3.25x"
+        ],
+        [
+          "socket iMF infer2 vs ph16 diffusion baseline",
+          "inference time reduction",
+          "iMF infer2 150k",
+          "diffusion ph16 infer100 150k",
+          "14.409",
+          "338.291",
+          "23.48x lower latency"
+        ],
+        [
+          "socket iMF infer3 vs ph16 diffusion baseline",
+          "inference fps ratio",
+          "iMF infer3 150k",
+          "diffusion ph16 infer100 150k",
+          "67.891",
+          "2.956",
+          "22.97x"
+        ],
+        [
+          "socket iMF infer2 vs ph32 diffusion baseline",
+          "inference fps ratio",
+          "iMF infer2 150k",
+          "diffusion ph32 infer100 150k",
+          "71.579",
+          "2.516",
+          "28.45x"
+        ],
+        [
+          "sim_transfer best iMF vs native diffusion policy",
+          "avg_reward ratio",
+          "sim-transfer iMF infer1",
+          "native diffusion policy",
+          "526.22",
+          "319.2",
+          "1.65x"
+        ],
+        [
+          "sim_transfer best iMF vs native diffusion policy",
+          "success_like delta",
+          "sim-transfer iMF infer1",
+          "native diffusion policy",
+          "44",
+          "29",
+          "+15 episodes"
+        ],
+        [
+          "sim_transfer ResNet18 multitoken iMF vs embed384 layer18 baseline",
+          "inference fps ratio",
+          "ResNet18 multitoken iMF",
+          "embed384 layer18",
+          "441.80",
+          "1.859",
+          "237.65x"
+        ]
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/workspace/outline.json b/workspace/outline.json
new file mode 100644
index 0000000..69d856a
--- /dev/null
+++ b/workspace/outline.json
@@ -0,0 +1,369 @@
+{
+  "plotting_plan": [
+    {
+      "figure_id": "fig_method_overview",
+      "title": "iMF-AttnRes VLA Overview",
+      "plot_type": "diagram",
+      "data_source": "idea.md",
+      "objective": "Paper Banana prompt placeholder for a conceptual pipeline diagram showing visual observations and robot state entering a VLA policy, iMF predicting average flow with one-to-few inference evaluations, AttnRes aggregating depth-wise residual features, and chunked robot actions being executed in RoboIMI.",
+      "aspect_ratio": "16:9"
+    },
+    {
+      "figure_id": "fig_socket_reward_latency",
+      "title": "Socket Insertion Reward-Latency Tradeoff",
+      "plot_type": "plot",
+      "data_source": "experimental_log.md",
+      "objective": "Paper Banana prompt placeholder for a scatter/bar hybrid plot comparing socket-insert avg_reward against avg_inference_time_ms or inference FPS for iMF-AttnRes, diffusion-style VLA, ACT, and SmolVLA, emphasizing that iMF-AttnRes reaches higher reward than diffusion-style baselines while using roughly 14--16 ms inference latency instead of hundreds of milliseconds.",
+      "aspect_ratio": "4:3"
+    },
+    {
+      "figure_id": "fig_sim_transfer_ablation",
+      "title": "Sim Transfer Ablation Summary",
+      "plot_type": "plot",
+      "data_source": "experimental_log.md",
+      "objective": "Paper Banana prompt placeholder for a grouped bar chart over sim_transfer ablations showing avg_reward and success_like episode count for native diffusion policy, best iMF-AttnRes, multi-token ResNet18 iMF, full-AttnRes vision, and horizon/execution variants.",
+      "aspect_ratio": "4:3"
+    },
+    {
+      "figure_id": "fig_imf_attnres_components",
+      "title": "iMF and AttnRes Component Mechanics",
+      "plot_type": "diagram",
+      "data_source": "idea.md",
+      "objective": "Paper Banana prompt placeholder for a technical diagram contrasting classical flow matching instantaneous velocity prediction, Mean Flow average velocity prediction, iMF's corrected instantaneous-velocity training relation, and AttnRes softmax aggregation over preceding residual outputs.",
+      "aspect_ratio": "16:9"
+    }
+  ],
+  "intro_related_work_plan": {
+    "introduction_strategy": {
+      "hook_hypothesis": "Diffusion-style action generators can produce expressive robot policies, but their iterative denoising or ODE-style sampling creates a latency bottleneck for closed-loop robot control.",
+      "problem_gap_hypothesis": "Existing fast robot policies often trade away action-distribution expressivity or require distillation, while existing one-step flow methods have not fully explored improved mean-flow training and depth-wise residual aggregation in VLA imitation learning.",
+      "search_directions": [
+        "Find foundational and survey papers on imitation learning and visuomotor policy learning for robotic manipulation before 2026-04-15",
+        "Find papers on diffusion policy and action diffusion for visuomotor robot control before 2026-04-15",
+        "Find papers on vision-language-action robot policies, including RT-1, RT-2, OpenVLA, SmolVLA, and flow-based VLA models before 2026-04-15",
+        "Find papers on flow matching, rectified flow, Mean Flow, and improved Mean Flow for one-step or few-step generation before 2026-04-15",
+        "Find papers on residual connections, Transformers, PreNorm residual architectures, hyper-connections, and attention residual aggregation before 2026-04-15"
+      ]
+    },
+    "related_work_strategy": {
+      "overview": "Organize related work around robot action generation, fast flow-based generation, and residual/attention architecture design. The review should explain why a one-to-few-step iMF policy with AttnRes is a plausible alternative to iterative diffusion action generation.",
+      "subsections": [
+        {
+          "subsection_title": "2.1 Diffusion and Flow Policies for Robot Action Generation",
+          "methodology_cluster": "Diffusion Policy, action diffusion, flow matching, and generative action policies",
+          "sota_investigation_mission": "Identify influential robot manipulation policies that use diffusion, flow matching, or iterative generative sampling, and summarize their sampling-step and latency implications.",
+          "limitation_hypothesis": "Iterative sampling improves expressivity but makes high-frequency closed-loop control expensive, especially when policies require tens or hundreds of inference evaluations per action chunk.",
+          "limitation_search_queries": [
+            "Diffusion Policy Visuomotor Policy Learning via Action Diffusion latency sampling steps",
+            "robot manipulation action diffusion policy inference speed sampling steps",
+            "flow matching robot policy action generation one-step few-step"
+          ],
+          "bridge_to_our_method": "iMF-AttnRes targets the same expressive action-generation setting but trains the policy to predict an average flow usable with one-to-few inference evaluations."
+        },
+        {
+          "subsection_title": "2.2 Vision-Language-Action Models and Action Chunking",
+          "methodology_cluster": "VLA policies, action tokenization, action chunking, and transformer-based robot policies",
+          "sota_investigation_mission": "Survey VLA and transformer imitation-learning systems that condition robot actions on language, images, and robot state, including models that improve deployment speed via chunking or compact action representations.",
+          "limitation_hypothesis": "Compact VLA and chunking approaches can improve speed, but may not directly address the multi-step generative sampling cost of diffusion-style policies or the training instability of naive one-step flow policies.",
+          "limitation_search_queries": [
+            "OpenVLA vision language action robot policy imitation learning",
+            "RT-1 RT-2 robotics transformer vision language action control",
+            "Action Chunking with Transformers ALOHA imitation learning",
+            "SmolVLA efficient vision language action robotics"
+          ],
+          "bridge_to_our_method": "The proposed policy keeps the VLA imitation-learning framing while replacing slow iterative action sampling with iMF and strengthening deep policy representation with AttnRes."
+        },
+        {
+          "subsection_title": "2.3 Mean-Flow Training for One-Step Generation",
+          "methodology_cluster": "Flow matching, rectified flow, Mean Flow, improved Mean Flow, and fast-forward generative models",
+          "sota_investigation_mission": "Trace the development from flow matching and rectified flow to Mean Flow and improved Mean Flow, focusing on how average velocity prediction enables one-step or few-step generation.",
+          "limitation_hypothesis": "Naively substituting conditional velocities into nonlinear mean-flow training relations can introduce unstable objectives; improved Mean Flow addresses this with a corrected training relation that better preserves flow-matching stability.",
+          "limitation_search_queries": [
+            "Mean Flows for One-step Generative Modeling average velocity",
+            "Improved Mean Flows fast-forward generative models conditional velocity marginal velocity",
+            "Flow Matching for Generative Modeling conditional vector field",
+            "Rectified Flow generative modeling one step sampling"
+          ],
+          "bridge_to_our_method": "We adapt the improved mean-flow idea from image generation to robot action generation, where lower sampling latency directly affects control frequency."
+        },
+        {
+          "subsection_title": "2.4 Residual Aggregation and Attention Residuals in Deep Transformers",
+          "methodology_cluster": "Residual connections, PreNorm Transformers, hyper-connections, and AttnRes",
+          "sota_investigation_mission": "Investigate why residual paths are central to deep networks and how learned or attention-based residual aggregation can mitigate feature dilution and gradient imbalance in deep transformers.",
+          "limitation_hypothesis": "Standard unit-weight residual accumulation treats all previous layer outputs equally, which can dilute individual layer contributions and produce depth-wise representation redundancy in deep VLA policies.",
+          "limitation_search_queries": [
+            "Attention Residuals AttnRes PreNorm dilution hidden-state growth",
+            "residual connections transformer PreNorm gradient flow depth",
+            "hyper-connections deep transformers residual aggregation",
+            "Deep Residual Learning for Image Recognition residual connections"
+          ],
+          "bridge_to_our_method": "AttnRes supplies a drop-in way for each policy layer to selectively aggregate earlier residual outputs rather than relying on fixed unit-weight accumulation."
+        }
+      ]
+    }
+  },
+  "section_plan": [
+    {
+      "section_title": "Abstract",
+      "subsections": [
+        {
+          "subsection_title": "Abstract Content",
+          "content_bullets": [
+            "State the robotics problem: diffusion-style VLA policies are expressive but slow when closed-loop control needs many policy evaluations.",
+            "Introduce iMF-AttnRes as a VLA imitation-learning policy combining improved Mean Flow for one-to-few-step action generation and Attention Residuals for depth-wise residual aggregation.",
+            "Report grounded socket-insert results from experimental_log.md: best iMF-AttnRes reaches avg_reward 1513.56 and median_reward 1901.5 with about 15.122 ms inference time, compared with diffusion-style infer100 baselines at 861.53/338.291 ms and 1019.39/397.912 ms.",
+            "Report grounded sim_transfer result: best iMF-AttnRes reaches avg_reward 526.22 and 44/100 success-like episodes compared with native diffusion policy at avg_reward 319.2 and 29/100 success-like episodes.",
+            "Mention limitations: simulation-only, heterogeneous hardware, and sensitivity to horizon/execution settings."
+          ],
+          "citation_hints": []
+        },
+        {
+          "subsection_title": "Abstract Grounding Checks",
+          "content_bullets": [
+            "Ensure every numeric claim in the abstract is copied from experimental_log.md Table 1, Table 2, or Table 3.",
+            "Do not cite papers in the abstract unless the final manuscript style requires it; preserve double-blind anonymity."
+          ],
+          "citation_hints": []
+        }
+      ]
+    },
+    {
+      "section_title": "1. Introduction",
+      "subsections": [
+        {
+          "subsection_title": "1.1 Motivation and Problem Setting",
+          "content_bullets": [
+            "Motivate fast closed-loop manipulation in RoboIMI socket-insert and sim_transfer environments using the latency and reward metrics in experimental_log.md.",
+            "Explain that iterative diffusion-style inference can require infer100 evaluations in the baselines, producing hundreds of milliseconds per inference on socket-insert runs.",
+            "Frame the research question: can a one-to-few-step average-flow action generator preserve or improve manipulation reward while reducing inference latency?"
+          ],
+          "citation_hints": [
+            "Chi et al. (Diffusion Policy: Visuomotor Policy Learning via Action Diffusion)",
+            "research paper or technical report introducing 'Vision-Language-Action robot policies'"
+          ]
+        },
+        {
+          "subsection_title": "1.2 Contributions",
+          "content_bullets": [
+            "List contributions: adapting improved Mean Flow to VLA action generation, integrating AttnRes into the policy transformer, and evaluating on two RoboIMI simulated manipulation tasks.",
+            "State that the paper provides speed-quality comparisons against diffusion-style VLA, Diffusion Policy native DiT/DDPM/ResNet, ACT, and SmolVLA baselines using the exact 100-rollout statistics from experimental_log.md.",
+            "Emphasize honest scope: first draft simulation evidence rather than real-robot transfer."
+          ],
+          "citation_hints": [
+            "Geng et al. (Mean Flows for One-step Generative Modeling)",
+            "Geng et al. (Improved Mean Flows: On the Challenges of Fast-Forward Generative Models)",
+            "research paper or technical report introducing 'Attention Residuals'"
+          ]
+        }
+      ]
+    },
+    {
+      "section_title": "2. Related Work",
+      "subsections": [
+        {
+          "subsection_title": "2.1 Diffusion and Flow Policies for Robot Action Generation",
+          "content_bullets": [
+            "Discuss diffusion-policy-style visuomotor action generation and its reliance on iterative denoising or repeated policy evaluations.",
+            "Connect diffusion and flow matching as continuous generative modeling tools, then distinguish iMF-AttnRes by its average-flow one-to-few-step inference objective.",
+            "Use related-work cluster 2.1 from outline.json to bridge to the method."
+          ],
+          "citation_hints": [
+            "Chi et al. (Diffusion Policy: Visuomotor Policy Learning via Action Diffusion)",
+            "Ho et al. (Denoising Diffusion Probabilistic Models)",
+            "Lipman et al. (Flow Matching for Generative Modeling)",
+            "Liu et al. (Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow)",
+            "Peebles and Xie (Scalable Diffusion Models with Transformers)"
+          ]
+        },
+        {
+          "subsection_title": "2.2 Vision-Language-Action Models and Action Chunking",
+          "content_bullets": [
+            "Summarize transformer and VLA robot policies as the broader setting for the proposed policy.",
+            "Discuss action chunking and compact VLA policies as complementary routes to practical control speed.",
+            "Position iMF-AttnRes as an action-generation module that can coexist with VLA conditioning and chunked execution."
+          ],
+          "citation_hints": [
+            "Brohan et al. (RT-1: Robotics Transformer for Real-World Control at Scale)",
+            "Brohan et al. (RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control)",
+            "Kim et al. (OpenVLA: An Open-Source Vision-Language-Action Model)",
+            "Zhao et al. (Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware)",
+            "research paper or technical report introducing 'SmolVLA'"
+          ]
+        },
+        {
+          "subsection_title": "2.3 Mean-Flow Training for One-Step Generation",
+          "content_bullets": [
+            "Use the derivations in idea.md to explain the distinction between instantaneous velocity v(x_t,t) and average velocity over [r,t].",
+            "Explain the Mean Flow relation between average velocity and instantaneous velocity and why the JVP term appears.",
+            "Explain the improved Mean Flow correction from idea.md: using the corrected relation to retain flow-matching training stability rather than directly substituting conditional velocity inside a nonlinear JVP expression."
+          ],
+          "citation_hints": [
+            "Geng et al. (Mean Flows for One-step Generative Modeling)",
+            "Geng et al. (Improved Mean Flows: On the Challenges of Fast-Forward Generative Models)",
+            "Lipman et al. (Flow Matching for Generative Modeling)",
+            "Liu et al. (Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow)"
+          ]
+        },
+        {
+          "subsection_title": "2.4 Residual Aggregation and Attention Residuals",
+          "content_bullets": [
+            "Derive the residual accumulation view in idea.md: x_t = y_0 + ... + y_t and y_{t+1}=f_{t+1}(sum y_s).",
+            "Explain the generalization to weighted residual aggregation and the AttnRes form a_{t+1,s} proportional to exp(w_{t+1} dot RMSNorm(y_s)).",
+            "Discuss why learned residual aggregation may help deeper policy networks avoid feature dilution and gradient imbalance."
+          ],
+          "citation_hints": [
+            "He et al. (Deep Residual Learning for Image Recognition)",
+            "Vaswani et al. (Attention Is All You Need)",
+            "research paper or technical report introducing 'Attention Residuals'",
+            "research paper or technical report introducing 'PreNorm Transformers'"
+          ]
+        }
+      ]
+    },
+    {
+      "section_title": "3. Method",
+      "subsections": [
+        {
+          "subsection_title": "3.1 Problem Formulation",
+          "content_bullets": [
+            "Define the imitation-learning policy input as multi-view visual observations, robot state, and optional language/task conditioning from the RoboIMI environment, and define the output as an action chunk of length exec.",
+            "Introduce the evaluation setting from experimental_log.md: socket-insert and sim_transfer 100-rollout evaluations with reward, success-like count, inference FPS, control FPS, and inference time.",
+            "Avoid inventing dataset sizes, optimizer settings, or training details not present in idea.md or experimental_log.md."
+          ],
+          "citation_hints": [
+            "research paper or technical report introducing 'behavior cloning for robotics'",
+            "research paper or technical report introducing 'Transformer architecture'"
+          ]
+        },
+        {
+          "subsection_title": "3.2 Improved Mean Flow Action Generation",
+          "content_bullets": [
+            "Formalize the ODE dx_t/dt = v(x_t,t) and average velocity ̄v(z_t,r,t)=1/(t-r) int_r^t v(z_tau,tau)d tau using the notation from idea.md.",
+            "Show the Mean Flow identity ̄v = v - (t-r) d̄v/dt and the JVP expression jvp(̄v,(z,r,t),(v,0,1)).",
+            "Explain the iMF training relation V_theta(z_t)=u_theta(z_t)+(t-r)JVP_sg(u_theta;v_theta) and that gradients update the average-flow branch u_theta, supporting one-to-few-step inference."
+          ],
+          "citation_hints": [
+            "Geng et al. (Mean Flows for One-step Generative Modeling)",
+            "Geng et al. (Improved Mean Flows: On the Challenges of Fast-Forward Generative Models)",
+            "Lipman et al. (Flow Matching for Generative Modeling)"
+          ]
+        },
+        {
+          "subsection_title": "3.3 Attention Residual Policy Transformer",
+          "content_bullets": [
+            "Use the residual decomposition from idea.md to show fixed residual accumulation as a uniform sum over layer outputs.",
+            "Define AttnRes weights a_{t+1,s} with a softmax over w_{t+1} dot RMSNorm(y_s), then define the layer input as a weighted sum of previous residual outputs.",
+            "State that the paper uses AttnRes primarily in the policy transformer in the strongest variants; the full-AttnRes vision ablation underperformed in sim_transfer."
+          ],
+          "citation_hints": [
+            "He et al. (Deep Residual Learning for Image Recognition)",
+            "Vaswani et al. (Attention Is All You Need)",
+            "research paper or technical report introducing 'Attention Residuals'"
+          ]
+        },
+        {
+          "subsection_title": "3.4 Inference and Control Loop",
+          "content_bullets": [
+            "Describe one-to-few inference evaluations for iMF-AttnRes, using infer1/infer2/infer3 in socket runs and infer1 in sim_transfer best run.",
+            "Contrast this with diffusion-style infer100 baselines in experimental_log.md.",
+            "Explain that predicted chunks are executed for exec steps, e.g., exec16 in the best socket and sim_transfer iMF-AttnRes variants."
+          ],
+          "citation_hints": [
+            "Chi et al. (Diffusion Policy: Visuomotor Policy Learning via Action Diffusion)",
+            "Zhao et al. (Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware)"
+          ]
+        }
+      ]
+    },
+    {
+      "section_title": "4. Experiments",
+      "subsections": [
+        {
+          "subsection_title": "4.1 Experimental Setup",
+          "content_bullets": [
+            "Describe RoboIMI socket-insert and sim_transfer environments as simulation benchmarks using only details stated in experimental_log.md.",
+            "Define metrics exactly as in experimental_log.md: avg_reward, median_reward, avg_max_reward, max_reward, nonzero_reward, success_like, avg_inference_fps, avg_control_fps, avg_inference_time_ms.",
+            "State that each row is based on 100 simulation rollouts and that hardware varies across run names, so speed is practical but not fully hardware-normalized."
+          ],
+          "citation_hints": [
+            "research paper or technical report introducing 'Diffusion Policy: Visuomotor Policy Learning via Action Diffusion'",
+            "research paper or technical report introducing 'ACT action chunking transformer'",
+            "research paper or technical report introducing 'SmolVLA'"
+          ]
+        },
+        {
+          "subsection_title": "4.2 Socket Peg Insertion Results",
+          "content_bullets": [
+            "Create a LaTeX table from Table 1 in experimental_log.md with the seven socket policies and their reward/speed metrics.",
+            "State that best iMF-AttnRes infer3 obtains avg_reward 1513.56 and median_reward 1901.5, exceeding diffusion-style ph16 infer100 avg_reward 861.53 and ph32 infer100 avg_reward 1019.39.",
+            "State that iMF-AttnRes infer2 latency is 14.409 ms and infer3 latency is 15.122 ms, compared with 338.291 ms and 397.912 ms for diffusion-style infer100 baselines.",
+            "Discuss the nonzero_reward caveat: diffusion-style ph16 has 97/100 nonzero episodes, but much lower median and average reward than iMF-AttnRes."
+          ],
+          "citation_hints": []
+        },
+        {
+          "subsection_title": "4.3 Sim Transfer Results and Ablations",
+          "content_bullets": [
+            "Create a LaTeX table from Table 2 in experimental_log.md summarizing native diffusion policy, diffusion-style VLA, and iMF ablations.",
+            "Report that sim-transfer iMF-AttnRes infer1 reaches avg_reward 526.22 and 44/100 success-like episodes, compared with native diffusion policy at avg_reward 319.2 and 29/100 success-like episodes.",
+            "Analyze ablations: multi-token ResNet18 iMF is very fast at 441.80 FPS but lower reward; full-AttnRes vision underperforms; horizon/execution choices are sensitive."
+          ],
+          "citation_hints": []
+        },
+        {
+          "subsection_title": "4.4 Speed-Quality Tradeoff and Failure Modes",
+          "content_bullets": [
+            "Use Table 3 from experimental_log.md for derived ratios: 1.76x socket reward over ph16 diffusion baseline, 22.97x socket inference FPS over ph16 diffusion baseline, and 1.65x sim_transfer average reward over native diffusion policy.",
+            "Discuss failure modes from experimental_log.md: simulation-only evidence, heterogeneous hardware, threshold differences across tasks, and sensitivity to horizon/execution and vision-residual design.",
+            "Emphasize that iMF-AttnRes improves latency but still needs controlled hardware-normalized studies and real-robot transfer validation."
+          ],
+          "citation_hints": []
+        }
+      ]
+    },
+    {
+      "section_title": "5. Limitations",
+      "subsections": [
+        {
+          "subsection_title": "5.1 Scope and Evaluation Limits",
+          "content_bullets": [
+            "Explicitly satisfy CoRL requirement by describing simulation-only evaluation and lack of real-robot experiments.",
+            "State heterogeneous hardware makes speed comparisons imperfect and motivates future hardware-normalized benchmarking.",
+            "State that iMF-AttnRes sensitivity to horizon/execution choices and full-AttnRes vision underperformance are current limitations."
+          ],
+          "citation_hints": []
+        },
+        {
+          "subsection_title": "5.2 Future Work",
+          "content_bullets": [
+            "Propose future work: real-robot transfer, more seeds, hardware-normalized latency tests, and better criteria for where to apply AttnRes in vision vs policy modules.",
+            "Mention possible integration with efficient VLA backbones and action tokenization but avoid claiming untested results."
+          ],
+          "citation_hints": []
+        }
+      ]
+    },
+    {
+      "section_title": "6. Conclusion",
+      "subsections": [
+        {
+          "subsection_title": "Conclusion Content",
+          "content_bullets": [
+            "Summarize that improved Mean Flow plus AttnRes is a practical recipe for low-latency VLA action generation in the reported RoboIMI simulations.",
+            "Restate the strongest socket and sim_transfer quantitative evidence without adding new numbers.",
+            "Close with a balanced statement that this is promising simulation evidence requiring real-robot validation."
+          ],
+          "citation_hints": []
+        },
+        {
+          "subsection_title": "Concluding Caveats",
+          "content_bullets": [
+            "End with simulation-only and hardware-normalization caveats from experimental_log.md rather than overclaiming real-robot deployment.",
+            "Mention future validation directions without adding unsupported experiments."
+          ],
+          "citation_hints": []
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new file mode 100644
index 0000000..d37122c
--- /dev/null
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+  "note": "First PaperOrchestra draft. Figure rendering intentionally skipped per user request; Paper Banana prompts stored as placeholders in figures/*.paperbanana_prompt.txt and embedded in LaTeX tcolorboxes. Exa discovery was attempted; Semantic Scholar API key was not visible to shell, and unauthenticated S2 was rate-limited, so refs.bib was assembled from verified known paper metadata plus web/arXiv checks where possible.",
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+}
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diff --git a/workspace/raw_candidates.json b/workspace/raw_candidates.json
new file mode 100644
index 0000000..5d89904
--- /dev/null
+++ b/workspace/raw_candidates.json
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+{
+  "candidates": [
+    {
+      "title": "Visuomotor policy learning via action diffusion - ACM Digital Library",
+      "snippet": "Diffusion policy: : Visuomotor policy learning via action diffusion: International Journal of Robotics Research: Vol 4\n[...]\n, No 1\n[...]\nother-periodical;requested\n[...]\n:string:\n[...]\nPublication Websites;subPage:\n[...]\n:Basic Abstract\n[...]\n;page:string:Article/Chapter View;ctype:string:Journal Content;group\n[...]\n:acm-\n[...]\ntype>other-periodical;website:website:\n[...]\n-site;\n[...]\n:issue:\n[...]\n\\:10.5\n[...]\n55/rbrs.20\n[...]\n.44.issue-10-11;csubtype:string:Periodical;taxonomy:taxonomy:acm-pubtype;pageGroup:string:Publication Pages\"> skip to main content\n\n \n\n \n\nContents\n[...]\nThis paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 15 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion mode",
+      "source_url": "https://dl.acm.org/doi/10.1177/02783649241273668",
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+      "_exa_published_date": null
+    },
+    {
+      "title": "Visuomotor Policy Learning via Action Diffusion - arXiv",
+      "snippet": "# Diffusion Policy: Visuomotor Policy Learning via Action Diffusion\n[...]\nThis paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 15 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details is available diffusion-policy.cs.columbia.edu\n[...]\nIn this work, we s",
+      "source_url": "https://arxiv.org/abs/2303.04137",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2303.04137",
+      "_exa_published_date": "2023-03-07T00:00:00.000Z"
+    },
+    {
+      "title": "[2303.04137v5] Diffusion Policy: Visuomotor Policy Learning via Action Diffusion",
+      "snippet": "[2303.04137v5] Diffusion Policy: Visuomotor Policy Learning via Action Diffusion\n[...]\n# Title:Diffusion Policy: Visuomotor Policy Learning via Action Diffusion\n[...]\n> Abstract:This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 12 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models.",
+      "source_url": "http://arxiv.org/abs/2303.04137v5",
+      "discovered_for": [
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+      ],
+      "_exa_id": "http://arxiv.org/abs/2303.04137v5",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Visuomotor Policy Learning via Action Diffusion - arXiv",
+      "snippet": "Diffusion Policy\n\n# Diffusion Policy\n\nCheng Chi1, Siyuan Feng2, Yilun Du3, Zhenjia Xu1, Eric Cousineau2, Benjamin Burchfiel2, Shuran Song1 1 Columbia University 2 Toyota Research Institute 3 MIT https://diffusion-policy.cs.columbia.edu\n\n# Diffusion Policy: Visuomotor Policy Learning via Action Diffusion\n\nCheng Chi1, Siyuan Feng2, Yilun Du3, Zhenjia Xu1, Eric Cousineau2, Benjamin Burchfiel2, Shuran Song1 1 Columbia University 2 Toyota Research Institute 3 MIT https://diffusion-policy.cs.columbia.edu\n\n###### Abstract\n\nThis paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 12 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper",
+      "source_url": "https://arxiv.org/html/2502.12371v1",
+      "discovered_for": [
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+      "_exa_id": "https://arxiv.org/html/2502.12371v1",
+      "_exa_published_date": "2025-02-17T00:00:00.000Z"
+    },
+    {
+      "title": "[2303.04137] Diffusion Policy - ar5iv - arXiv",
+      "snippet": "# Diffusion Policy: Visuomotor Policy Learning via Action Diffusion\n[...]\nThis paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot’s visuomotor policy as a conditional denoising diffusion process. We benchmark Diffusion Policy across 12 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%. Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps. We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability. To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer. We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models. Code, data, and training details will be publicly available.\n[...]\nIn this work, we seek to address thi",
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+      "_exa_published_date": null
+    },
+    {
+      "title": "[2006.11239v2] Denoising Diffusion Probabilistic Models",
+      "snippet": "[2006.11239\n[...]\n] Denoising Diffusion Probabilistic Models\n[...]\n# Title:Denoising Diffusion Probabilistic Models\n[...]\nAuthors: Jonathan Ho, Ajay Jain, Pieter Abbeel\n[...]\n> Abstract:We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at this https URL\n\n \n\nhttps://doi.org/10.48550/arXiv.2006.11239\n\n \n\narXiv-issued DOI via DataCite",
+      "source_url": "https://arxiv.org/abs/2006.11239v2",
+      "discovered_for": [
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+      "_exa_id": "https://arxiv.org/abs/2006.11239v2",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[2006.11239] Denoising Diffusion Probabilistic Models - arXiv",
+      "snippet": "[2006.11239] Denoising Diffusion Probabilistic Models\n[...]\n# Denoising Diffusion Probabilistic Models\n[...]\nJonathan Ho UC Berkeley jonathanho@berkeley.edu &Ajay Jain UC Berkeley ajayj@berkeley.edu &Pieter Abbeel UC Berkeley pabbeel@cs.berkeley.edu\n[...]\nWe present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our implementation is available at https://github.com/hojonathanho/diffusion.\n[...]\nThis paper presents progress in diffusion probabilistic models [53]. A diffusion probabilistic model (which we will call a “diffusion model” for brevity) is a parameterized Markov chain trained using variational inference to produce samples matching the data after finite time. Transitions of this chain are learned to reverse a diffusion process, which is a Markov chain that gradually adds noise to the data in the opposite direction of s",
+      "source_url": "https://arxiv.org/abs/2006.11239",
+      "discovered_for": [
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+      "_exa_id": "https://arxiv.org/abs/2006.11239",
+      "_exa_published_date": "2020-06-19T00:00:00.000Z"
+    },
+    {
+      "title": "Denoising Diffusion Probabilistic Models",
+      "snippet": "Denoising Diffusion Probabilistic Models \n\nAuthorFeedback Bibtex MetaReview Paper Review Supplemental\n\n## Abstract\n[...]\nWe present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.\n\n \n\nDo not remove: This comment is monitored to verify that the site is working properly",
+      "source_url": "https://papers.nips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html",
+      "discovered_for": [
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+      "_exa_published_date": null
+    },
+    {
+      "title": "",
+      "snippet": "Denoising Diffusion Probabilistic Models\n[...]\njonathanho@berkeley.edu\n[...]\nAjay Jain\n[...]\najayj@berkeley.edu\n[...]\nPieter Abbeel\n[...]\npabbeel@cs.berkeley.edu\n[...]\nWe present high quality image synthesis results using diffusion probabilistic models,\n[...]\na class of latent variable models inspired by considerations from nonequilibrium\n[...]\nthermodynamics. Our best results are obtained by training on a weighted variational\n[...]\nbound designed according to a novel connection between diffusion probabilistic\n[...]\nmodels and denoising score matching with Langevin dynamics, and our models nat\u0002urally admit a progressive lossy decompression scheme that can be interpreted as a\n[...]\ngeneralization of autoregressive decoding. On the unconditional CIFAR10 dataset,\n[...]\nwe obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On\n[...]\n256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our imple\u0002mentation is available at https://github.com/hojonathanho/diffusion.\n[...]\nThis paper presents progress in diffusion probabilistic models [50]. A diffusion probabilistic model\n[...]\n(which we will call a “diffusion model” for brevity) is a parameterized Markov chain trained using\n[...]\nvariational inference to produce samples matching the data after finite time. Transitions of this chain\n[...]\nare learned to reverse a diffusion process, which is a Markov chain that gradually adds noise to the\n[...]\ndata in the opposite direction of sampling until signal",
+      "source_url": "https://arxiv.org/pdf/2006.11239v1",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2006.11239v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] Denoising Diffusion Probabilistic Models - arXiv",
+      "snippet": "Denoising Diffusion Probabilistic Models\n[...]\njonathanho@berkeley.edu\n[...]\nAjay Jain\n[...]\najayj@berkeley.edu\n[...]\nPieter Abbeel\n[...]\npabbeel@cs.berkeley.edu\n[...]\nWe present high quality image synthesis results using diffusion probabilistic models,\n[...]\na class of latent variable models inspired by considerations from nonequilibrium\n[...]\nthermodynamics. Our best results are obtained by training on a weighted variational\n[...]\nbound designed according to a novel connection between diffusion probabilistic\n[...]\nmodels and denoising score matching with Langevin dynamics, and our models nat\u0002urally admit a progressive lossy decompression scheme that can be interpreted as a\n[...]\ngeneralization of autoregressive decoding. On the unconditional CIFAR10 dataset,\n[...]\nwe obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On\n[...]\n256x256 LSUN, we obtain sample quality similar to ProgressiveGAN. Our imple\u0002mentation is available at https://github.com/hojonathanho/diffusion.\n[...]\nThis paper presents progress in diffusion probabilistic models [53]. A diffusion probabilistic model\n[...]\n(which we will call a “diffusion model” for brevity) is a parameterized Markov chain trained using\n[...]\nvariational inference to produce samples matching the data after finite time. Transitions of this chain\n[...]\nare learned to reverse a diffusion process, which is a Markov chain that gradually adds noise to the\n[...]\ndata in the opposite direction of sampling until signal",
+      "source_url": "https://arxiv.org/pdf/2006.11239",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2006.11239",
+      "_exa_published_date": "2020-12-16T00:00:00.000Z"
+    },
+    {
+      "title": "[2212.09748] Scalable Diffusion Models with Transformers - arXiv",
+      "snippet": "William Peebles* UC Berkeley Saining Xie New York University\n[...]\nWe explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops—through increased transformer depth/width or increased number of input tokens—consistently have lower FID. In addition to possessing good scalability properties, our largest DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512 $\\times$ 512 and 256 $\\times$ 256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.\n[...]\n, or Di\n[...]\nfor short. Di\n[...]\nadhere to the best practices of Vision Transformers (ViTs) [10], which have been shown to scale more effectively for visual recognition than\n[...]\nconvolutional networks (e.g., Res\n[...]\n[15]).\n[...]\nMore specifically, we study the scaling behavior of transformers with respect to network complexity vs. sample quality. We show that by constructing and benchmarking the DiT design space under the Latent Diffusion Models (LDMs) [48] framework, where diffusion models are trained within a VAE’s latent space, we can successfully replace the U-Net backbone with a transformer. We further show that DiTs are scalable architectures for diff",
+      "source_url": "https://arxiv.org/abs/2212.09748",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2212.09748",
+      "_exa_published_date": "2022-12-19T00:00:00.000Z"
+    },
+    {
+      "title": "Scalable Diffusion Models with Transformers | IEEE Conference Publication | IEEE Xplore",
+      "snippet": "Scalable Diffusion Models with Transformers | IEEE Conference Publication | IEEE Xplore\n\n \n\n \n\n \n\n### IEEE Account",
+      "source_url": "https://ieeexplore.ieee.org/document/10377858/",
+      "discovered_for": [
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+      ],
+      "_exa_id": "https://ieeexplore.ieee.org/document/10377858/",
+      "_exa_published_date": "2025-05-14T00:00:00.000Z"
+    },
+    {
+      "title": "[2212.09748v1] Scalable Diffusion Models with Transformers",
+      "snippet": "[2212.09748v1] Scalable Diffusion Models with Transformers\n[...]\n# Title:Scalable Diffusion Models with Transformers\n[...]\nAuthors: William Peebles, Saining Xie\n[...]\n> Abstract:We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops -- through increased transformer depth/width or increased number of input tokens -- consistently have lower FID. In addition to possessing good scalability properties, our largest DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512x512 and 256x256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.",
+      "source_url": "http://arxiv.org/abs/2212.09748v1",
+      "discovered_for": [
+        "rw.diffusion_policy"
+      ],
+      "_exa_id": "http://arxiv.org/abs/2212.09748v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] Scalable Diffusion Models with Transformers | Semantic Scholar",
+      "snippet": "[PDF] Scalable Diffusion Models with Transformers | Semantic Scholar \n\nNavigate Paper Download (opens in a new tab) Share\n[...]\n```\n@article{Peebles2022ScalableDM,\n  title={Scalable Diffusion Models with Transformers},\n  author={William S. Peebles and Saining Xie},\n  journal={2023 IEEE/CVF International Conference on Computer Vision (ICCV)},\n  year={2022},\n  pages={4172-4182},\n  url={https://api.semanticscholar.org/CorpusID:254854389}\n}\n```",
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+      "_exa_published_date": "2022-12-19T14:39:50.000Z"
+    },
+    {
+      "title": "Scalable Diffusion Models with Transformers - IEEE Xplore",
+      "snippet": "Scalable Diffusion Models with Transformers | IEEE Conference Publication | IEEE Xplore\n**\n\n### IEEE Account\n* Change Username/Password\n* Update Address\n### Purchase Details\n* Payment Options\n* Order History\n* View Purchased Documents\n### Profile Information\n* Communications Preferences\n* Profession and Education\n* Technical Interests\n### Need Help?\n* **US & Canada:**+1 800 678 4333\n* **Worldwide:**+1 732 981 0060\n* Contact & Support\n* About IEEE*Xplore*\n* Contact Us\n* Help\n* Accessibility\n* Terms of Use\n* Nondiscrimination Policy\n* Sitemap\n* Privacy & Opting Out of Cookies\nA not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.\n© Copyright 2025 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.\n**",
+      "source_url": "https://ieeexplore.ieee.org/iel7/10376473/10376477/10377858.pdf",
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+      "_exa_id": "https://ieeexplore.ieee.org/iel7/10376473/10376477/10377858.pdf",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] Flow Matching for Generative Modeling - arXiv",
+      "snippet": "FLOW MATCHING FOR GENERATIVE MODELING\n[...]\nYaron Lipman1,2 Ricky T. Q. Chen1 Heli Ben-Hamu2 Maximilian Nickel1 Matt Le1\n[...]\nWe introduce a new paradigm for generative modeling built on Continuous\n[...]\n(CNFs), allowing us to train CNFs at unprecedented scale.\n[...]\nSpecifically, we present the notion of Flow Matching (FM), a simulation-free\n[...]\napproach for training CNFs based on regressing vector fields of fixed conditional\n[...]\nprobability paths. Flow Matching is compatible with a general family of Gaussian\n[...]\nprobability paths for transforming between noise and data samples—which\n[...]\nsubsumes existing diffusion paths as specific instances. Interestingly, we find\n[...]\nthat employing FM with diffusion paths results in a more robust and stable\n[...]\nalternative for training diffusion models. Furthermore, Flow Matching opens\n[...]\nthe door to training CNFs with other, non-diffusion probability paths. An\n[...]\ninstance of particular interest is using Optimal Transport (OT) displacement\n[...]\ninterpolation to define the conditional probability paths. These paths are more\n[...]\nefficient than diffusion paths, provide faster training and sampling, and result in\n[...]\nbetter generalization. Training CNFs using Flow Matching on ImageNet leads\n[...]\nto consistently better performance than alternative diffusion-based methods in\n[...]\nterms of both likelihood and sample quality, and allows fast and reliable sample\n[...]\ngeneration using off-the-shelf numerical ODE solvers.\n",
+      "source_url": "https://arxiv.org/pdf/2210.02747",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2210.02747",
+      "_exa_published_date": "2023-02-08T00:00:00.000Z"
+    },
+    {
+      "title": "",
+      "snippet": "FLOW MATCHING FOR GENERATIVE MODELING\n[...]\nYaron Lipman1,2 Ricky T. Q. Chen1 Heli Ben-Hamu2 Maximilian Nickel1 Matt Le1\n[...]\nWe introduce a new paradigm for generative modeling built on Continuous\n[...]\nNormalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale.\n[...]\nSpecifically, we present the notion of Flow Matching (FM), a simulation-free\n[...]\napproach for training CNFs based on regressing vector fields of fixed conditional\n[...]\nprobability paths. Flow Matching is compatible with a general family of Gaussian\n[...]\nprobability paths for transforming between noise and data samples—which\n[...]\nsubsumes existing diffusion paths as specific instances. Interestingly, we find\n[...]\nthat employing FM with diffusion paths results in a more robust and stable\n[...]\nalternative for training diffusion models. Furthermore, Flow Matching opens\n[...]\nthe door to training CNFs with other, non-diffusion probability paths. An\n[...]\ninstance of particular interest is using Optimal Transport (OT) displacement\n[...]\ninterpolation to define the conditional probability paths. These paths are more\n[...]\nefficient than diffusion paths, provide faster training and sampling, and result in\n[...]\nbetter generalization. Training CNFs using Flow Matching on ImageNet leads\n[...]\nto consistently better performance than alternative diffusion-based methods in\n[...]\nterms of both likelihood and sample quality, and allows fast and reliable sample\n[...]\ngeneration using off-the-shelf numer",
+      "source_url": "https://openreview.net/pdf?id=PqvMRDCJT9t",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://openreview.net/pdf?id=PqvMRDCJT9t",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[2210.02747] Flow Matching for Generative Modeling - arXiv",
+      "snippet": "[221\n[...]\n] Flow Matching for Generative Modeling\n[...]\n# Flow Matching for Generative Modeling\n[...]\nYaron Lipman1,2 Ricky T. Q. Chen1 Heli Ben-Hamu2 Maximilian Nickel1 Matt Le1 1Meta AI (FAIR) 2Weizmann Institute of Science\n[...]\nWe introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian probability paths for transforming between noise and data samples—which subsumes existing diffusion paths as specific instances. Interestingly, we find that employing FM with diffusion paths results in a more robust and stable alternative for training diffusion models. Furthermore, Flow Matching opens the door to training CNFs with other, non-diffusion probability paths. An instance of particular interest is using Optimal Transport (OT) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. Training CNFs using Flow Matching on ImageNet leads to consistently better performance than alternative diffusion-based methods in terms of both likelihood and sample quality, and allows fast and reliable sample generation using off-the-s",
+      "source_url": "https://arxiv.org/abs/2210.02747",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2210.02747",
+      "_exa_published_date": "2022-10-06T00:00:00.000Z"
+    },
+    {
+      "title": "[PDF] Flow Matching for Generative Modeling | Semantic Scholar",
+      "snippet": "```\n@article{Lipman2022FlowMF,\n  title={Flow Matching for Generative Modeling},\n  author={Yaron Lipman and Ricky T. Q. Chen and Heli Ben-Hamu and Maximilian Nickel and Matt Le},\n  journal={ArXiv},\n  year={2022},\n  volume={abs/2210.02747},\n  url={https://api.semanticscholar.org/CorpusID:252734897}\n}\n```",
+      "source_url": "https://www.semanticscholar.org/reader/af68f10ab5078bfc519caae377c90ee6d9c504e9",
+      "discovered_for": [
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+      ],
+      "_exa_id": "https://www.semanticscholar.org/reader/af68f10ab5078bfc519caae377c90ee6d9c504e9",
+      "_exa_published_date": "2022-10-06T03:39:18.000Z"
+    },
+    {
+      "title": "Flow Matching for Generative Modeling - OpenReview",
+      "snippet": "Flow Matching for Generative Modeling | OpenReview\n\n## Flow Matching for Generative Modeling\n\nICLR 2023 notable top 25%Readers: Everyone\n\nKeywords: continuous normalizing flows, generative models\n\nAbstract: We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian probability paths for transforming between noise and data samples---which subsumes existing diffusion paths as specific instances. Interestingly, we find that employing FM with diffusion paths results in a more robust and stable alternative for training diffusion models. Furthermore, Flow Matching opens the door to training CNFs with other, non-diffusion probability paths. An instance of particular interest is using Optimal Transport (OT) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. Training CNFs using Flow Matching on ImageNet leads to consistently better performance than alternative diffusion-based methods in terms of both likelihood and sample quality, and allows fast and reliable sample generation using off-the-shelf numerical ODE solve",
+      "source_url": "https://openreview.net/forum?id=PqvMRDCJT9t",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=PqvMRDCJT9t",
+      "_exa_published_date": "2022-09-29T08:12:00.000Z"
+    },
+    {
+      "title": "[2505.13447] Mean Flows for One-step Generative Modeling - arXiv",
+      "snippet": "# Mean Flows for One-step Generative Modeling\n[...]\nZhengyang Geng1 Mingyang Deng2 Xingjian Bai2 J. Zico Kolter1 Kaiming He2 1CMU 2MIT Work partly done when visiting MIT.\n[...]\nWe propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantaneous velocity modeled by Flow Matching methods. A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training. Our method, termed the MeanFlow model, is self-contained and requires no pre-training, distillation, or curriculum learning. MeanFlow demonstrates strong empirical performance: it achieves an FID of 3.43 with a single function evaluation (1-NFE) on ImageNet 256 $\\times$ 256 trained from scratch, significantly outperforming previous state-of-the-art one-step diffusion/flow models. Our study substantially narrows the gap between one-step diffusion/flow models and their multi-step predecessors, and we hope it will motivate future research to revisit the foundations of these powerful models.\n[...]\nIn this work, we propose a principled and effective framework, termed MeanFlow, for one-step generation. The core idea is to introduce a new ground-truth field representing the average velocity, in contrast to the instantaneous velocity typically modeled in Flow Matching. Average velocity is defined as the ratio of displacement to a time interval, with displacement give",
+      "source_url": "https://arxiv.org/abs/2505.13447",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2505.13447",
+      "_exa_published_date": "2025-05-19T00:00:00.000Z"
+    },
+    {
+      "title": "Mean Flows for One-step Generative Modeling",
+      "snippet": "# Mean Flows for One-step Generative Modeling\n[...]\nZhengyang Geng1 Mingyang Deng2 Xingjian Bai2 J. Zico Kolter1 Kaiming He2 1CMU 2MIT Work partly done when visiting MIT.\n[...]\nWe propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantaneous velocity modeled by Flow Matching methods. A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training. Our method, termed the MeanFlow model, is self-contained and requires no pre-training, distillation, or curriculum learning. MeanFlow demonstrates strong empirical performance: it achieves an FID of 3.43 with a single function evaluation (1-NFE) on ImageNet 256 $\\times$ 256 trained from scratch, significantly outperforming previous state-of-the-art one-step diffusion/flow models. Our study substantially narrows the gap between one-step diffusion/flow models and their multi-step predecessors, and we hope it will motivate future research to revisit the foundations of these powerful models.\n[...]\nIn this work, we propose a principled and effective framework, termed MeanFlow, for one-step generation. The core idea is to introduce a new ground-truth field representing the average velocity, in contrast to the instantaneous velocity typically modeled in Flow Matching. Average velocity is defined as the ratio of displacement to a time interval, with displacement give",
+      "source_url": "https://arxiv.org/html/2505.13447",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/html/2505.13447",
+      "_exa_published_date": null
+    },
+    {
+      "title": "",
+      "snippet": "Mean Flows for One-step Generative Modeling\n[...]\nZhengyang Geng1∗ Mingyang Deng2 Xingjian Bai2 J. Zico Kolter1 Kaiming He2\n[...]\nWe propose a principled and effective framework for one-step generative modeling.\n[...]\nWe introduce the notion of average velocity to characterize flow fields, in contrast to\n[...]\ninstantaneous velocity modeled by Flow Matching methods. A well-defined identity\n[...]\nbetween average and instantaneous velocities is derived and used to guide neural\n[...]\nnetwork training. Our method, termed the MeanFlow model, is self-contained and\n[...]\nrequires no pre-training, distillation, or curriculum learning. MeanFlow demon\u0002strates strong empirical performance: it achieves an FID of 3.43 with a single\n[...]\nfunction evaluation (1-NFE) on ImageNet 256×256 trained from scratch, signifi\u0002cantly outperforming previous state-of-the-art one-step diffusion/flow models. Our\n[...]\nstudy substantially narrows the gap between one-step diffusion/flow models and\n[...]\ntheir multi-step predecessors, and we hope it will motivate future research to revisit\n[...]\nIn this work, we propose a principled and effective framework, termed MeanFlow, for one-step\n[...]\ngeneration. The core idea is to introduce a new ground-truth field representing the average velocity,\n[...]\nin contrast to the instantaneous velocity typically modeled in Flow Matching. Average velocity is\n[...]\ndefined as the ratio of displacement to a time interval, with displacement given by the time integral of\n[...",
+      "source_url": "https://openreview.net/pdf/611127035c61b58c8523c81094b8109e0d8f84fc.pdf",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://openreview.net/pdf/611127035c61b58c8523c81094b8109e0d8f84fc.pdf",
+      "_exa_published_date": "2025-10-24T00:00:00.000Z"
+    },
+    {
+      "title": "Mean Flows for One-step Generative Modeling - OpenReview",
+      "snippet": "Mean Flows for One-step Generative Modeling | OpenReview\n\n## Mean Flows for One-step Generative Modeling\n\n### Zhengyang Geng, Mingyang Deng, Xingjian Bai, J Zico Kolter, Kaiming He\n\nNeurIPS 2025 oralEveryone Revisions BibTeX CC BY 4.0\n\nKeywords: Generative Models\n\nAbstract: We propose a principled and effective framework for one-step generative modeling. We introduce the notion of average velocity to characterize flow fields, in contrast to instantaneous velocity modeled by Flow Matching methods. A well-defined identity between average and instantaneous velocities is derived and used to guide neural network training. Our method, termed the \\textit{MeanFlow} model, is self-contained and requires no pre-training, distillation, or curriculum learning. MeanFlow demonstrates strong empirical performance: it achieves an FID of 3.43 with a single function evaluation (1-NFE) on ImageNet 256$\\times$256 trained from scratch, significantly outperforming previous state-of-the-art one-step diffusion/flow models. Our study substantially narrows the gap between one-step diffusion/flow models and their multi-step predecessors, and we hope it will motivate future research to revisit the foundations of these powerful models.\n\nPrimary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)\n\nSubmission Number: 754\n\nLoading",
+      "source_url": "https://openreview.net/forum?id=uWj4s7rMnR",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=uWj4s7rMnR",
+      "_exa_published_date": "2025-10-29T14:53:17.000Z"
+    },
+    {
+      "title": "[PDF] Mean Flows for One-step Generative Modeling | Semantic Scholar",
+      "snippet": "[PDF] Mean Flows for One-step Generative Modeling | Semantic Scholar\n[...]\n```\n@article{Geng2025MeanFF,\n  title={Mean Flows for One-step Generative Modeling},\n  author={Zhengyang Geng and Mingyang Deng and Xingjian Bai and J. Zico Kolter and Kaiming He},\n  journal={ArXiv},\n  year={2025},\n  volume={abs/2505.13447},\n  url={https://api.semanticscholar.org/CorpusID:278769814}\n}\n```",
+      "source_url": "https://www.semanticscholar.org/reader/19df654b0d0f634a451564346a09af8bd348dac0",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://www.semanticscholar.org/reader/19df654b0d0f634a451564346a09af8bd348dac0",
+      "_exa_published_date": "2025-05-19T06:36:24.000Z"
+    },
+    {
+      "title": "Improved Mean Flows: On the Challenges of Fastforward Generative ...",
+      "snippet": "# Improved Mean Flows: On the Challenges of Fastforward Generative Models\n[...]\nZhengyang Geng1,2,3, Yiyang Lu4,2,∗ Zongze Wu3 Eli Shechtman3 J. Zico Kolter1 Kaiming He2 1CMU 2MIT 3Adobe 4THU Equal contribution. Part of this work was done when Z. Geng was interning at Adobe and MIT, and when Y. Lu was interning at MIT.\n[...]\nMeanFlow (MF) has recently been established as a framework for one-step generative modeling. However, its “fastforward” nature introduces key challenges in both the training objective and the guidance mechanism. First, the original MF’s training target depends not only on the underlying ground-truth fields but also on the network itself. To address this issue, we recast the objective as a loss on the instantaneous velocity $v$ , re-parameterized by a network that predicts the average velocity $u$ . Our reformulation yields a more standard regression problem and improves the training stability. Second, the original MF fixes the classifier-free guidance scale during training, which sacrifices flexibility. We tackle this issue by formulating guidance as explicit conditioning variables, thereby retaining flexibility at test time. The diverse conditions are processed through in-context conditioning, which reduces model size and benefits performance. Overall, our improved MeanFlow (iMF) method, trained entirely from scratch, achieves 1.72 FID with a single function evaluation (1-NFE) on ImageNet 256 $\\times$ 256. iMF substantially outperforms prior methods of t",
+      "source_url": "https://arxiv.org/abs/2512.02012",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2512.02012",
+      "_exa_published_date": "2025-12-01T00:00:00.000Z"
+    },
+    {
+      "title": "",
+      "snippet": "probability path. At the same time, the design space around this training objective is rapidly expand\u0002ing with emerging techniques. For example, Mean Flow (Geng et al., 2025a) replaces instantaneous\n[...]\nvelocities with average velocities to enable strong one-step generation, improved Mean Flow vari\u0002ants (Geng et al., 2025b) reformulate the objective and guidance mechanism for better stability and\n[...]\nflexibility,\n[...]\n” (Li\n[...]\npatch Transformers.\n[...]\ncurriculum sampling, a\n[...]\n-phase curriculum that\n[...]\n(Logit-Normal)\n[...]\na coverage-focused distribution (Uniform). This method outperforms static base\u0002lines, achieving a 1\n[...]\n% relative improvement in FID over standard Uniform sampling\n[...]\nrevisits a central but often under-specified design choice in flow-based generative model\u0002ing: the timestep sampling distribution\n[...]\n). We identify a performance paradox in standard training:\n[...]\nMotivated by this analysis, we propose a two-phase curriculum for timestep sampling. The cur\u0002riculum uses a Logit-Normal distribution in a structure-learning phase to exploit fast early progress,\n[...]\nthen switches to Uniform sampling in a refinement phase to restore coverage of\n[...]\n-10, this simple schedule improves the best FID from 3.85 (Uniform) to 3.22 (a 16.4%\n[...]\nZhengyang Geng, Mingyang Deng, Xingjian Bai, J Zico Kolter, and Kaiming He. Mean flows for\n[...]\none-step generative modeling. NeurIPS, 2025a.\n[...]\nZhengyang Geng, Yiyang Lu, Zongze Wu, Eli Shechtman, J.",
+      "source_url": "https://arxiv.org/pdf/2603.12517",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2603.12517",
+      "_exa_published_date": null
+    },
+    {
+      "title": "",
+      "snippet": "Tianhong Li 1 Zhengyang Geng 2 Kaiming He 1\n[...]\nModern diffusion/flow-based models for image\n[...]\nmade encouraging progress on each aspect in\u0002dividually, paving the way toward one-step dif\u0002fusion/flow without latents. In this work, we\n[...]\n“pixel MeanFlow” (pMF). Our core guideline is\n[...]\nformulate the network output space and the\n[...]\nloss space separately. The network target is de\u0002signed to be on a presumed low-dimensional im\u0002age manifold (i.e., x-prediction), while the loss\n[...]\nis defined via MeanFlow in the velocity space.\n[...]\nWe introduce a simple transformation between\n[...]\nthe image manifold and the average velocity field.\n[...]\nIn experiments, pMF achieves strong results for\n[...]\none-step latent-free generation on ImageNet at\n[...]\n256×256 resolution (2.22 FID) and 512×512 res\u0002olution (2.48 FID), filling a key missing piece in\n[...]\nIn this work, we propose pixel MeanFlow (pMF) for one\u0002step latent-free image generation. pMF follows the im\u0002proved MeanFlow (iMF) (Geng et al., 2025b) that learns\n[...]\nthe average velocity field (namely, u) using a loss defined\n[...]\nin the space of instantaneous velocity (namely, v). On the\n[...]\nother hand, following JiT (Li & He, 2025), pMF directly\n[...]\nparameterizes a denoised-image-like quantity (namely, x\u0002prediction), which is expected to lie on a low-dimensional\n[...]\nmanifold. To accommodate both formulations, we intro\u0002duce a conversion that relates the fields v, u, and x. We\n[...]\nempirically show that this formula",
+      "source_url": "https://arxiv.org/pdf/2601.22158",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2601.22158",
+      "_exa_published_date": null
+    },
+    {
+      "title": "",
+      "snippet": "In this work, we propose Discrete MeanFlow (DMF) training curriculum, a budget-friendly frame\u0002work designed to bridge the gap between standard flow models and the MeanFlow identity for fast\n[...]\n. Our approach\n[...]\na staged curriculum\n[...]\nZhengyang Geng, Mingyang Deng, Xingjian Bai, J. Zico Kolter, and Kaiming He. Mean flows for\n[...]\n, 20\n[...]\n. URL https\n[...]\narxiv.org/\n[...]\n/2505.13447.\n[...]\nZhengyang Geng, Yiyang Lu, Zongze Wu, Eli Shechtman, J. Zico Kolter, and Kaiming He. Im\u0002proved mean flows: On the challenges of fastforward generative models, 2025b. URL https:\n[...]\n//arxiv.org/abs/2512.02012.",
+      "source_url": "https://arxiv.org/pdf/2604.08837",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2604.08837",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[2511.19065] Understanding, Accelerating, and Improving MeanFlow Training",
+      "snippet": "Accelerating, and Improving MeanFlow Training\n[...]\n# Title:Understanding, Accelerating, and Improving MeanFlow Training\n\nAuthors: Jin-Young Kim, Hyojun Go, Lea Bogensperger, Julius Erbach, Nikolai Kalischek, Federico Tombari, Konrad Schindler, Dominik Narnhofer\n\nView PDF HTML (experimental)\n[...]\n> Abstract:MeanFlow promises high-quality generative modeling in few steps, by jointly learning instantaneous and average velocity fields. Yet, the underlying training dynamics remain unclear. We analyze the interaction between the two velocities and find: (i) well-established instantaneous velocity is a prerequisite for learning average velocity; (ii) learning of instantaneous velocity benefits from average velocity when the temporal gap is small, but degrades as the gap increases; and (iii) task-affinity analysis indicates that smooth learning of large-gap average velocities, essential for one-step generation, depends on the prior formation of accurate instantaneous and small-gap average velocities. Guided by these observations, we design an effective training scheme that accelerates the formation of instantaneous velocity, then shifts emphasis from short- to long-interval average velocity. Our enhanced MeanFlow training yields faster convergence and significantly better few-step generation: With the same DiT-XL backbone, our method reaches an impressive FID of 2.87 on 1-NFE ImageNet 256x256, compared to 3.43 for the conventional MeanFlow baseline. Alternatively, our method matche",
+      "source_url": "https://arxiv.org/abs/2511.19065",
+      "discovered_for": [
+        "rw.mean_flow"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2511.19065",
+      "_exa_published_date": null
+    },
+    {
+      "title": "RT-1: Robotics Transformer for Real-World Control at Scale",
+      "snippet": "By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project’s website and videos can be found at robotics-transformer1.github.io\n[...]\nThe second challenge lies in the design of the model itself. Effective robotic multi-task learning requires a high capacity model, and Transformer (Vaswani et al., 2017) models excel in this regard, particularly when it is necessary to learn many tasks conditioned, as in our case, on language instruct",
+      "source_url": "https://arxiv.org/html/2212.06817",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2212.06817",
+      "_exa_published_date": null
+    },
+    {
+      "title": "RT-1: Robotics Transformer for Real-World Control at Scale - arXiv",
+      "snippet": "By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project’s website and videos can be found at robotics-transformer1.github.io\n[...]\nThe second challenge lies in the design of the model itself. Effective robotic multi-task learning requires a high capacity model, and Transformer (Vaswani et al., 2017) models excel in this regard, particularly when it is necessary to learn many tasks conditioned, as in our case, on language instruct",
+      "source_url": "https://arxiv.org/abs/2212.06817",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2212.06817",
+      "_exa_published_date": "2022-12-13T00:00:00.000Z"
+    },
+    {
+      "title": "RT-1: Robotics Transformer for Real-World Control at Scale - arXiv",
+      "snippet": "By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project’s website and videos can be found at robotics-transformer1.github.io\n[...]\nThe second challenge lies in the design of the model itself. Effective robotic multi-task learning requires a high capacity model, and Transformer (Vaswani et al., 2017) models excel in this regard, particularly when it is necessary to learn many tasks conditioned, as in our case, on language instruct",
+      "source_url": "https://arxiv.org/html/2212.06817v2",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2212.06817v2",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Bringing the RT-1-X Foundation Model to a SCARA robot",
+      "snippet": "Traditional robotic systems require specific training data for each task, environment, and robot form. While recent advancements in machine learning have enabled models to generalize across new tasks and environments, the challenge of adapting these models to entirely new settings remains largely unexplored. This study addresses this by investigating the generalization capabilities of the RT-1-X robotic foundation model to a type of robot unseen during its training: a SCARA robot from UMI-RTX.\n[...]\nRecent breakthroughs in machine learning and artificial intelligence suggest that training on large, diverse datasets can lead to highly\n[...]\nwhich often exceed\n[...]\nperformance of models developed for\n[...]\ndatasets tailored to\n[...]\nAs a result,\n[...]\nfield has been exploring more\n[...]\ncan adapt to\n[...]\n. Recent advancements like transformer\n[...]\n’s RT-1 [brohan_rt-1_2022], which demonstrate the potential for\n[...]\nGoogle’s RT-1 model is an impressive work, tested on a collection of real-world robotic experiences, where in different institutes a fleet of robots were performing 700 tasks [brohan_rt-1_2022]. The robots in the training set, such as the Franka, Kuka iiwa, UR5 and the EveryDay robot, can move their end-effector in a spherical working-space. None of the robots in the dataset is of the SCARA (Selective Compliance Assembly Robot Arm) type. With a SCARA robot the movement of z-axis is decoupled from the movement in the x-y plane, which gives a SCARA robot an kidney ",
+      "source_url": "https://arxiv.org/html/2409.03299v1",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2409.03299v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "",
+      "snippet": "generalization capabilities of\n[...]\nfoundation models have\n[...]\n’s RT-1\n[...]\nGoogle’s RT-1 model is an impressive work, tested on a collection of real\u0002\n[...]\nrobotic experiences, where in\n[...]\ninstitutes a fleet of robots were\n[...]\nstudy is to see if generalization capabilities of the Google’s RT-1 model can\n[...]\nextended to an unseen robot\n[...]\nof a complete different type.\n[...]\nThe RT-1 model [2] was presented as a joint effort between Robotics at Google,\n[...]\nEveryday Robots, and Google Research, at the end of 2022. The purpose of RT\u00021 is to investigate if it is possible to train a single, capable, multi-task model\n[...]\non data consisting of a wide variety of robotic tasks, and to find out if such\n[...]\na model brings the same benefits observed in other domains, namely zero-shot\n[...]\ngeneralization to new tasks, environments, and objects.\n[...]\nWith RT-1, the authors present a model architecture along with a signif\u0002icant training dataset that fulfills those requirements, as well as demonstrate\n[...]\nof this model [2\n[...]\nBecause initial experiments [17] showed that zero-shot generalization was not\n[...]\npossible to the unseen UMI robotic embodiment, the decision was made to fine\u0002tune the model.\n[...]\nevaluate the generalization potential of\n[...]\n-1-\n[...]\nThis study explored the generalization capabilities of the RT-1-X model, particu\u0002larly its ability to adapt to a robot type not seen before. A dataset of demonstra\u0002tions was collected using the UMI robot, and",
+      "source_url": "https://arxiv.org/pdf/2409.03299",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2409.03299",
+      "_exa_published_date": "2024-09-06T00:00:00.000Z"
+    },
+    {
+      "title": "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control | OpenReview",
+      "snippet": "RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control | OpenReview\n[...]\n## RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control\n[...]\nTL;DR: Vision-language models, trained on Internet-scale data, can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning.\n[...]\nAbstract: We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robotic trajectory data and Internet-scale vision-language tasks, such as visual question answering. In contrast to other approaches, we propose a simple, general recipe to achieve this goal: in order to fit both natural language responses and robotic actions into the same format, we express the actions as text tokens and incorporate them directly into the training set of the model in the same way as natural language tokens. We refer to such category of models as vision-language-action models (VLA) and instantiate an example of such a model, which we call RT-2. Our extensive evaluation (6k evaluation trials) shows th",
+      "source_url": "https://openreview.net/forum?id=XMQgwiJ7KSX",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=XMQgwiJ7KSX",
+      "_exa_published_date": "2023-08-30T15:38:01.000Z"
+    },
+    {
+      "title": "[PDF] RT-2: Vision-Language-Action Models Transfer Web Knowledge to ...",
+      "snippet": "Transfer Web Knowledge\n[...]\n# RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control\n[...]\nWe study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robotic trajectory data and Internet-scale vision-language tasks, such as visual question answering. In contrast to other approaches, we propose a simple, general recipe to achieve this goal: in order to fit both natural language responses and robotic actions into the same format, we express the actions as text tokens and incorporate them directly into the training set of the model in the same way as natural language tokens. We refer to such category of models as vision-language-action models (VLA) and instantiate an example of such a model, which we call RT-2. Our extensive evaluation (6k evaluation trials) shows that our approach leads to performant robotic policies and enables RT-2 to obtain a range of emergent capabilities from Internet-scale training. This includes significantly improved generalization to novel objects, the ability to interpret commands not present in the robot t",
+      "source_url": "https://arxiv.org/pdf/2307.15818",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2307.15818",
+      "_exa_published_date": "2023-07-28T00:00:00.000Z"
+    },
+    {
+      "title": "[2307.15818] RT-2: Vision-Language-Action Models Transfer Web ...",
+      "snippet": "Transfer Web Knowledge\n[...]\n# RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control\n[...]\nWe study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web. To this end, we propose to co-fine-tune state-of-the-art vision-language models on both robotic trajectory data and Internet-scale vision-language tasks, such as visual question answering. In contrast to other approaches, we propose a simple, general recipe to achieve this goal: in order to fit both natural language responses and robotic actions into the same format, we express the actions as text tokens and incorporate them directly into the training set of the model in the same way as natural language tokens. We refer to such category of models as vision-language-action models (VLA) and instantiate an example of such a model, which we call RT-2. Our extensive evaluation (6k evaluation trials) shows that our approach leads to performant robotic policies and enables RT-2 to obtain a range of emergent capabilities from Internet-scale training. This includes significantly improved generalization to novel objects, the ability to interpret commands not present in the robot t",
+      "source_url": "https://arxiv.org/abs/2307.15818",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2307.15818",
+      "_exa_published_date": "2023-07-28T00:00:00.000Z"
+    },
+    {
+      "title": "",
+      "snippet": "language and point clouds and varying sequence lengths (from ∼ 200 to few thousand). Left: A 5B vision-language-action model from\n[...]\nthe RT-2 class [1] (sequence length: L = 196). The manipulation policy is conditioned on the text instruction. Right: A Point Cloud\n[...]\n-parameter vision-language-action models or\n[...]\nAs), into\n[...]\nspeeding up: (a) the class of recently introduced RT-2 models\n[...]\n[1], the first VLA robotic policies pre-trained on internet\u0002scale data, as well as (b) Point Cloud Transformer (PCT)\n[...]\n[12]), multi-modal sensor fusion [16], finally the first vision\u0002language-action robotic manipulation powered by massive\n[...]\nvision language models [1].\n[...]\nparameter models such as RT-2 [1].\n[...]\nto convert pre-trained or already fine-tuned Transformer\u0002based robotic policies of quadratic space and time complex\u0002ity (including massive billion-parameter vision-language\u0002action models or VLAs), into their efficient linear-attention\n[...]\neffectiveness of SARA-RT by speeding up: (a) the class\n[...]\nof the aforementioned RT-2 models, the first VLA robotic\n[...]\npolicies pre-trained on internet-scale data, as well as (b)\n[...]\nB. RT-2 vision-language-action models\n[...]\n1) The setting: Next we consider the class of RT-2 archi\u0002tectures from [1]. Those apply PaLI-X [36]) vision-language\u0002model (VLM) backbones to encode policies taking vision\n[...]\ninput and conditioned on natural language instructions. We\n[...]\nfocus on the 5B PaLI-X variant, as more practical ",
+      "source_url": "https://arxiv.org/pdf/2312.01990",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2312.01990",
+      "_exa_published_date": null
+    },
+    {
+      "title": "",
+      "snippet": "robotics applications is particularly\n[...]\n. One notable\n[...]\nexample is RT-2, a system capable of generating low-level actions\n[...]\nrepresented in textual format from a given instruction alongside\n[...]\na sequence of historical actions and image observations. To\n[...]\nstimulate further research in this domain, we introduce an\n[...]\nopen-source implementation tailored for utilizing VLMs in\n[...]\ninstruction-based robot control. This implementation supports\n[...]\na variety of VLM architectures and facilitates straightforward\n[...]\nintegration of new models. We use our framework to train\n[...]\nmultiple VLMs and evaluate them on a physical robot. The\n[...]\nresults validate the practical efficacy of our framework, thus\n[...]\npaving the way for enhanced understanding and capabilities in\n[...]\ninstruction-based robot control systems. The code is available\n[...]\nA notable endeavor in this domain is RT-2 [12], which\n[...]\nemploys a VLM to interpret an instruction, past actions\n[...]\nand image observations, and generate subsequent actions in\n[...]\ntextual form. RT-2 has demonstrated the potential of creating\n[...]\ninstruction-based low-level robot control policies, showcasing\n[...]\nremarkable performance and notable generalization capabil\u0002ities. However, RT-2’s unavailability to the public and its\n[...]\nconsiderable scale, with 55 billion parameters, pose challenges\n[...]\nfor academic endeavors to engage with it effectively, hindering\n[...]\nfurther exploration and refinement of thi",
+      "source_url": "https://openreview.net/pdf?id=nfm2qcV1S4",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/pdf?id=nfm2qcV1S4",
+      "_exa_published_date": null
+    },
+    {
+      "title": "OpenVLA: An Open-Source Vision-Language-Action Model - arXiv",
+      "snippet": "OpenVLA: An Open-Source Vision-Language-Action Model\n[...]\n# OpenVLA: An Open-Source Vision-Language-Action Model\n[...]\nLarge policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rather than training new behaviors from scratch, we can fine-tune such vision-language-action (VLA) models to obtain robust, generalizable policies for visuomotor control. Yet, widespread adoption of VLAs for robotics has been challenging as 1) existing VLAs are largely closed and inaccessible to the public, and 2) prior work fails to explore methods for efficiently fine-tuning VLAs for new tasks, a key component for adoption. Addressing these challenges, we introduce OpenVLA, a 7B-parameter open-source VLA trained on a diverse collection of 970k real-world robot demonstrations. OpenVLA builds on a Llama 2 language model combined with a visual encoder that fuses pretrained features from DINOv2 and SigLIP. As a product of the added data diversity and new model components, OpenVLA demonstrates strong results for generalist manipulation, outperforming closed models such as RT-2-X (55B) by 16.5% in absolute task success rate across 29 tasks and multiple robot embodiments, with 7x fewer parameters. We further show that we can effectively fine-tune OpenVLA for new settings, with especially strong generalization results in multi-task environments involving multiple objects and strong language",
+      "source_url": "https://arxiv.org/abs/2406.09246",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2406.09246",
+      "_exa_published_date": "2024-06-13T00:00:00.000Z"
+    },
+    {
+      "title": "[PDF] OpenVLA: An Open-Source Vision-Language-Action Model | Semantic Scholar",
+      "snippet": "[PDF] OpenVLA: An Open-Source Vision-Language-Action Model | Semantic Scholar \n\nNavigate Paper Download (opens in a new tab) Share\n[...]\n```\n@article{Kim2024OpenVLAAO,\n  title={OpenVLA: An Open-Source Vision-Language-Action Model},\n  author={Moo Jin Kim and Karl Pertsch and Siddharth Karamcheti and Ted Xiao and Ashwin Balakrishna and Suraj Nair and Rafael Rafailov and Ethan Paul Foster and Grace Lam and Pannag R. Sanketi and Quan Vuong and Thomas Kollar and Benjamin Burchfiel and Russ Tedrake and Dorsa Sadigh and Sergey Levine and Percy Liang and Chelsea Finn},\n  journal={ArXiv},\n  year={2024},\n  volume={abs/2406.09246},\n  url={https://api.semanticscholar.org/CorpusID:270440391}\n}\n```",
+      "source_url": "https://www.semanticscholar.org/reader/8f9ceb5ffad8e7a066dfc9d9aaa5153b714740ee",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://www.semanticscholar.org/reader/8f9ceb5ffad8e7a066dfc9d9aaa5153b714740ee",
+      "_exa_published_date": "2024-06-13T09:02:12.000Z"
+    },
+    {
+      "title": "OpenVLA: An Open-Source Vision-Language-Action Model",
+      "snippet": "OpenVLA: An Open-Source Vision-Language-Action Model | OpenReview\n\n## OpenVLA: An Open-Source Vision-Language-Action Model\n\n### Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan P Foster, Pannag R Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, Chelsea Finn \n\nCoRL 2024everyonesince 05 Sept 2024\">Everyone Revisions BibTeX CC BY 4.0\n\nKeywords: Vision-Language-Action Models, Generalist Policies, Large-scale Robot Learning, Robotic Manipulation, Robotics, Vision-Language Models\n\nTL;DR: We introduce OpenVLA, a state-of-the-art, open-source 7B-parameter VLA model that obtains strong performance for cross-embodiment robot control out-of-the-box and can be easily adapted to new robot setups via parameter-efficient fine-tuning.\n\nAbstract: Large policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rather than training new behaviors from scratch, we can fine-tune such vision-language-action (VLA) models to obtain robust, generalizable policies for visuomotor control. Yet, widespread adoption of VLAs for robotics has been challenging as 1) existing VLAs are largely closed and inaccessible to the public, and 2) prior work fails to explore methods for efficiently fine-tuning VLAs for new tasks, a key component for adoption. Addressing these challeng",
+      "source_url": "https://openreview.net/forum?id=ZMnD6QZAE6",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=ZMnD6QZAE6",
+      "_exa_published_date": "2024-09-05T00:00:00.000Z"
+    },
+    {
+      "title": "[2406.09246v3] OpenVLA: An Open-Source Vision-Language-Action Model",
+      "snippet": "[2406.09246v3] OpenVLA: An Open-Source Vision-Language-Action Model\n[...]\n# Title:OpenVLA: An Open-Source Vision-Language-Action Model\n[...]\n> Abstract:Large policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rather than training new behaviors from scratch, we can fine-tune such vision-language-action (VLA) models to obtain robust, generalizable policies for visuomotor control. Yet, widespread adoption of VLAs for robotics has been challenging as 1) existing VLAs are largely closed and inaccessible to the public, and 2) prior work fails to explore methods for efficiently fine-tuning VLAs for new tasks, a key component for adoption. Addressing these challenges, we introduce OpenVLA, a 7B-parameter open-source VLA trained on a diverse collection of 970k real-world robot demonstrations. OpenVLA builds on a Llama 2 language model combined with a visual encoder that fuses pretrained features from DINOv2 and SigLIP. As a product of the added data diversity and new model components, OpenVLA demonstrates strong results for generalist manipulation, outperforming closed models such as RT-2-X (55B) by 16.5% in absolute task success rate across 29 tasks and multiple robot embodiments, with 7x fewer parameters. We further show that we can effectively fine-tune OpenVLA for new settings, with especially strong generalization results in multi-task environments involving mult",
+      "source_url": "https://arxiv.org/abs/2406.09246v3",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2406.09246v3",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Revision History for OpenVLA: An Open-Source... - OpenReview",
+      "snippet": "Revisions | OpenReview\n\nLoading",
+      "source_url": "https://openreview.net/revisions?id=ZMnD6QZAE6",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/revisions?id=ZMnD6QZAE6",
+      "_exa_published_date": null
+    },
+    {
+      "title": "A Vision-Language-Action Model for Affordable and Efficient Robotics",
+      "snippet": "SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\n# SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\nVision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into vision-language-action (VLA) models that enable natural language-driven perception and control. However, existing VLAs are typically massive–often with billions of parameters–leading to high training costs and limited real-world deployability. Moreover, they rely on academic and industrial datasets, overlooking the growing availability of community-collected data from affordable robotic platforms. In this work, we present SmolVLA, a small, efficient, and community-driven VLA that drastically reduces both training and inference costs, while retaining competitive performance. SmolVLA is designed to be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. To further improve responsiveness, we introduce an asynchronous inference stack decoupling perception and action prediction from action execution, allowing higher control rates with chunked action generation. Despite its compact size, SmolVLA achieves performance comparable to VLAs that are 10 $\\times$ larger. We evaluate SmolVLA on a range of both simulated as well as real-world robotic bench",
+      "source_url": "https://arxiv.org/abs/2506.01844",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2506.01844",
+      "_exa_published_date": "2025-06-02T00:00:00.000Z"
+    },
+    {
+      "title": "[PDF] SmolVLA: A Vision-Language-Action Model for Affordable ... - arXiv",
+      "snippet": "SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\n# SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\nVision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into vision-language-action (VLA) models that enable natural language-driven perception and control. However, existing VLAs are typically massive–often with billions of parameters–leading to high training costs and limited real-world deployability. Moreover, they rely on academic and industrial datasets, overlooking the growing availability of community-collected data from affordable robotic platforms. In this work, we present SmolVLA, a small, efficient, and community-driven VLA that drastically reduces both training and inference costs, while retaining competitive performance. SmolVLA is designed to be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. To further improve responsiveness, we introduce an asynchronous inference stack decoupling perception and action prediction from action execution, allowing higher control rates with chunked action generation. Despite its compact size, SmolVLA achieves performance comparable to VLAs that are 10 $\\times$ larger. We evaluate SmolVLA on a range of both simulated as well as real-world robotic bench",
+      "source_url": "https://arxiv.org/pdf/2506.01844",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2506.01844",
+      "_exa_published_date": "2025-06-02T00:00:00.000Z"
+    },
+    {
+      "title": "SmolVLA: A vision-language-action model for affordable and ... - arXiv",
+      "snippet": "SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\n# SmolVLA: A vision-language-action model for affordable and efficient robotics\n[...]\nVision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into vision-language-action (VLA) models that enable natural language-driven perception and control. However, existing VLAs are typically massive–often with billions of parameters–leading to high training costs and limited real-world deployability. Moreover, they rely on academic and industrial datasets, overlooking the growing availability of community-collected data from affordable robotic platforms. In this work, we present SmolVLA, a small, efficient, and community-driven VLA that drastically reduces both training and inference costs, while retaining competitive performance. SmolVLA is designed to be trained on a single GPU and deployed on consumer-grade GPUs or even CPUs. To further improve responsiveness, we introduce an asynchronous inference stack decoupling perception and action prediction from action execution, allowing higher control rates with chunked action generation. Despite its compact size, SmolVLA achieves performance comparable to VLAs that are 10 $\\times$ larger. We evaluate SmolVLA on a range of both simulated as well as real-world robotic bench",
+      "source_url": "https://arxiv.org/html/2506.01844v1",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2506.01844v1",
+      "_exa_published_date": "2025-06-02T00:00:00.000Z"
+    },
+    {
+      "title": "[PDF] SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics | Semantic Scholar",
+      "snippet": "[PDF] SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics | Semantic Scholar \n\nNavigate Paper Download (opens in a new tab) Share\n[...]\n```\n@article{Shukor2025SmolVLAAV,\n  title={SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},\n  author={Mustafa Shukor and Dana Aubakirova and Francesco Capuano and Pepijn Kooijmans and Steven Palma and Adil Zouitine and Michel Aractingi and Caroline Pascal and Martino Russi and Andr{\\'e}s Marafioti and Simon Alibert and Matthieu Cord and Thomas Wolf and R{\\'e}mi Cad{\\`e}ne},\n  journal={ArXiv},\n  year={2025},\n  volume={abs/2506.01844},\n  url={https://api.semanticscholar.org/CorpusID:279119427}\n}\n```",
+      "source_url": "https://www.semanticscholar.org/reader/6ab4d113676d00e74b55e918fee4c7affaa8652f",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://www.semanticscholar.org/reader/6ab4d113676d00e74b55e918fee4c7affaa8652f",
+      "_exa_published_date": "2025-06-02T13:54:14.000Z"
+    },
+    {
+      "title": "Lite VLA: Efficient Vision-Language-Action Control on CPU-Bound Edge Robots",
+      "snippet": "By leveraging NF4 quantization and the llama-cpp runtime, the proposed LiteVLA implementation pioneers the CPU-only deployment path, achieving functional asynchronous visuomotor control on the low-cost Raspberry Pi 4. This represents a novel deployment strategy not demonstrated by prior GPU-centric VLA frameworks such as SmolVLA by Shukor et al. [22], whose work focused primarily on static robotic arms. Beyond proving technical feasibility, this work establishes a scalable methodology for deploying generalist robot intelligence under strict computational budgets.\n[...]\nParameter-efficient adaptation. We fine-tune a compact SmolVLM backbone using LoRA (rank 8, $\\alpha{=}8$ , dropout 0.1) to specialize visuomotor translation under tight memory/compute budgets (Alg. 1; Sec. III, pp. 2–3).\n[...]\n. 4).\n[...]\nLarge-scale multimodal systems such as PaLM-E, SayCan, and RT-2 have shown that unified language-conditioned reasoning enables robots to follow natural language commands and execute complex manipulation tasks. However, these approaches rely heavily on cloud-based computation and high-end GPUs, making them impractical for resource-limited or field-deployed robots. SMolVLA by Shukor et al. [22] introduced a small and efficient vision-language-action framework designed for community-driven robotic experimentation. It demonstrated that compact multimodal transformers could achieve competitive visuomotor reasoning performance while running on consumer-grade GPUs or CPUs. Nonetheles",
+      "source_url": "https://arxiv.org/html/2511.05642",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2511.05642",
+      "_exa_published_date": null
+    },
+    {
+      "title": "A Vision-Language-Action Flow Model for General Robot Control",
+      "snippet": "𝜋₀: A Vision-Language-Action Flow Model for General Robot Control\n[...]\n# $\\pi_{0}$ : A Vision-Language-Action Flow Model for General Robot Control\n[...]\nholds tremendous promise to unlock\n[...]\nfull potential of flexible, general, and dexterous\n[...]\nsystems, as well as to address some of\n[...]\ndeepest questions in artificial intelligence\n[...]\nHowever, bringing robot learning to\n[...]\nlevel of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how\n[...]\ncan design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of\n[...]\nability to perform tasks via direct prompting, follow language instructions from people and from a high-level VLM policy, and\n[...]\nability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes.\n[...]\nIn this paper, we present a prototype model and learning framework, which we call $\\pi_",
+      "source_url": "https://arxiv.org/abs/2410.24164",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2410.24164",
+      "_exa_published_date": "2024-10-31T00:00:00.000Z"
+    },
+    {
+      "title": "π: A Vision-Language-Action Flow Model for General Robot Control | OpenReview",
+      "snippet": "π: A Vision-Language-Action Flow Model for General Robot Control | OpenReview\n[...]\n## π: A Vision-Language-Action Flow Model for General Robot Control\n[...]\nAbstract: Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how we can design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people and from a high-level VLM policy, and its ability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes.",
+      "source_url": "https://openreview.net/forum?id=38a45ho9Nq",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=38a45ho9Nq",
+      "_exa_published_date": null
+    },
+    {
+      "title": "π0: A Vision-Language-Action Flow Model for General Robot Control",
+      "snippet": "# π0: A Vision-Language-Action Flow Model for General Robot Control\n[...]\n```\n@article{Black20240AV,\n  title={$\\pi$0: A Vision-Language-Action Flow Model for General Robot Control},\n  author={Kevin Black and Noah Brown and Danny Driess and Adnan Esmail and Michael Equi and Chelsea Finn and Niccolo Fusai and Lachy Groom and Karol Hausman and Brian Ichter and Szymon Jakubczak and Tim Jones and Liyiming Ke and Sergey Levine and Adrian Li-Bell and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Lucy Xiaoyang Shi and James Tanner and Quan Vuong and Anna Walling and Haohuan Wang and Ury Zhilinsky},\n  journal={ArXiv},\n  year={2024},\n  volume={abs/2410.24164},\n  url={https://api.semanticscholar.org/CorpusID:273811174}\n}\n\n```\n[...]\n- Kevin Black, Noah Brown, +21 authors Ury Zhilinsky\n[...]\n- Published in arXiv.org 31 October\n[...]\n2024\n[...]\n- Computer Science, Engineering\n[...]\nTLDR\n\nA novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge is proposed and evaluated in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people and from a high-level VLM policy, and its ability to acquire new skills via fine-tuning.Expand",
+      "source_url": "https://www.semanticscholar.org/paper/%CF%800%3A-A-Vision-Language-Action-Flow-Model-for-General-Black-Brown/7e7e59d2e247d99954081080ddd5aae93d10b9e0",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://www.semanticscholar.org/paper/%CF%800%3A-A-Vision-Language-Action-Flow-Model-for-General-Black-Brown/7e7e59d2e247d99954081080ddd5aae93d10b9e0",
+      "_exa_published_date": null
+    },
+    {
+      "title": "𝜋₀: A Vision-Language-Action Flow Model for General Robot Control",
+      "snippet": "𝜋₀: A Vision-Language-Action Flow Model for General Robot Control\n[...]\n# $\\pi_{0}$ : A Vision-Language-Action Flow Model for General Robot Control\n[...]\nRobot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence\n[...]\nHowever, bringing robot learning to\n[...]\nlevel of generality required for effective real-world systems faces major obstacles in terms of data, generalization, and robustness. In this paper, we discuss how generalist robot policies (i.e., robot foundation models) can address these challenges, and how we can design effective generalist robot policies for complex and highly dexterous tasks. We propose a novel flow matching architecture built on top of a pre-trained vision-language model (VLM) to inherit Internet-scale semantic knowledge. We then discuss how this model can be trained on a large and diverse dataset from multiple dexterous robot platforms, including single-arm robots, dual-arm robots, and mobile manipulators. We evaluate our model in terms of its ability to perform tasks in zero shot after pre-training, follow language instructions from people and from a high-level VLM policy, and\n[...]\nability to acquire new skills via fine-tuning. Our results cover a wide variety of tasks, such as laundry folding, table cleaning, and assembling boxes.\n[...]\nIn this paper, we present a prototype model and learning framework, wh",
+      "source_url": "https://arxiv.org/html/2410.24164v1",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2410.24164v1",
+      "_exa_published_date": "2024-10-31T00:00:00.000Z"
+    },
+    {
+      "title": "",
+      "snippet": "We train three policy classes: MolmoBot, a Molmo2-based multi-frame vision-language model with\n[...]\na flow-matching action head; MolmoBot-Pi0, which replicates the π0 architecture to enable direct\n[...]\ncomparison; and MolmoBot-SPOC, a lightweight policy suitable for edge deployment and amenable to\n[...]\nNVIDIA’s GR00T [1], Physical Intelligence’s π0 [2, 3], and Google DeepMind’s Gemini Robotics [4] frames\n[...]\nlarge-scale real-world training as the basis for generalist manipulation agents that act in the physical world.\n[...]\nMolmoBot-Pi0\n[...]\nUsing this data, we train three policy classes. Our flagship model, MolmoBot, is built on top of Molmo2 [7], our\n[...]\nvideo-language model capable of ingesting past frames for context. We augment this architecture with a DiT\u0002based flow-matching action head that is layerwise coupled to the vision-language backbone. Each action layer\n[...]\ncross-attends to the corresponding intermediate hidden states of the underlying VLM, while also incorporating\n[...]\nrobot-state features, allowing actions to be generated from multi-scale multimodal representations.\n[...]\nfrom MolmoBot, we also train MolmoBot-Pi0, which exactly replicates the π0 architecture for controlled\n[...]\ncomparison; and MolmoBot-SPOC, a lightweight non-VLA policy suitable for edge deployment and future RL\n[...]\nfine-tuning.\n[...]\nWe provide ablations demonstrating\n[...]\nimportance of data scale and diversity, and show through MolmoBot\u0002Pi0 that our\n[...]\nyields strong perfor",
+      "source_url": "https://arxiv.org/pdf/2603.16861",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2603.16861",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Efficient Action Tokenization for Vision-Language-Action Models",
+      "snippet": "Autoregressive sequence models, such as Transformer-based vision-language action (VLA) policies, can be tremendously effective for capturing complex and generalizable robotic behaviors. However, such models require us to choose a tokenization of our continuous action signals, which determines how the discrete symbols predicted by the model map to continuous robot actions. We find that current approaches for robot action tokenization, based on simple per-dimension, per-timestep binning schemes, typically perform poorly when learning dexterous skills from high-frequency robot data. To address this challenge, we propose a new compression-based tokenization scheme for robot actions, based on the discrete cosine transform. Our tokenization approach, Frequency-space Action Sequence Tokenization (FAST), enables us to train autoregressive VLAs for highly dexterous and high-frequency tasks where standard discretization methods fail completely. Based on FAST, we release FAST+, a universal robot action tokenizer, trained on 1M real robot action trajectories. It can be used as a black-box tokenizer for a wide range of robot action sequences, with diverse action spaces and control frequencies. Finally, we show that, when combined with the $\\bm{\\pi_{0}}$ VLA, our method can scale to training on 10k hours of robot data and match the performance of diffusion VLAs, while reducing training time by up to 5x.\n[...]\nFigure 4: Overview of the FAST action tokenization pipeline. Given a normalized c",
+      "source_url": "https://arxiv.org/abs/2501.09747",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2501.09747",
+      "_exa_published_date": "2025-01-16T00:00:00.000Z"
+    },
+    {
+      "title": "FAST: Efficient Action Tokenization for Vision-Language ... - arXiv",
+      "snippet": "Autoregressive sequence models, such as Transformer-based vision-language action (VLA) policies, can be tremendously effective for capturing complex and generalizable robotic behaviors. However, such models require us to choose a tokenization of our continuous action signals, which determines how the discrete symbols predicted by the model map to continuous robot actions. We find that current approaches for robot action tokenization, based on simple per-dimension, per-timestep binning schemes, typically perform poorly when learning dexterous skills from high-frequency robot data. To address this challenge, we propose a new compression-based tokenization scheme for robot actions, based on the discrete cosine transform. Our tokenization approach, Frequency-space Action Sequence Tokenization (FAST), enables us to train autoregressive VLAs for highly dexterous and high-frequency tasks where standard discretization methods fail completely. Based on FAST, we release FAST+, a universal robot action tokenizer, trained on 1M real robot action trajectories. It can be used as a black-box tokenizer for a wide range of robot action sequences, with diverse action spaces and control frequencies. Finally, we show that, when combined with the $\\bm{\\pi_{0}}$ VLA, our method can scale to training on 10k hours of robot data and match the performance of diffusion VLAs, while reducing training time by up to 5x.\n[...]\nFigure 4: Overview of the FAST action tokenization pipeline. Given a normalized c",
+      "source_url": "https://arxiv.org/html/2501.09747v1",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2501.09747v1",
+      "_exa_published_date": "2025-01-16T00:00:00.000Z"
+    },
+    {
+      "title": "ActionCodec: What Makes for Good Action Tokenizers",
+      "snippet": "without any robotics\n[...]\nintroduce ActionCodec, a robust action tokenizer that integrates the\n[...]\n. Moreover, ActionCodec leverages Residual Vector Quantization (RVQ) (Lee et al., 2022) post-training to refine reconstruction fidelity and incorporates embodiment-specific soft prompts to facilitate knowledge transfer across diverse robotic platforms\n[...]\nthat VLA\n[...]\nActionCodec, without any additional architectural modifications\n[...]\nefficiency, success rates\n[...]\n. ActionCodec achieves SOTA performance in both\n[...]\nenvironments, providing a systematic\n[...]\nfor the future of VQ-\n[...]\nOur contributions are\n[...]\nas follows:\n[...]\nTokenization Schemes\n[...]\n20\n[...]\nsuffers from low training efficiency and ignores the\n[...]\nparallel decoding (\n[...]\n(Goy\n[...]\n., 2025),\n[...]\nfundamental inefficiencies of heuristic binning. Other\n[...]\nrepresent actions as strings for direct VLM prediction (Hancock et\n[...]\nhowever, this approach\n[...]\nno significant performance benefits while greatly increasing the token budget and extending latency to several seconds, limiting\n[...]\nPertsch et al., 2025) introduces Byte-Pair Encoding (BPE) on frequency-domain signals; however, its reliance on fixed geometric priors limits its capacity for cross-embodiment knowledge transfer. Data-driven schemes, particularly those based on Vector Quantization (VQ) (Wang et al., 2025b; Belkhale and Sadigh, 2024; Mete et al., 2024; Lee et al., 2024), offer a more flexible alternative by learning disc",
+      "source_url": "https://arxiv.org/abs/2602.15397",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2602.15397",
+      "_exa_published_date": null
+    },
+    {
+      "title": "OAT: Ordered Action Tokenization",
+      "snippet": "action tokenization\n[...]\nTo bridge this gap, we introduce Ordered Action Tokenization (OAT), a learned action tokenizer that discretizes continuous action chunks into highly compressed and causally ordered token sequences. OAT employs transformer-based register tokens to aggregate temporal information, finite scalar quantization (FSQ) to construct a discrete bottleneck, and nested dropout to explicitly induce ordering that aligns the latent space with autoregressive generation. The resulting tokenization ensures that any token prefix corresponds to a plausible action chunk. Beyond improved modelability, the ordered structure learned by OAT enables a key capability absent from prior approaches: prefix-based decoding. Autoregressive policies may terminate generation early and still produce valid actions, yielding a natural trade-off between computation and action fidelity. As additional tokens are generated, decoded actions are progressively refined.\n[...]\nAn alternative line of work explores frequency-domain compression, for instance Frequency-space Action Sequence Tokenization (FAST) [49], which employs the Discrete Cosine Transform (DCT) to decompose action chunks into frequency coefficients, followed by Byte Pair Encoding (BPE) [18]. FAST achieves high information density (P.1), and crucially, its low-frequency components first then high-frequency components ordering (P.3) improves downstream autoregressive policies: early token predictions capture the overall trajectory s",
+      "source_url": "https://arxiv.org/html/2602.04215",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2602.04215",
+      "_exa_published_date": null
+    },
+    {
+      "title": "PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding",
+      "snippet": "the above challenges, we present a novel parallel decoding framework for the mainstream VLA model with action chunking, called Parallel\n[...]\nfor VLA (PD-VLA). Fig. 1 illustrates the\n[...]\nconcept of our parallel decoding approach. Our\n[...]\naction decoding as a system of\n[...]\nsolved through parallel fixed-point iteration methods, e.g., Jacobi fix-point iteration method [32]. This approach preserves\n[...]\nimproving decoding speed\n[...]\nthat we only accelerate the decoding process\n[...]\nVLA inference.\n[...]\n, our method enables friendly\n[...]\ntraining-free acceleration without redesign and modification of models (\n[...]\n, our method\n[...]\nsynergy with existing acceleration\n[...]\nVarious acceleration strategies, including quantization [21] and token pruning [5], have been effectively applied to LLMs, yet they often fail to meet the stringent real-time requirements of action generation. Efforts to enhance efficiency have led to architectural modifications in VLA models, such as DeeR-VLA [43], which dynamically adjusts inference depth, and QAIL [33], which integrates quantization-aware training. Further innovations, like RoboMamba [25] and TinyVLA [41], replace traditional attention mechanisms or focus on developing lightweight models from the ground up, frequently necessitating model re-training and additional data collection. Meanwhile, VLA-Cache [42] selectively caches static tokens and recomputes only dynamic or task-relevant ones. FAST [34] proposes a compression-based toke",
+      "source_url": "https://arxiv.org/html/2503.02310v2",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2503.02310v2",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware | OpenReview",
+      "snippet": "Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware | OpenReview\n[...]\n## Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware\n[...]\nAbstract: Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback. Performing these tasks typically requires high-end robots, accurate sensors, or careful calibration, which can be expensive and difficult to set up. Can learning enable low-cost and imprecise hardware to perform these fine manipulation tasks? We present a low-cost system that performs end-to-end imitation learning directly from real demonstrations, collected with a custom teleoperation interface. Imitation learning, however, presents its own challenges, particularly in high-precision domains: errors in the policy can compound over time, and human demonstrations can be non-stationary. To address these challenges, we develop a simple yet novel algorithm, Action Chunking with Transformers (ACT), which learns a generative model over action sequences. ACT allows the robot to learn 6 difficult tasks in the real world, such as opening a translucent condiment cup and slotting a battery with 80-90% success, with only 10 minutes worth of demonstrations.",
+      "source_url": "https://openreview.net/forum?id=e8Eu1lqLaf",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=e8Eu1lqLaf",
+      "_exa_published_date": "2023-07-09T06:52:40.000Z"
+    },
+    {
+      "title": "Learning Fine-Grained Bimanual Manipulation with Low-Cost ... - arXiv",
+      "snippet": "Fine manipulation tasks, such as threading cable ties or slotting a battery, are notoriously difficult for robots because they require precision, careful coordination of contact forces, and closed-loop visual feedback. Performing these tasks typically requires high-end robots, accurate sensors, or careful calibration, which can be expensive and difficult to set up. Can learning enable low-cost and imprecise hardware to perform these fine manipulation tasks? We present a low-cost system that performs end-to-end imitation learning directly from real demonstrations, collected with a custom teleoperation interface. Imitation learning, however, presents its own challenges, particularly in high-precision domains: errors in the policy can compound over time, and human demonstrations can be non-stationary. To address these challenges, we develop a simple yet novel algorithm, Action Chunking with Transformers (ACT), which learns a generative model over action sequences. ACT allows the robot to learn 6 difficult tasks in the real world, such as opening a translucent condiment cup and slotting a battery with 80-90% success, with only 10 minutes worth of demonstrations. Project website: tonyzhaozh.github.io/aloha\n[...]\nImitation learning algorithm. Tasks that require precision and visual feedback present a significant challenge for imitation learning, even with high-quality demonstrations. Small errors in the predicted action can incur large differences in the state, exacerbating the “co",
+      "source_url": "https://arxiv.org/abs/2304.13705",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2304.13705",
+      "_exa_published_date": "2023-04-23T00:00:00.000Z"
+    },
+    {
+      "title": "Learning Bimanual Manipulation via Action Chunking and Inter-Arm Coordination with Transformers",
+      "snippet": "coordinated biman\n[...]\n. To address the\n[...]\narms, particularly for synchronized actions. Therefore, we propose a novel imitation learning architecture that predicts cooperative actions. We differentiate the architecture for both arms and add an intermediate encoder layer, Inter-Arm Coordinated transformer Encoder (IACE),\n[...]\nInter-Arm Coordinated transformer Encoder (IACE), that can adjust the synchronization and timing of potential bimanual movements against the encoders corresponding to each arm. Our overall model\n[...]\na local Transformer encoder for each\n[...]\narm trajectory, the IACE to facilitate learning biman\n[...]\nactions, and a Transformer decoder to\n[...]\nthe action chunk. We compare two types of Transformer decoders: split decoders and single decoders.\n[...]\nWe build our proposed models on the ACT model to design different encoder and decoder structures. In particular, we propose a new design called the inter-arm coordinated transformer Encoder (IACE), which helps synchronize and time the movements of both arms.\n[...]\npropose basic architectures that consist of encoders\n[...]\narm, designed to leverage the biman\n[...]\nfeatures the IACE, allowing the individual robot arms to learn their trajectories while simultaneously considering the state\n[...]\nThe model should focus on the corresponding wrist camera and joint values to determine the appropriate trajectory for each robot arm. Each arm is supported by its local encoder. Global information is also integrated t",
+      "source_url": "https://arxiv.org/html/2503.13916",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2503.13916",
+      "_exa_published_date": null
+    },
+    {
+      "title": "ALPHA-𝛼 and Bi-ACT Are All You Need: Importance of Position and Force Information/Control for Imitation Learning of Unimanual and Bimanual Robotic Manipulation with Low-Cost System",
+      "snippet": "Autonomous manipulation in everyday tasks requires flexible action generation to handle complex, diverse real-world environments, such as objects with varying hardness and softness. Imitation Learning (IL) enables robots to learn complex tasks from expert demonstrations. However, a lot of existing methods rely on position/unilateral control, leaving challenges in tasks that require force information/control, like carefully grasping fragile or varying-hardness objects. As the need for diverse controls increases, there are demand for low-cost bimanual robots that consider various motor inputs. To address these challenges, we introduce Bilateral Control-Based Imitation Learning via Action Chunking with Transformers(Bi-ACT) and”A” ”L”ow-cost ”P”hysical ”Ha”rdware Considering Diverse Motor Control Modes for Research in Everyday Bimanual Robotic Manipulation (ALPHA- $\\alpha$ ). Bi-ACT leverages bilateral control to utilize both position and force information, enhancing the robot’s adaptability to object characteristics such as hardness, shape, and weight. The concept of ALPHA- $\\alpha$ is affordability, ease of use, repairability, ease of assembly, and diverse control modes (position, velocity, torque), allowing researchers/developers to freely build control systems using ALPHA- $\\alpha$ . In our experiments, we conducted a detailed analysis of Bi-ACT in unimanual manipulation tasks, confirming its superior performance and adaptability compared to Bi-ACT without force control. Base",
+      "source_url": "https://arxiv.org/html/2411.09942",
+      "discovered_for": [
+        "rw.vla"
+      ],
+      "_exa_id": "https://arxiv.org/html/2411.09942",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] Learning Fine-Grained Bimanual Manipulation with Low-Cost ...",
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+      "source_url": "https://openreview.net/pdf/4abc35d9793e56c5b73634eaf903e2495311fbcf.pdf",
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+    },
+    {
+      "title": "[1512.03385] Deep Residual Learning for Image Recognition - arXiv",
+      "snippet": "[1512.03385] Deep Residual Learning for Image Recognition\n[...]\n# Deep Residual Learning for Image Recognition\n[...]\nKaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com\n[...]\nDeeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8 $\\times$ deeper than VGG nets [41] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.\n[...]\nreferenced mapping. To\n[...]\nextreme, if\n[...]\nwould be easier\n[...]\nresidual to zero than\n[...]\nnonlinear layers.\n[...]\non ImageNet [36\n[...]\nOn the ImageNet classification dataset [36], we obtain excellent results by extremely deep residual nets. Our 152-layer residual net is the deepest network ever presented on ImageNet, while still having lower complexity than VGG nets [41]. Our ensem",
+      "source_url": "https://arxiv.org/abs/1512.03385",
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+      "_exa_id": "https://arxiv.org/abs/1512.03385",
+      "_exa_published_date": "2015-12-10T00:00:00.000Z"
+    },
+    {
+      "title": "[1512.03385] Deep Residual Learning for Image Recognition",
+      "snippet": "[1512.03385] Deep Residual Learning for Image Recognition\n[...]\n# Deep Residual Learning for Image Recognition\n[...]\nKaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research {kahe, v-xiangz, v-shren, jiansun}@microsoft.com\n[...]\nDeeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8 $\\times$ deeper than VGG nets [41] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers.\n[...]\nreferenced mapping. To\n[...]\nextreme, if\n[...]\nwould be easier\n[...]\nresidual to zero than\n[...]\nnonlinear layers.\n[...]\non ImageNet [36\n[...]\nOn the ImageNet classification dataset [36], we obtain excellent results by extremely deep residual nets. Our 152-layer residual net is the deepest network ever presented on ImageNet, while still having lower complexity than VGG nets [41]. Our ensem",
+      "source_url": "https://arxiv.org/abs/1512.03385v1",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://arxiv.org/abs/1512.03385v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] Deep Residual Learning for Image Recognition - People | MIT CSAIL",
+      "snippet": "Deep Residual Learning\nfor Image Recognition\nKaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun\nwork done at\nMicrosoft Research Asia\n[...]\nResNet @ ILSVRC & COCO 2015 Competitions\n[...]\n1st places in all five main tracks\n[...]\n• ImageNet Classification: “Ultra-deep” 152-layer nets \n• ImageNet Detection: 16% better than 2nd\n[...]\n• ImageNet Localization: 27% better than 2nd\n[...]\n• COCO Detection: 11% better than 2nd\n[...]\n• COCO Segmentation: 12% better than 2nd\n[...]\n*improvements are relative numbers\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nRevolution of Depth\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiangyu Zhang, Shaoqing Ren, & Jian Sun. “Deep Residual Learning for Image Recognition”. CVPR 2016.\n[...]\nKaiming He, Xiang",
+      "source_url": "https://pdfs.semanticscholar.org/1cea/9b1931b9e87641708fec43d03f2a58f4d2b0.pdf",
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+      "_exa_id": "https://pdfs.semanticscholar.org/1cea/9b1931b9e87641708fec43d03f2a58f4d2b0.pdf",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Deep Residual Learning for Image Recognition: A Survey - MDPI",
+      "snippet": "Deep Residual Learning for Image Recognition: A Survey\n[...]\n# Deep Residual Learning for Image Recognition: A Survey\n[...]\nMuhammad Shafiq\n[...]\n1,* and\n\nZhaoquan Gu\n[...]\n2,3,*\n[...]\nCyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China\n[...]\nDepartment of New Networks, Peng Cheng Laboratory, Shenzhen 518055, China\n[...]\nDepartment of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China\n[...]\nAppl. Sci. 2022, 12(18), 8972; https://doi.org/10.3390/app12188972\n[...]\nDeep Residual Networks have recently been shown to significantly improve the performance of neural networks trained on ImageNet, with results beating all previous methods on this dataset by large margins in the image classification task. However, the meaning of these impressive numbers and their implications for future research are not fully understood yet. In this survey, we will try to explain what Deep Residual Networks are, how they achieve their excellent results, and why their successful implementation in practice represents a significant advance over existing techniques. We also discuss some open questions related to residual learning as well as possible applications of Deep Residual Networks beyond ImageNet. Finally, we discuss some issues that still need to be resolved before deep residual learning can be applied on more complex problems.\n[...]\ndeep residual learning for image recognition\n[...]\ndeep residual learning;\n[...]\nDeep resid",
+      "source_url": "https://www.mdpi.com/2076-3417/12/18/8972",
+      "discovered_for": [
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+      ],
+      "_exa_id": "https://www.mdpi.com/2076-3417/12/18/8972",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[1603.05027] Identity Mappings in Deep Residual Networks - arXiv",
+      "snippet": "Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun\n[...]\nDeep residual networks [1] have emerged as a family of extremely deep architectures showing compelling accuracy and nice convergence behaviors. In this paper, we analyze the propagation formulations behind the residual building blocks, which suggest that the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation. A series of ablation experiments support the importance of these identity mappings. This motivates us to propose a new residual unit, which makes training easier and improves generalization. We report improved results using a 1001-layer ResNet on CIFAR-10 (4.62% error) and CIFAR-100, and a 200-layer ResNet on Image\n[...]\n. Code is available at: https://github.com/KaimingHe/resnet-1k-layers.\n[...]\n- [1] He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. (2016)",
+      "source_url": "https://arxiv.org/abs/1603.05027",
+      "discovered_for": [
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+      ],
+      "_exa_id": "https://arxiv.org/abs/1603.05027",
+      "_exa_published_date": null
+    },
+    {
+      "title": "",
+      "snippet": "(AttnRes) [30] recently showed that the fixed residual connections in Trans\u0002formers, which accumulate layer outputs with uniform unit weights, can be re\u0002placed by learned softmax attention over all preceding layer outputs. A single\n[...]\npseudo-query vector per layer selects which earlier representations to aggregate,\n[...]\nenabling content-aware, position-specific routing with minimal overhead. This\n[...]\nprinciple of learned\n[...]\nAttention Residuals (AttnRes) were proposed by Chen et al. [30] for Transformer\u0002based LLMs. The standard residual connection accumulates layer outputs with\n[...]\nfixed unit weights: hl = hl−1 + fl(hl−1). As network depth grows, this causes\n[...]\ntwo problems: feature dilution, where each layer’s relative contribution to the\n[...]\naccumulated sum diminishes, and unbounded magnitude growth, a well-known\n[...]\nissue in PreNorm Transformers. AttnRes replaces the fixed accumulation with\n[...]\nsoftmax attention over all preceding layer outputs, parameterized by a single\n[...]\nlearned pseudo-query wl ∈ R\n[...]\nd per layer. This enables selective, content-aware\n[...]\naccess to earlier representations while keeping output magnitudes bounded.\n[...]\nWe extend Attention Residuals from same-dimensional layer aggregation in\n[...]\nIn standard Transformers [31], the residual connection at layer l accumulates\n[...]\nwhere fl denotes the layer computation (e.g., self-attention or feed-forward net\u0002work). Attention Residuals [30] replace this fixed accumulation with a",
+      "source_url": "https://arxiv.org/pdf/2604.03297",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2604.03297",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[2603.15031] Attention Residuals - arXiv",
+      "snippet": "Untitled Document\n\n$0$ $5$ $10$ $15$ $20$\n\nUntitled Document\n$0$ $5$ $10$ $15$ $20$\nBETA",
+      "source_url": "https://arxiv.org/abs/2603.15031",
+      "discovered_for": [
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+      ],
+      "_exa_id": "https://arxiv.org/abs/2603.15031",
+      "_exa_published_date": "2026-03-16T00:00:00.000Z"
+    },
+    {
+      "title": "SiameseNorm: Breaking the Barrier to Reconciling Pre/Post-Norm",
+      "snippet": "In this paper, we propose SiameseNorm, an elegant two\n[...]\nstream residual architecture that unifies\n[...]\nmaintain two residual streams\n[...]\nshared parameters:\n[...]\nthe advantages of\n[...]\nnegligible computational overhead\n[...]\nboosts accuracy from\n[...]\n128\n[...]\n639.6\n[...]\nwhere the product denotes an ordered composition of Jacobians from layer N−1N-1 down to i+1i+1. Notably, the term 𝐈\\mathbf{I} corresponds to the skip connection, which preserves an explicit identity gradient path. This allows gradients to flow through the network without explicit attenuation, facilitating the training of large scale models. However, it implicitly allows the representation magnitudes to grow unbounded. As noted previously, Pre-Norm exhibits insufficient effective depth, an issue that likely stems from a structural mismatch: As shown in Figure˜2(a), the main path accumulates residual updates without re-normalization, causing hidden state magnitudes to grow with depth (peri-ln). Consequently, deeper blocks encounter a scaling imbalance: they must influence an increasingly high-magnitude main path while being restricted to normalized, fixed-scale inputs. This growing disparity effectively dilutes the relative contribution of deeper layers, thereby limiting the effective depth of the model.\n[...]\nBy maintaining a clean identity path, Pre-Norm ensures stable gradient propagation. However, this comes at the cost of unbounded magnitude growth. As illustrated in Figure 2(a), while the input ",
+      "source_url": "https://arxiv.org/html/2602.08064v1",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://arxiv.org/html/2602.08064v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] Attention Residuals - arXiv",
+      "snippet": "Untitled Document\n\n$0$ $5$ $10$ $15$ $20$\n\nUntitled Document\n$0$ $5$ $10$ $15$ $20$\nBETA",
+      "source_url": "https://arxiv.org/pdf/2603.15031",
+      "discovered_for": [
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+      ],
+      "_exa_id": "https://arxiv.org/pdf/2603.15031",
+      "_exa_published_date": "2026-03-16T00:00:00.000Z"
+    },
+    {
+      "title": "",
+      "snippet": "However, deep PreNorm models exhibit their own limita\u0002tions. The clean residual path can lead to representation\n[...]\nlayers fail to learn new features (Li\n[...]\n). This “Curse of Depth” is characterized by\n[...]\nexponential growth in activation variance and layers that de\u0002volve into identity functions, limiting the benefits of scaling\n[...]\nPre-LayerNorm (PreNorm): To address the training in\u0002stability of PostNorm, especially when training deep models,\n[...]\n. Here,\n[...]\neach sub-layer,\n[...]\n. This creates a clean, identity\n[...]\nover PreNorm\n[...]\nRepresentation Collapse A primary pathology in deep\n[...]\nPreNorm Transformers is the uncontrolled growth of fea\u0002ture variance, which leads to representation collapse. In a\n[...]\nstandard PreNorm block, the main residual path acts as an\n[...]\nidentity map, causing variance to accumulate linearly with\n[...]\ndepth. Formally, for a network of depth L, the variance of\n[...]\nthe hidden states scales as Var(X′\n[...]\nΘ(L) (Kedia et al.,\n[...]\n024).\n[...]\nThis variance explosion degrades the learning capability of\n[...]\ndeep layers. Consider the Jacobian of the l-th PreNorm\n[...]\nblock. Let Res(·) denote the transformation within the resid\u0002ual branch (e.g., Self-Attention or FFN). Since the transfor\u0002mation within the residual branch operates on inputs nor\u0002malized by the feature standard deviation σl =\n[...]\nthe Jacobian JPre can be expressed as:\n[...]\nAs l → ∞, σl → ∞, causing the residual term to vanish at a\n[...]\nrate of O(1/\n[...]\nl).",
+      "source_url": "https://arxiv.org/pdf/2601.22580v1",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/2601.22580v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[2409.19606] Hyper-Connections - arXiv",
+      "snippet": "We present hyper-connections, a simple yet effective method that can serve as an alternative to residual connections. This approach specifically addresses common drawbacks observed in residual connection variants, such as the seesaw effect between gradient vanishing and representation collapse. Theoretically, hyper-connections allow the network to adjust the strength of connections between features at different depths and dynamically rearrange layers. We conduct experiments focusing on the pre-training of large language models, including dense and sparse models, where hyper-connections show significant performance improvements over residual connections. Additional experiments conducted on vision tasks also demonstrate similar improvements. We anticipate that this method will be broadly applicable and beneficial across a wide range of AI problems.\n[...]\nDeep learning has achieved tremendous success across various domains, where residual connections (He et al., 2016) have been instrumental in contemporary neural network architectures, including transformers and CNNs. Residual connections help mitigate the problem of gradient vanishing, enabling the effective training of very deep networks. However, it is important to acknowledge that residual connections are not infallible solutions and still present limitations that remain unresolved.\n[...]\nDriven by the limitations of residual connections, an important question arises: Can neural networks autonomously learn the optimal streng",
+      "source_url": "https://arxiv.org/abs/2409.19606",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://arxiv.org/abs/2409.19606",
+      "_exa_published_date": "2024-09-29T00:00:00.000Z"
+    },
+    {
+      "title": "Hyper-Connections - OpenReview",
+      "snippet": "Hyper-Connections | OpenReview\n\n## Hyper-Connections\n\n### Defa Zhu, Hongzhi Huang, Zihao Huang, Yutao Zeng, Yunyao Mao, Banggu Wu, Qiyang Min, Xun Zhou\n\nICLR 2025 Postereveryonesince 04 Oct 2024\">Everyone Revisions BibTeX CC BY 4.0\n\nKeywords: Network Architecture, Residual Connections, LLMs, Pre-training\n\nAbstract: We present hyper-connections, a simple yet effective method that can serve as an alternative to residual connections. This approach specifically addresses common drawbacks observed in residual connection variants, such as the seesaw effect between gradient vanishing and representation collapse. Theoretically, hyper-connections allow the network to adjust the strength of connections between features at different depths and dynamically rearrange layers. We conduct experiments focusing on the pre-training of large language models, including dense and sparse models, where hyper-connections show significant performance improvements over residual connections. Additional experiments conducted on vision tasks also demonstrate similar improvements. We anticipate that this method will be broadly applicable and beneficial across a wide range of AI problems.\n\nPrimary Area: foundation or frontier models, including LLMs\n\nCode Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.\n\nSubmission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Con",
+      "source_url": "https://openreview.net/forum?id=9FqARW7dwB",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=9FqARW7dwB",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Birkhoff-Exact Hyper-Connections: Exact Spectral Stability for Deep Residual Networks | OpenReview",
+      "snippet": "## Birkhoff-Exact Hyper-Connections: Exact Spectral Stability for Deep Residual Networks\n[...]\nKeywords: doubly stochastic matrices, spectral stability, deep residual networks, Birkhoff-von Neumann theorem, hyper-connections, token mixing, extreme depth training, quantization robustness\n[...]\nTL;DR: We propose BE-HC, which uses the Birkhoff-von Neumann theorem to construct exactly doubly stochastic mixing matrices as convex combinations of permutation matrices, enabling stable training at 1000+ layers where prior methods fail.\n[...]\nAbstract: Learnable information routing in deep networks faces the *depth-stability-efficiency trilemma*: architectures that scale to extreme depths often sacrifice efficiency; efficient approaches lack stability guarantees. Prior work uses iterative Sinkhorn-Knopp normalization to approximate doubly stochastic mixing matrices, but residual errors destabilize training beyond several hundred layers. We propose **Birkhoff-Exact Hyper-Connections (BE-HC)**, which leverages the Birkhoff-von Neumann theorem to construct *exactly* doubly stochastic matrices as convex combinations of permutation matrices. This guarantees spectral radius $\\rho = 1$ exactly—not approximately—enabling stable training at unprecedented depths. **Key results:** (1) *Extreme depth:* BE-HC trains stably at **1000 layers**, achieving 35.71% accuracy where ReZero and other baselines fail to converge. (2) *Long context:* BE-HC handles **8K tokens** on a single V100 GPU (22.56% vali",
+      "source_url": "https://openreview.net/forum?id=jpIjkN1B1Q",
+      "discovered_for": [
+        "rw.attnres"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=jpIjkN1B1Q",
+      "_exa_published_date": "2026-03-02T22:01:32.000Z"
+    },
+    {
+      "title": "",
+      "snippet": "We present the first study of Hyper-Connections (HC) for volumetric multi-modal\n[...]\nbrain tumor segmentation, integrating them as a drop-in replacement for fixed\n[...]\nresidual connections across five architectures: nnU-Net, SwinUNETR, VT-UNet,\n[...]\nNetpp.\n[...]\nIn this work, we explore Hyper-Connections (HC) [18], a recently proposed generalization of residual\n[...]\nconnections that enables dynamic, input-dependent feature aggregation. While HC has shown\n[...]\nit has not been investigated for medical image segmentation, particularly in volumetric and multi\u0002modal settings. We extend HC\n[...]\nboth 3D\n[...]\nand training dynamics. Residual connections [5]\n[...]\nstable optimization of deep networks through\n[...]\narchitectural components. In contrast, the Hyper-Connection framework [18] provides a unified\n[...]\nformulation that simultaneously adapts both depth-wise and width-wise connections, representing a\n[...]\nstrict generalization of prior approaches. To the best of our knowledge, this work presents the first\n[...]\nHyper-Connections (HC) were originally proposed in the context of large language model pre-training,\n[...]\nwhere the primary challenge is to maintain stable gradient flow across very deep transformer archi\u0002tectures operating on sequential token embeddings [18]. In that setting, HC improves optimization\n[...]\nby learning adaptive depth-wise aggregation, thereby avoiding limitations associated with fixed\n[...]\nresidual formulations such as Pre-Norm and Post-Norm va",
+      "source_url": "https://www.arxiv.org/pdf/2603.19844",
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+      "_exa_id": "https://www.arxiv.org/pdf/2603.19844",
+      "_exa_published_date": null
+    },
+    {
+      "title": "Ablate and Rescue: A Causal Analysis of Residual Stream Hyper-Connections",
+      "snippet": "Multi-stream transformer architectures have recently been proposed as a promising direction for managing representation collapse and the vanishing gradient problem for residual connections, yet their internal mechanisms remain unexplored. In particular, the recently introduced Manifold-Constrained Hyper-Connections (mHC) architecture posits multiple residual streams with constrained interaction, but lacks in-depth mechanistic analysis. We present the first open-source mHC language model (https://huggingface.co/wgpeng/mhc-780m) and analyze the multiple-stream architecture with a suite of representation-level metrics and causal interventions to probe how parallel streams encode and utilize information. Specifically, we introduce a systematic stream ablation-and-rescue framework that enables direct causal comparison of residual streams during inference. Through targeted pairwise interventions and controlled recovery experiments, we distinguish functional redundancy from asymmetric utilization and reveal how information is distributed across streams beyond what is observable from representational similarity alone.\n[...]\nHyper-Connections extend the standard transformer residual architecture by allowing multiple residual streams per layer, dynamically mixed through learned routing matrices (He et al., 2015; Zhu et al., 2025). Manifold-Constrained Hyper-Connections (mHC) further refines this framework by imposing geometric constraints on inter-stream mixing (Xie et al., 2026).\n[...",
+      "source_url": "https://www.arxiv.org/pdf/2603.14833",
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+    {
+      "title": "",
+      "snippet": "DECOUPLED WEIGHT DECAY REGULARIZATION\n[...]\nIlya Loshchilov & Frank Hutter\n[...]\nL2 regularization and weight decay regularization are equivalent for standard\n[...]\nstochastic gradient descent (when rescaled by the learning rate), but as we demon\u0002strate this is not the case for adaptive gradient algorithms, such as Adam. While\n[...]\nexpose), we propose a simple modification to recover the original formulation of\n[...]\nweight decay regularization by decoupling the weight decay from the optimization\n[...]\nsteps taken w.r.t. the loss function. We provide empirical evidence that our pro\u0002posed modification (i) decouples the optimal choice of weight decay factor from\n[...]\nthe setting of the learning rate for both standard SGD and Adam and (ii) substan\u0002tially improves Adam’s generalization performance, allowing it to compete with\n[...]\nSGD with momentum on image classification datasets (on which it was previously\n[...]\ntypically outperformed by the latter). Our proposed decoupled weight decay has\n[...]\ncommunity has implemented\n[...]\nit in TensorFlow and PyTorch; the complete source code for our experiments is\n[...]\navailable at https://github.com/loshchil/AdamW-and-SGDW\n[...]\nThe main contribution of this paper is to improve regularization in Adam by decoupling the weight\n[...]\ndecay from the gradient-based update. In a comprehensive analysis, we show that Adam generalizes\n[...]\nsubstantially better with decoupled weight decay than with L2 regularization, achieving 15% relative\n[.",
+      "source_url": "https://arxiv.org/pdf/1711.05101v3",
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+    },
+    {
+      "title": "[1711.05101] Decoupled Weight Decay Regularization - arXiv",
+      "snippet": "[1711.05\n[...]\npled Weight Decay Regularization\n[...]\nIlya Loshchilov & Frank Hutter University of Freiburg Freiburg, Germany, {ilya,fh}@cs.uni-freiburg.de\n[...]\nL2 regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is not the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L2 regularization (often calling it “weight decay” in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by decoupling the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam’s generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at https://github.com/loshchil/AdamW-and-SGDW\n[...]\nThe main contribution of this paper is to improve regularization in Adam by decoupling ",
+      "source_url": "https://arxiv.org/abs/1711.05101",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://arxiv.org/abs/1711.05101",
+      "_exa_published_date": "2017-11-14T00:00:00.000Z"
+    },
+    {
+      "title": "[PDF] Decoupled Weight Decay Regularization - arXiv",
+      "snippet": "DECOUPLED WEIGHT DECAY REGULARIZATION\n[...]\nIlya Loshchilov & Frank Hutter\n[...]\nL2 regularization and weight decay regularization are equivalent for standard\n[...]\nstochastic gradient descent (when rescaled by the learning rate), but as we demon\u0002strate this is not the case for adaptive gradient algorithms, such as Adam. While\n[...]\nexpose), we propose a simple modification to recover the original formulation of\n[...]\nweight decay regularization by decoupling the weight decay from the optimization\n[...]\nsteps taken w.r.t. the loss function. We provide empirical evidence that our pro\u0002posed modification (i) decouples the optimal choice of weight decay factor from\n[...]\nthe setting of the learning rate for both standard SGD and Adam and (ii) substan\u0002tially improves Adam’s generalization performance, allowing it to compete with\n[...]\nSGD with momentum on image classification datasets (on which it was previously\n[...]\ntypically outperformed by the latter). Our proposed decoupled weight decay has\n[...]\ncommunity has implemented\n[...]\nit in TensorFlow and PyTorch; the complete source code for our experiments is\n[...]\navailable at https://github.com/loshchil/AdamW-and-SGDW\n[...]\nThe main contribution of this paper is to improve regularization in Adam by decoupling the weight\n[...]\ndecay from the gradient-based update. In a comprehensive analysis, we show that Adam generalizes\n[...]\nsubstantially better with decoupled weight decay than with L2 regularization, achieving 15% relative\n[.",
+      "source_url": "https://arxiv.org/pdf/1711.05101",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://arxiv.org/pdf/1711.05101",
+      "_exa_published_date": "2019-01-04T00:00:00.000Z"
+    },
+    {
+      "title": "Decoupled Weight Decay Regularization - OpenReview",
+      "snippet": "Decoupled Weight Decay Regularization | OpenReview\n\n## Decoupled Weight Decay Regularization\n\nICLR 2019 Conference Blind SubmissionReaders: Everyone\n\nAbstract: L$_2$ regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demonstrate this is \\emph{not} the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L$_2$ regularization (often calling it ``weight decay'' in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by \\emph{decoupling} the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our proposed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substantially improves Adam's generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at \\url{https://github.com/loshchil/AdamW-and-SGDW}\n\nKeywords: optimization, regularization, weight decay, Adam\n\nCode: ",
+      "source_url": "https://openreview.net/forum?id=Bkg6RiCqY7",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://openreview.net/forum?id=Bkg6RiCqY7",
+      "_exa_published_date": "2018-09-27T08:45:35.000Z"
+    },
+    {
+      "title": "",
+      "snippet": "Ilya Loshchilov & Frank Hutter \nUniversity of Freiburg \nFreiburg, Germany, \nilya.loshchilov@gmail.com, fh@cs.uni-freiburg.de \n\nABSTRACT\n[...]\nL2 regularization and weight decay regularization are equivalent for standard stochastic gradient descent (when rescaled by the learning rate), but as we demon strate this is not the case for adaptive gradient algorithms, such as Adam. While common implementations of these algorithms employ L2 regularization (often calling it “weight decay” in what may be misleading due to the inequivalence we expose), we propose a simple modification to recover the original formulation of weight decay regularization by decoupling the weight decay from the optimization steps taken w.r.t. the loss function. We provide empirical evidence that our pro posed modification (i) decouples the optimal choice of weight decay factor from the setting of the learning rate for both standard SGD and Adam and (ii) substan tially improves Adam’s generalization performance, allowing it to compete with SGD with momentum on image classification datasets (on which it was previously typically outperformed by the latter). Our proposed decoupled weight decay has already been adopted by many researchers, and the community has implemented it in TensorFlow and PyTorch; the complete source code for our experiments is available at https://github.com/loshchil/AdamW-and-SGDW\n[...]\n(AdamW),\n[...]\n(Loshchilov & Hutter,\n[...]\n. Figure 1\n[...]\nweight decay (\n[...]\nshow that weight decay ",
+      "source_url": "https://openreview.net/pdf?id=Bkg6RiCqY7",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://openreview.net/pdf?id=Bkg6RiCqY7",
+      "_exa_published_date": null
+    },
+    {
+      "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
+      "snippet": "PyTorch: An Imperative Style, High-Performance Deep Learning Library\n[...]\nDeep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks.",
+      "source_url": "https://papers.nips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://papers.nips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[PDF] An Imperative Style, High-Performance Deep Learning Library - NIPS",
+      "snippet": "PyTorch: An Imperative Style, High-Performance Deep Learning Library\n\n| | Adam | Paszke | | Sam | Gross | | | Francisco | Massa | | |\n[...]\nAbstract Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.\n[...]\nWith the increased interest in deep learning in recent years, there has been an explosion of machine learning tools. Many popular frameworks such as Caffe [1], CNTK [2], TensorFlow [3], and Theano [4], construct a static dataflow graph that represents the computation and which can then be applied repeatedly to batches of data. This approach provides visibility into the whole comput",
+      "source_url": "https://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf",
+      "_exa_published_date": null
+    },
+    {
+      "title": "[1912.01703v1] PyTorch: An Imperative Style, High-Performance Deep Learning Library",
+      "snippet": "[1912.01703v1] PyTorch: An Imperative Style, High-Performance Deep Learning Library\n[...]\n# Title:PyTorch: An Imperative Style, High-Performance Deep Learning Library\n[...]\n> Abstract:Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several common benchmarks.",
+      "source_url": "https://arxiv.org/abs/1912.01703v1",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "https://arxiv.org/abs/1912.01703v1",
+      "_exa_published_date": null
+    },
+    {
+      "title": "PyTorch: An Imperative Style, High-Performance Deep Learning ...",
+      "snippet": "[1912.01703] PyTorch: An Imperative Style, High-Performance Deep Learning Library\n[...]\n# PyTorch: An Imperative Style, High-Performance Deep Learning Library\n[...]\nAdam Paszke University of Warsaw adam.paszke@gmail.com Sam Gross Facebook AI Research sgross@fb.com Francisco Massa Facebook AI Research fmassa@fb.com Adam Lerer Facebook AI Research alerer@fb.com James Bradbury Google jekbradbury@gmail.com Gregory Chanan Facebook AI Research gchanan@fb.com Trevor Killeen Self Employed killeent@cs.washington.edu Zeming Lin Facebook AI Research zlin@fb.com Natalia Gimelshein NVIDIA ngimelshein@nvidia.com Luca Antiga Orobix luca.antiga@orobix.com Alban Desmaison Oxford University alban@robots.ox.ac.uk Andreas Köpf Xamla andreas.koepf@xamla.com Edward Yang Facebook AI Research ezyang@fb.com Zach DeVito Facebook AI Research zdevito@cs.stanford.edu Martin Raison Nabla martinraison@gmail.com Alykhan Tejani Twitter atejani@twitter.com Sasank Chilamkurthy Qure.ai sasankchilamkurthy@gmail.com Benoit Steiner Facebook AI Research benoitsteiner@fb.com Lu Fang Facebook lufang@fb.com Junjie Bai Facebook jbai@fb.com Soumith Chintala Facebook AI Research soumith@gmail.com\n[...]\nDeep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it provides an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popu",
+      "source_url": "https://arxiv.org/abs/1912.01703",
+      "discovered_for": [
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+      ],
+      "_exa_id": "https://arxiv.org/abs/1912.01703",
+      "_exa_published_date": "2019-12-03T00:00:00.000Z"
+    },
+    {
+      "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
+      "snippet": "PyTorch: An Imperative Style, High-Performance Deep Learning Library\n[...]\nDeep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible: it was designed from first principles to support an imperative and Pythonic programming style that supports code as a model, makes debugging easy and is consistent with other popular scientific computing libraries, while remaining efficient and supporting hardware accelerators such as GPUs. In this paper, we detail the principles that drove the implementation of PyTorch and how they are reflected in its architecture. We emphasize that every aspect of PyTorch is a regular Python program under the full control of its user. We also explain how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance. We demonstrate the efficiency of individual subsystems, as well as the overall speed of PyTorch on several commonly used benchmarks.",
+      "source_url": "http://papers.neurips.cc/paper/9015-pytorch-an-",
+      "discovered_for": [
+        "method.training"
+      ],
+      "_exa_id": "http://papers.neurips.cc/paper/9015-pytorch-an-",
+      "_exa_published_date": null
+    }
+  ]
+}
\ No newline at end of file
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new file mode 100644
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@@ -0,0 +1,269 @@
+
+\documentclass{article}
+
+\usepackage{corl_2026}
+\usepackage[T1]{fontenc}
+\usepackage{microtype}
+\usepackage{booktabs}
+\usepackage{multirow}
+\usepackage{graphicx}
+\usepackage{amsmath,amssymb}
+\usepackage{enumitem}
+\usepackage{tcolorbox}
+\usepackage[capitalize]{cleveref}
+\usepackage{url}
+
+\title{iMF-AttnRes: Fast Mean-Flow Action Generation with Attention Residuals for Simulated Robot Manipulation}
+
+\author{Anonymous Authors}
+
+\begin{document}
+\maketitle
+
+\begin{abstract}
+Diffusion-style vision-language-action policies provide expressive action distributions, but iterative inference can be too slow for closed-loop manipulation. We study whether improved Mean Flow (iMF), originally motivated by fast-forward generative modeling, can be adapted to action generation so that a policy uses one to a few flow evaluations rather than long denoising chains. We combine iMF with Attention Residuals (AttnRes), which replace fixed residual accumulation in a deep policy transformer with learned depth-wise aggregation over previous layer outputs. In RoboIMI socket peg insertion, the best iMF-AttnRes policy reaches an average reward of 1513.56 and median reward of 1901.5 with 15.122 ms average inference time, compared with diffusion-style infer100 baselines at 861.53/338.291 ms and 1019.39/397.912 ms. In RoboIMI object transfer, the best iMF-AttnRes policy reaches average reward 526.22 and 44/100 success-like episodes, compared with a native diffusion-policy baseline at 319.2 and 29/100. These results suggest that average-flow action generation is a promising route to low-latency robot policies, while also revealing sensitivity to horizon choices, vision-token design, and simulation-to-real validation.
+\end{abstract}
+
+\keywords{Robot learning, imitation learning, flow matching, vision-language-action models}
+
+\section{Introduction}
+\label{sec:introduction}
+
+Learning-based robot manipulation policies increasingly use expressive generative action models. Diffusion Policy showed that action diffusion can be an effective visuomotor policy class for manipulation~\citep{black2024pi0,chi2023diffusion}, inheriting the representational flexibility of denoising diffusion models~\citep{ho2020denoising} and transformer-based diffusion backbones~\citep{peebles2023scalable}. However, this expressivity often comes with a deployment cost: the policy must be evaluated repeatedly during sampling. In the RoboIMI socket peg experiments studied here, two diffusion-style VLA baselines use infer100 sampling and require 338.291 ms and 397.912 ms per inference on their recorded rollouts. Such latencies are undesirable when the robot must repeatedly close the perception-action loop.
+
+This paper asks a direct question: can a robot action generator retain the quality benefits of generative policies while reducing inference to one or a few evaluations? We explore this question by adapting improved Mean Flow (iMF) to VLA-style imitation learning. Flow matching and rectified flow formulate generation through continuous vector fields~\citep{lipman2023flow,liu2022flow}. Mean Flow reframes generation around average velocity, enabling one-step generation~\citep{geng2025mean}; iMF further addresses instability caused by substituting conditional velocities into nonlinear mean-flow relations~\citep{geng2025improved}. For robot control, this distinction matters because fewer evaluations directly increase control responsiveness.
+
+We pair iMF with Attention Residuals (AttnRes). Residual connections are essential for deep visual and transformer networks~\citep{he2016deep,vaswani2017attention}, but standard residual accumulation adds all layer outputs with fixed unit weight. The motivation in our notes is to rewrite residual networks as a sum over layer increments, then replace the uniform sum with a learned softmax aggregation. AttnRes implements this view by letting each layer attend over preceding residual outputs~\citep{kimi2026attention}. We use this mechanism in the policy transformer to reduce depth-wise feature dilution while keeping the action generator fast.
+
+Our contributions are:
+\begin{enumerate}[leftmargin=*]
+    \item We formulate an iMF-AttnRes VLA policy for simulated robot imitation learning, combining one-to-few-step average-flow action generation with learned residual aggregation.
+    \item We provide 100-rollout evaluations on two RoboIMI manipulation environments, socket peg insertion and object transfer, comparing against diffusion-style VLA baselines, a native Diffusion Policy baseline, ACT-style action chunking, and SmolVLA-style compact VLA inference~\citep{zhao2023learning,shukor2025smolvla}.
+    \item We report both task quality and deployment speed. On socket insertion, the strongest iMF-AttnRes run improves average reward over the ph16 diffusion-style baseline by 1.76$\times$ and inference FPS by 22.97$\times$; on object transfer, the strongest iMF-AttnRes run improves average reward over the native diffusion-policy baseline by 1.65$\times$.
+\end{enumerate}
+We deliberately keep the claims scoped to simulation: hardware varies across runs, and real-robot transfer remains future work.
+
+\section{Related Work}
+\label{sec:related_work}
+
+\paragraph{Diffusion and flow policies for robot action generation.}
+Diffusion Policy introduced action diffusion as a visuomotor policy learning framework~\citep{black2024pi0,chi2023diffusion}, building on denoising diffusion objectives~\citep{ho2020denoising}. Diffusion transformers further show that transformer backbones can scale generative modeling~\citep{peebles2023scalable}. These models are expressive because they iteratively refine samples, but iterative refinement is also a latency source. Flow matching provides an alternative continuous generative formulation by learning vector fields that transport probability paths~\citep{lipman2023flow}. Rectified flow emphasizes straighter transport paths and faster sampling~\citep{liu2022flow}. Our method follows this fast-generation lineage but uses the improved mean-flow relation to target one-to-few-step action generation.
+
+\paragraph{Vision-language-action models and action chunking.}
+Transformer robot policies such as RT-1 and RT-2 show how large sequence models can map observations and task context to robot actions~\citep{brohan2022rt,brohan2023rt}. OpenVLA studies open VLA modeling at scale~\citep{kim2024openvla}, while compact or efficient VLA models such as SmolVLA and FAST reduce deployment cost through smaller backbones or efficient action tokenization~\citep{shukor2025smolvla,pertsch2025fast}. Action Chunking with Transformers (ACT) predicts chunks of future actions to improve temporal consistency and execution efficiency~\citep{zhao2023learning}. The iMF-AttnRes policy is complementary: it retains chunked execution but changes the generative action sampler so that each chunk can be produced with one to a few evaluations.
+
+\paragraph{Mean-flow training for one-step generation.}
+Let $x_t$ denote a sample at time $t$ and let the instantaneous flow satisfy
+\begin{equation}
+    \frac{d x_t}{dt} = v(x_t,t).
+\end{equation}
+For two times $r<t$, Mean Flow defines an average velocity
+\begin{equation}
+    \bar{v}(z_t,r,t) = \frac{1}{t-r}\int_r^t v(z_\tau,\tau)d\tau.
+\end{equation}
+Differentiating $(t-r)\bar{v}$ with respect to $t$ yields
+\begin{equation}
+    \bar{v}(z_t,r,t)=v(z_t,t) - (t-r)\frac{d}{dt}\bar{v}(z_t,r,t),
+\end{equation}
+where the total derivative can be written as a Jacobian-vector product (JVP), $\mathrm{jvp}(\bar{v},(z,r,t),(v,0,1))$. Mean Flow uses this identity to train an average-velocity predictor for one-step generation~\citep{geng2025mean}. The iMF motivation in our source notes is that a nonlinear JVP expression should use marginal rather than conditional velocity; directly substituting conditional velocity can make the objective unstable. iMF instead uses a corrected relation
+\begin{equation}
+    V_\theta(z_t) = u_\theta(z_t) + (t-r)\mathrm{JVP}_{\mathrm{sg}}(u_\theta; v_\theta),
+\end{equation}
+where gradients update the average-flow branch $u_\theta$ while the JVP direction is stop-gradient. This preserves the practical stability of flow-matching-style supervision while training the model used for fast inference~\citep{geng2025improved}.
+
+\paragraph{Residual aggregation and attention residuals.}
+Standard residual networks update $x_t=x_{t-1}+y_t$, so recursively $x_t=y_0+y_1+\cdots+y_t$. Equivalently, the next layer receives a uniform aggregation of all previous residual increments. A natural generalization is
+\begin{equation}
+    y_{t+1}=f_{t+1}\left(\sum_{s=0}^t a_{t+1,s}y_s\right),\qquad a_{t+1,s}\ge0,\quad \sum_{s=0}^t a_{t+1,s}=1.
+\end{equation}
+AttnRes instantiates the weights with an attention distribution over previous residual outputs,
+\begin{equation}
+    a_{t+1,s}\propto\exp\left(w_{t+1}^{\top}\mathrm{RMSNorm}(y_s)\right).
+\end{equation}
+This preserves the residual pathway while allowing each layer to select useful earlier representations instead of uniformly accumulating all of them~\citep{kimi2026attention}. We use this mechanism in the policy transformer; our ablations suggest that applying AttnRes too broadly inside the vision stack is not automatically beneficial.
+
+\section{Method}
+\label{sec:method}
+
+\subsection{Policy formulation}
+\label{sec:policy_formulation}
+
+We consider simulated imitation learning in RoboIMI. At each control step, the policy receives visual observations, robot state, and a task context, and predicts an action chunk of length \texttt{exec}. The main iMF-AttnRes socket and object-transfer policies use ph32/exec16 or related horizon-execution settings. The rollout evaluator records cumulative reward, median reward, mean maximum reward, nonzero-reward episode count, task-specific success-like threshold count, inference FPS, control FPS, and inference latency.
+
+The policy contains three conceptual parts. First, an observation encoder maps visual and state inputs into policy tokens. Second, a transformer policy core uses AttnRes in place of fixed residual accumulation. Third, the action generator uses iMF to predict an average flow over action noise-to-data paths, enabling one-to-few inference evaluations. In the strongest socket variants, the generator uses infer2 or infer3; in the strongest object-transfer run, it uses infer1.
+
+\subsection{Improved Mean Flow action generation}
+\label{sec:imf_action_generation}
+
+Classical flow matching trains an instantaneous velocity field. For action generation, a low number of integration steps can cause large path errors if the learned field is curved. Mean Flow addresses this by training an average velocity over a time interval. The model receives $(z_t,r,t)$ and predicts $u_\theta(z_t,r,t)$, an approximation to $\bar{v}(z_t,r,t)$. At inference, this average velocity is used to move directly across a larger interval, replacing a long sequence of small denoising or ODE steps.
+
+The iMF training objective used in our implementation follows the corrected relation in the source notes:
+\begin{equation}
+    V_\theta(z_t,r,t) = u_\theta(z_t,r,t) + (t-r)\mathrm{JVP}_{\mathrm{sg}}\big(u_\theta; v_\theta\big).
+\end{equation}
+Here $v_\theta$ is obtained from the same network at the instantaneous case, and the stop-gradient JVP direction prevents the nonlinear term from destabilizing the target. The supervised target remains the conditional straight-path velocity available from the imitation action sample and noise sample, but the learned branch used at deployment is the average-flow predictor $u_\theta$. This is the mechanism that allows infer1, infer2, and infer3 policies to compete with infer100 diffusion-style baselines.
+
+\subsection{Attention Residual policy transformer}
+\label{sec:attnres_policy}
+
+In a standard PreNorm transformer, residual updates are added with fixed unit weights. From the residual-increment view, this means layer $t+1$ consumes a uniform sum of $y_0,\ldots,y_t$. We replace this fixed accumulation in the policy transformer with AttnRes. Each layer has a learned query vector $w_{t+1}$; previous residual increments are normalized and scored; the layer input is the weighted sum. This adds a lightweight depth-selection mechanism:
+\begin{equation}
+    \tilde{x}_{t+1}=\sum_{s=0}^{t} \mathrm{softmax}_s\left(w_{t+1}^{\top}\mathrm{RMSNorm}(y_s)\right)y_s.
+\end{equation}
+The transformer block then computes $y_{t+1}=f_{t+1}(\tilde{x}_{t+1})$. The design is intended to reduce feature dilution in deep policies. In our object-transfer ablations, the best settings apply AttnRes in the policy transformer; replacing residuals throughout the vision encoder underperforms, suggesting that the placement of learned residual aggregation is important.
+
+\subsection{Inference and control loop}
+\label{sec:inference_loop}
+
+At deployment, the iMF-AttnRes policy samples an action chunk with one to a few average-flow evaluations and then executes the chunk for \texttt{exec} control steps. This contrasts with diffusion-style baselines whose run names include infer100. For socket insertion, the strongest iMF-AttnRes policies use ph32/exec16 and infer2 or infer3. For object transfer, the strongest iMF-AttnRes run uses ph32/exec16 and infer1. The resulting control loop is simple: encode observations, run the AttnRes transformer, evaluate the iMF action generator a small number of times, execute the predicted chunk, and replan on the next observation window.
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-method-overview}}]
+\small Paper Banana prompt: create a clean 16:9 academic system diagram for iMF-AttnRes VLA. Show multi-view images, robot state, and optional language/task conditioning entering a VLA encoder; a policy transformer with AttnRes depth-wise residual aggregation; an improved Mean Flow action generator predicting average flow with one-to-few evaluations; and an exec-length action chunk controlling RoboIMI socket-insert and sim\_transfer robots. Use muted conference-paper colors, clear arrows, no decorative 3D, and labels for iMF, AttnRes, infer1/infer2/infer3, and exec16.
+\end{tcolorbox}
+\caption{Planned method overview figure. The current draft intentionally stores the Paper Banana generation prompt as a placeholder instead of generating an image.}
+\label{fig:method_overview}
+\end{figure}
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-imf-attnres-components}}]
+\small Paper Banana prompt: create a 16:9 technical diagram contrasting classical flow matching instantaneous velocity $dx_t/dt=v(x_t,t)$; Mean Flow average velocity over $[r,t]$ with the JVP correction term; improved Mean Flow training relation $V_\theta(z_t)=u_\theta(z_t)+(t-r)\mathrm{JVP}_{\mathrm{sg}}(u_\theta;v_\theta)$; and AttnRes weighted residual aggregation $a_{t+1,s}$ proportional to $\exp(w_{t+1}\cdot\mathrm{RMSNorm}(y_s))$. Use equation callouts, minimal arrows, and a final callout saying one-to-few-step action generation for robot control.
+\end{tcolorbox}
+\caption{Planned technical component figure. The prompt is retained as a placeholder for later Paper Banana rendering.}
+\label{fig:imf_attnres_components}
+\end{figure}
+
+\section{Experiments}
+\label{sec:experiments}
+
+\subsection{Setup and metrics}
+\label{sec:setup_metrics}
+
+We evaluate in two RoboIMI simulation environments. The socket peg task measures progress toward inserting a peg-like object into a socket. The sim\_transfer task measures object-transfer manipulation. Each reported row uses 100 rollouts. Metrics are copied directly from the rollout logs: average cumulative reward, median reward, mean maximum reward, maximum cumulative reward, count of nonzero-reward episodes, count of success-like episodes, inference FPS, control FPS, and average inference time when available. For socket insertion, the recorded success-like threshold is \texttt{max\_reward > 4}; for sim\_transfer, it is \texttt{max\_reward >= 4}. Hardware differs across runs, so speed comparisons are practical deployment measurements rather than perfectly normalized throughput benchmarks.
+
+\subsection{Socket peg insertion}
+\label{sec:socket_results}
+
+\Cref{tab:socket_results} summarizes the socket peg insertion results. The best iMF-AttnRes policy by average reward is the infer3 model, with average reward 1513.56 and median reward 1901.5. It exceeds both diffusion-style infer100 baselines: the ph16 baseline obtains average reward 861.53 and median reward 668.0, while the ph32/exec16 baseline obtains average reward 1019.39 and median reward 804.0. The latency gap is large. The iMF-AttnRes infer2 and infer3 policies require 14.409 ms and 15.122 ms per inference, while the two infer100 baselines require 338.291 ms and 397.912 ms.
+
+\begin{table*}[t]
+\centering
+\small
+\setlength{\tabcolsep}{3.5pt}
+\caption{Socket peg insertion over 100 rollouts. Success-like uses the recorded \texttt{max\_reward > 4} count.}
+\label{tab:socket_results}
+\begin{tabular}{llrrrrrr}
+\toprule
+Method & Setting & Avg. reward & Median & Avg. max & Nonzero & Success-like & Latency ms \\
+\midrule
+iMF-AttnRes & infer1, ph32, exec16, 50k & 1275.22 & 1490.5 & 3.12 & 84/100 & 0/100 & 16.186 \\
+Diffusion-style VLA & infer100, ph16, 150k & 861.53 & 668.0 & 2.58 & 97/100 & 2/100 & 338.291 \\
+Diffusion-style VLA & infer100, ph32, exec16, 150k & 1019.39 & 804.0 & 2.47 & 92/100 & 4/100 & 397.912 \\
+iMF-AttnRes & infer2, ph32, exec16, 150k & 1472.62 & 1825.0 & 3.29 & 83/100 & 5/100 & 14.409 \\
+iMF-AttnRes & infer3, ph32, exec16, 150k & \textbf{1513.56} & \textbf{1901.5} & 3.28 & 83/100 & 3/100 & 15.122 \\
+ACT & action chunking & 289.60 & 13.0 & 1.29 & 55/100 & 1/100 & 100.212 \\
+SmolVLA & 100k & 466.16 & 106.0 & 1.72 & 89/100 & 0/100 & \textbf{2.614} \\
+\bottomrule
+\end{tabular}
+\end{table*}
+
+The nonzero-reward count reveals an important caveat. The ph16 diffusion-style baseline has 97/100 nonzero episodes, higher than the iMF-AttnRes infer2 and infer3 policies at 83/100. Yet its average and median rewards are much lower. Thus, in this task, nonzero contact or partial progress is not sufficient; the iMF-AttnRes policies more often produce high-reward trajectories when they engage the task successfully. SmolVLA is the fastest method in raw latency and control FPS, but its reward is lower than iMF-AttnRes. ACT underperforms both iMF-AttnRes and the diffusion-style VLA baselines in this setup.
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-socket-reward-latency}}]
+\small Paper Banana prompt: create a 4:3 publication-quality reward-latency comparison for socket peg insertion. Plot methods from the socket table with average reward on the y-axis and average inference time or inverse latency on the x-axis. Highlight iMF-AttnRes infer2/infer3 at 1472.62/1513.56 average reward and 14.409/15.122 ms, diffusion-style infer100 baselines at 861.53/1019.39 average reward and 338.291/397.912 ms, ACT at 289.6 and 100.2116 ms, and SmolVLA at 466.16 and 2.614 ms. Use log-scale latency if helpful and label the speed-quality Pareto frontier.
+\end{tcolorbox}
+\caption{Planned socket reward-latency figure. The current draft contains the Paper Banana prompt placeholder instead of a rendered image.}
+\label{fig:socket_reward_latency}
+\end{figure}
+
+\subsection{Object transfer and ablations}
+\label{sec:sim_transfer_results}
+
+\Cref{tab:sim_transfer_results} reports the object-transfer results. The strongest iMF-AttnRes run reaches average reward 526.22 and 44/100 success-like episodes, exceeding the native diffusion-policy DiT/DDPM/ResNet baseline at average reward 319.2 and 29/100 success-like episodes. This is the clearest sim\_transfer evidence that iMF-AttnRes can improve both reward and success-like threshold count.
+
+\begin{table*}[t]
+\centering
+\small
+\setlength{\tabcolsep}{3.2pt}
+\caption{Object transfer / sim\_transfer over 100 rollouts. Success-like uses the recorded \texttt{max\_reward >= 4} count.}
+\label{tab:sim_transfer_results}
+\begin{tabular}{llrrrrrr}
+\toprule
+Method & Setting & Avg. reward & Median & Avg. max & Nonzero & Success-like & Inf. FPS \\
+\midrule
+Native Diffusion Policy & DiT + DDPM + ResNet & 319.20 & 6.0 & 1.68 & 55/100 & 29/100 & 32.09 \\
+Diffusion-style VLA & emb384, layer18 & 233.52 & 0.0 & 1.12 & 39/100 & 17/100 & 1.859 \\
+iMF multi-token ResNet18 & step34999, ph16, exec08 & 260.66 & 0.0 & 1.12 & 33/100 & 23/100 & \textbf{441.80} \\
+iMF full AttnRes vision & ph16, exec08, 50k & 228.42 & 0.0 & 0.94 & 31/100 & 16/100 & 55.997 \\
+iMF-AttnRes DiT only & ph16, exec16, 50k & 240.64 & 0.0 & 1.02 & 32/100 & 19/100 & 137.996 \\
+iMF-AttnRes DiT only & ph32, exec08, 50k & 163.28 & 0.0 & 0.76 & 26/100 & 12/100 & 85.638 \\
+iMF-AttnRes DiT only & ph32, exec32, 50k & 260.72 & 0.0 & 1.18 & 38/100 & 21/100 & 9.557 \\
+iMF-AttnRes DiT only & ph16, exec08, 50k & 229.56 & 0.0 & 1.18 & 41/100 & 18/100 & 69.984 \\
+iMF-AttnRes DiT only & ph08, exec08, 50k & 237.02 & 0.0 & 1.32 & 48/100 & 18/100 & 69.192 \\
+iMF-AttnRes DiT only & ph32, exec16, 50k & 49.88 & 0.0 & 0.32 & 13/100 & 4/100 & 138.903 \\
+iMF-AttnRes & infer1, ph32, exec16, 50k & \textbf{526.22} & \textbf{86.0} & \textbf{2.14} & \textbf{63/100} & \textbf{44/100} & 8.911 \\
+\bottomrule
+\end{tabular}
+\end{table*}
+
+The ablations are mixed and therefore useful. The ResNet18 multi-token iMF model is extremely fast at 441.80 inference FPS, but its average reward is 260.66, below the native diffusion-policy baseline. The full-AttnRes vision model reaches 228.42 average reward, suggesting that replacing residuals inside the vision encoder is not automatically helpful. Horizon and execution length are also sensitive: the ph32/exec16 DiT-only iMF variant reaches only 49.88 average reward despite high inference FPS. The strongest object-transfer run therefore combines iMF-AttnRes with the right horizon and execution setting rather than showing a universally dominant architectural change.
+
+\begin{figure}[t]
+\centering
+\begin{tcolorbox}[width=0.96\linewidth,colback=white,colframe=black!35,title={Placeholder for \texttt{fig-sim-transfer-ablation}}]
+\small Paper Banana prompt: create a 4:3 grouped bar chart for sim\_transfer ablations. Show average reward and success-like episode count for native diffusion policy, best sim-transfer iMF-AttnRes infer1, ResNet18 multi-token iMF, full-AttnRes vision, and selected horizon/execution variants. Emphasize that best iMF-AttnRes reaches 526.22 average reward and 44/100 success-like episodes versus native diffusion policy at 319.2 and 29/100, while several ablations underperform.
+\end{tcolorbox}
+\caption{Planned sim\_transfer ablation figure. The prompt is retained for later Paper Banana rendering.}
+\label{fig:sim_transfer_ablation}
+\end{figure}
+
+\subsection{Derived speed-quality comparisons}
+\label{sec:derived_comparisons}
+
+\Cref{tab:derived_comparisons} lists the derived comparisons used to summarize the tradeoff. On socket insertion, iMF-AttnRes infer3 improves average reward by 1.76$\times$ over the ph16 diffusion-style baseline and by 1.48$\times$ over the ph32 diffusion-style baseline. The same infer3 run improves inference FPS by 22.97$\times$ over the ph16 diffusion-style baseline. The infer2 run is 23.48$\times$ lower latency than the ph16 diffusion-style baseline and 28.45$\times$ higher inference FPS than the ph32 diffusion-style baseline. On object transfer, best iMF-AttnRes improves average reward by 1.65$\times$ and success-like episodes by +15 over native Diffusion Policy.
+
+\begin{table}[t]
+\centering
+\small
+\caption{Derived comparisons from the 100-rollout logs.}
+\label{tab:derived_comparisons}
+\begin{tabular}{llr}
+\toprule
+Comparison & Metric & Result \\
+\midrule
+Socket iMF infer3 vs ph16 diffusion & Avg. reward & 1.76$\times$ \\
+Socket iMF infer3 vs ph32 diffusion & Avg. reward & 1.48$\times$ \\
+Socket iMF infer3 vs ACT & Avg. reward & 5.23$\times$ \\
+Socket iMF infer3 vs SmolVLA & Avg. reward & 3.25$\times$ \\
+Socket iMF infer2 vs ph16 diffusion & Latency reduction & 23.48$\times$ \\
+Socket iMF infer3 vs ph16 diffusion & Inference FPS & 22.97$\times$ \\
+Socket iMF infer2 vs ph32 diffusion & Inference FPS & 28.45$\times$ \\
+Sim transfer iMF vs native diffusion & Avg. reward & 1.65$\times$ \\
+Sim transfer iMF vs native diffusion & Success-like episodes & +15 \\
+ResNet18 iMF vs layer18 baseline & Inference FPS & 237.65$\times$ \\
+\bottomrule
+\end{tabular}
+\end{table}
+
+\section{Limitations}
+\label{sec:limitations}
+
+All experiments are simulation rollouts. The data do not establish real-robot transfer, robustness to sensing changes, or safety under hardware execution. CoRL submissions should provide convincing robotics evidence; the present draft should therefore be viewed as a simulation-first manuscript that still needs real-robot or stronger transfer evidence before formal submission.
+
+The speed measurements are also not perfectly hardware-normalized. Runs were collected on RTX 5880 Ada, L20, and RTX 5090 machines as encoded by their run names. Large latency differences between infer100 diffusion-style policies and infer1--3 iMF-AttnRes policies are meaningful because they reflect algorithmic sampling cost, but exact FPS ratios may include hardware and implementation effects. Future experiments should rerun all main policies on the same GPU, with identical rollout parallelism and identical observation preprocessing.
+
+Finally, iMF-AttnRes is sensitive to design choices. In sim\_transfer, several iMF variants underperform the native diffusion-policy baseline, and full AttnRes in the vision encoder performs worse than more conservative policy-transformer AttnRes. This suggests that average-flow training is not a plug-in guarantee; horizon, execution length, visual tokenization, and residual placement must be tuned carefully.
+
+\section{Conclusion}
+\label{sec:conclusion}
+
+We presented a first simulation study of iMF-AttnRes for fast robot action generation. The method adapts improved Mean Flow to VLA imitation learning and uses Attention Residuals to replace fixed residual accumulation in the policy transformer. In socket peg insertion, iMF-AttnRes achieves higher average and median reward than diffusion-style infer100 baselines while reducing inference latency from hundreds of milliseconds to roughly 15 ms. In object transfer, the best iMF-AttnRes run improves average reward and success-like episode count over the native diffusion-policy baseline. At the same time, ablations show that the method is sensitive to horizon/execution settings and residual placement. The next step is controlled, hardware-normalized evaluation with real-robot transfer.
+
+\clearpage
+\bibliography{refs}
+
+\end{document}
diff --git a/workspace/refinement/worklog.json b/workspace/refinement/worklog.json
new file mode 100644
index 0000000..5ce6e07
--- /dev/null
+++ b/workspace/refinement/worklog.json
@@ -0,0 +1,7 @@
+[
+  {
+    "iteration": 0,
+    "decision": "accepted_first_draft",
+    "notes": "Generated first version only. No iterative content-refinement loop was run because user asked to first write one version for inspection."
+  }
+]
diff --git a/workspace/refs.bib b/workspace/refs.bib
new file mode 100644
index 0000000..a892f5b
--- /dev/null
+++ b/workspace/refs.bib
@@ -0,0 +1,140 @@
+@article{chi2023diffusion,
+  title = {Diffusion Policy: Visuomotor Policy Learning via Action Diffusion},
+  author = {Chi, Cheng and Xu, Zhenjia and Feng, Siyuan and Cousineau, Eric and Du, Yilun and Burchfiel, Benjamin and Tedrake, Russ and Song, Shuran},
+  year = {2023},
+  journal = {arXiv preprint arXiv:2303.04137},
+  eprint = {2303.04137},
+  archivePrefix = {arXiv}
+}
+
+@inproceedings{ho2020denoising,
+  title = {Denoising Diffusion Probabilistic Models},
+  author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
+  year = {2020},
+  booktitle = {Advances in Neural Information Processing Systems}
+}
+
+@inproceedings{peebles2023scalable,
+  title = {Scalable Diffusion Models with Transformers},
+  author = {Peebles, William and Xie, Saining},
+  year = {2023},
+  booktitle = {IEEE/CVF International Conference on Computer Vision}
+}
+
+@inproceedings{lipman2023flow,
+  title = {Flow Matching for Generative Modeling},
+  author = {Lipman, Yaron and Chen, Ricky T. Q. and Ben-Hamu, Heli and Nickel, Maximilian and Le, Matt},
+  year = {2023},
+  booktitle = {International Conference on Learning Representations}
+}
+
+@article{liu2022flow,
+  title = {Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow},
+  author = {Liu, Xingchao and Gong, Chengyue and Liu, Qiang},
+  year = {2022},
+  journal = {arXiv preprint arXiv:2209.03003},
+  eprint = {2209.03003},
+  archivePrefix = {arXiv}
+}
+
+@article{geng2025mean,
+  title = {Mean Flows for One-step Generative Modeling},
+  author = {Geng, Zhengyang and Deng, Mingyang and Bai, Xingjian and Kolter, J. Zico and He, Kaiming},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2505.13447},
+  eprint = {2505.13447},
+  archivePrefix = {arXiv}
+}
+
+@article{geng2025improved,
+  title = {Improved Mean Flows: On the Challenges of Fastforward Generative Models},
+  author = {Geng, Zhengyang and Lu, Yiyang and Wu, Zongze and Shechtman, Eli and Kolter, J. Zico and He, Kaiming},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2512.02012},
+  eprint = {2512.02012},
+  archivePrefix = {arXiv}
+}
+
+@article{brohan2022rt,
+  title = {RT-1: Robotics Transformer for Real-World Control at Scale},
+  author = {Brohan, Anthony and Brown, Noah and Carbajal, Justice and Chebotar, Yevgen and Dabis, Joseph and Finn, Chelsea and Gopalakrishnan, Keerthana and Hausman, Karol and Herzog, Alexander and Hsu, Jasmine and others},
+  year = {2022},
+  journal = {arXiv preprint arXiv:2212.06817},
+  eprint = {2212.06817},
+  archivePrefix = {arXiv}
+}
+
+@inproceedings{brohan2023rt,
+  title = {RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control},
+  author = {Brohan, Anthony and Brown, Noah and Carbajal, Justice and Chebotar, Yevgen and Chen, Xi and Choromanski, Krzysztof and Ding, Tianli and Driess, Danny and Dubey, Avinava and Finn, Chelsea and others},
+  year = {2023},
+  booktitle = {Conference on Robot Learning}
+}
+
+@article{kim2024openvla,
+  title = {OpenVLA: An Open-Source Vision-Language-Action Model},
+  author = {Kim, Moo Jin and Pertsch, Karl and Karamcheti, Siddharth and Xiao, Ted and Balakrishna, Ashwin and Nair, Suraj and Rafailov, Rafael and Foster, Ethan and Lam, Grace and Sanketi, Pannag and others},
+  year = {2024},
+  journal = {arXiv preprint arXiv:2406.09246},
+  eprint = {2406.09246},
+  archivePrefix = {arXiv}
+}
+
+@article{shukor2025smolvla,
+  title = {SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics},
+  author = {Shukor, Mustafa and Aubakirova, Dana and Capuano, Francesco and Kooijmans, Pepijn and Palma, Steven and Zouitine, Adil and Aractingi, Michel and Pascal, Caroline and Russi, Martino and Marafioti, Andres and others},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2506.01844},
+  eprint = {2506.01844},
+  archivePrefix = {arXiv}
+}
+
+@article{black2024pi0,
+  title = {{$\pi_0$}: A Vision-Language-Action Flow Model for General Robot Control},
+  author = {Black, Kevin and Brown, Noah and Driess, Danny and Esmail, Adnan and Equi, Michael and Finn, Chelsea and Fusai, Niccolo and Groom, Lachy and Hausman, Karol and Ichter, Brian and others},
+  year = {2024},
+  journal = {arXiv preprint arXiv:2410.24164},
+  eprint = {2410.24164},
+  archivePrefix = {arXiv}
+}
+
+@article{pertsch2025fast,
+  title = {FAST: Efficient Action Tokenization for Vision-Language-Action Models},
+  author = {Pertsch, Karl and Stachowicz, Kyle and Ichter, Brian and Driess, Danny and Nair, Suraj and Vuong, Quan and Mees, Oier and Finn, Chelsea and Levine, Sergey},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2501.09747},
+  eprint = {2501.09747},
+  archivePrefix = {arXiv}
+}
+
+@article{zhao2023learning,
+  title = {Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware},
+  author = {Zhao, Tony Z. and Kumar, Vikash and Levine, Sergey and Finn, Chelsea},
+  year = {2023},
+  journal = {arXiv preprint arXiv:2304.13705},
+  eprint = {2304.13705},
+  archivePrefix = {arXiv}
+}
+
+@inproceedings{he2016deep,
+  title = {Deep Residual Learning for Image Recognition},
+  author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
+  year = {2016},
+  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition}
+}
+
+@inproceedings{vaswani2017attention,
+  title = {Attention Is All You Need},
+  author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
+  year = {2017},
+  booktitle = {Advances in Neural Information Processing Systems}
+}
+
+@article{kimi2026attention,
+  title = {Attention Residuals},
+  author = {{Kimi Team}},
+  year = {2026},
+  journal = {arXiv preprint arXiv:2603.15031},
+  eprint = {2603.15031},
+  archivePrefix = {arXiv}
+}
diff --git a/workspace/tex_profile.json b/workspace/tex_profile.json
new file mode 100644
index 0000000..27ccca1
--- /dev/null
+++ b/workspace/tex_profile.json
@@ -0,0 +1,21 @@
+{
+  "available": [
+    "cleveref",
+    "nicefrac",
+    "microtype",
+    "url",
+    "booktabs",
+    "natbib",
+    "hyperref",
+    "fontenc",
+    "times",
+    "lmodern"
+  ],
+  "missing": [],
+  "use_cleveref": true,
+  "use_nicefrac": true,
+  "use_microtype": true,
+  "use_t1_fontenc": true,
+  "tex_binary": "pdflatex",
+  "checked_at": "2026-05-15T11:10:11.905693"
+}
\ No newline at end of file