Logic
369 lines
25 KiB
JSON
369 lines
25 KiB
JSON
{
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"plotting_plan": [
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{
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"figure_id": "fig_method_overview",
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"title": "iMF-AttnRes VLA Overview",
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"plot_type": "diagram",
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"data_source": "idea.md",
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"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.",
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"aspect_ratio": "16:9"
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},
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{
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"figure_id": "fig_socket_reward_latency",
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"title": "Socket Insertion Reward-Latency Tradeoff",
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"plot_type": "plot",
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"data_source": "experimental_log.md",
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"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.",
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"aspect_ratio": "4:3"
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},
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{
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"figure_id": "fig_sim_transfer_ablation",
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"title": "Sim Transfer Ablation Summary",
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"plot_type": "plot",
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"data_source": "experimental_log.md",
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"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.",
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"aspect_ratio": "4:3"
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},
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{
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"figure_id": "fig_imf_attnres_components",
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"title": "iMF and AttnRes Component Mechanics",
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"plot_type": "diagram",
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"data_source": "idea.md",
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"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.",
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"aspect_ratio": "16:9"
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}
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],
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"intro_related_work_plan": {
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"introduction_strategy": {
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"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.",
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"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.",
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"search_directions": [
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"Find foundational and survey papers on imitation learning and visuomotor policy learning for robotic manipulation before 2026-04-15",
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"Find papers on diffusion policy and action diffusion for visuomotor robot control before 2026-04-15",
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"Find papers on vision-language-action robot policies, including RT-1, RT-2, OpenVLA, SmolVLA, and flow-based VLA models before 2026-04-15",
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"Find papers on flow matching, rectified flow, Mean Flow, and improved Mean Flow for one-step or few-step generation before 2026-04-15",
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"Find papers on residual connections, Transformers, PreNorm residual architectures, hyper-connections, and attention residual aggregation before 2026-04-15"
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]
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},
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"related_work_strategy": {
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"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.",
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"subsections": [
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{
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"subsection_title": "2.1 Diffusion and Flow Policies for Robot Action Generation",
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"methodology_cluster": "Diffusion Policy, action diffusion, flow matching, and generative action policies",
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"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.",
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"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.",
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"limitation_search_queries": [
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"Diffusion Policy Visuomotor Policy Learning via Action Diffusion latency sampling steps",
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"robot manipulation action diffusion policy inference speed sampling steps",
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"flow matching robot policy action generation one-step few-step"
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],
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"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."
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},
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{
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"subsection_title": "2.2 Vision-Language-Action Models and Action Chunking",
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"methodology_cluster": "VLA policies, action tokenization, action chunking, and transformer-based robot policies",
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"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.",
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"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.",
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"limitation_search_queries": [
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"OpenVLA vision language action robot policy imitation learning",
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"RT-1 RT-2 robotics transformer vision language action control",
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"Action Chunking with Transformers ALOHA imitation learning",
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"SmolVLA efficient vision language action robotics"
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],
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"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."
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},
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{
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"subsection_title": "2.3 Mean-Flow Training for One-Step Generation",
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"methodology_cluster": "Flow matching, rectified flow, Mean Flow, improved Mean Flow, and fast-forward generative models",
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"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.",
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"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.",
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"limitation_search_queries": [
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"Mean Flows for One-step Generative Modeling average velocity",
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"Improved Mean Flows fast-forward generative models conditional velocity marginal velocity",
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"Flow Matching for Generative Modeling conditional vector field",
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"Rectified Flow generative modeling one step sampling"
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],
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"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."
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},
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{
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"subsection_title": "2.4 Residual Aggregation and Attention Residuals in Deep Transformers",
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"methodology_cluster": "Residual connections, PreNorm Transformers, hyper-connections, and AttnRes",
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"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.",
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"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.",
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"limitation_search_queries": [
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"Attention Residuals AttnRes PreNorm dilution hidden-state growth",
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"residual connections transformer PreNorm gradient flow depth",
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"hyper-connections deep transformers residual aggregation",
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"Deep Residual Learning for Image Recognition residual connections"
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],
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"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."
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}
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]
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}
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},
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"section_plan": [
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{
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"section_title": "Abstract",
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"subsections": [
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{
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"subsection_title": "Abstract Content",
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"content_bullets": [
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"State the robotics problem: diffusion-style VLA policies are expressive but slow when closed-loop control needs many policy evaluations.",
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"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.",
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"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.",
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"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.",
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"Mention limitations: simulation-only, heterogeneous hardware, and sensitivity to horizon/execution settings."
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],
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"citation_hints": []
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},
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{
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"subsection_title": "Abstract Grounding Checks",
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"content_bullets": [
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"Ensure every numeric claim in the abstract is copied from experimental_log.md Table 1, Table 2, or Table 3.",
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"Do not cite papers in the abstract unless the final manuscript style requires it; preserve double-blind anonymity."
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],
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"citation_hints": []
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}
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]
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},
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{
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"section_title": "1. Introduction",
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"subsections": [
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{
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"subsection_title": "1.1 Motivation and Problem Setting",
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"content_bullets": [
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"Motivate fast closed-loop manipulation in RoboIMI socket-insert and sim_transfer environments using the latency and reward metrics in experimental_log.md.",
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"Explain that iterative diffusion-style inference can require infer100 evaluations in the baselines, producing hundreds of milliseconds per inference on socket-insert runs.",
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"Frame the research question: can a one-to-few-step average-flow action generator preserve or improve manipulation reward while reducing inference latency?"
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],
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"citation_hints": [
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"Chi et al. (Diffusion Policy: Visuomotor Policy Learning via Action Diffusion)",
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"research paper or technical report introducing 'Vision-Language-Action robot policies'"
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]
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},
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{
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"subsection_title": "1.2 Contributions",
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"content_bullets": [
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"List contributions: adapting improved Mean Flow to VLA action generation, integrating AttnRes into the policy transformer, and evaluating on two RoboIMI simulated manipulation tasks.",
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"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.",
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"Emphasize honest scope: first draft simulation evidence rather than real-robot transfer."
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],
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"citation_hints": [
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"Geng et al. (Mean Flows for One-step Generative Modeling)",
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"Geng et al. (Improved Mean Flows: On the Challenges of Fast-Forward Generative Models)",
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"research paper or technical report introducing 'Attention Residuals'"
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]
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}
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]
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},
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{
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"section_title": "2. Related Work",
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"subsections": [
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{
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"subsection_title": "2.1 Diffusion and Flow Policies for Robot Action Generation",
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"content_bullets": [
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"Discuss diffusion-policy-style visuomotor action generation and its reliance on iterative denoising or repeated policy evaluations.",
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"Connect diffusion and flow matching as continuous generative modeling tools, then distinguish iMF-AttnRes by its average-flow one-to-few-step inference objective.",
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"Use related-work cluster 2.1 from outline.json to bridge to the method."
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],
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"citation_hints": [
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"Chi et al. (Diffusion Policy: Visuomotor Policy Learning via Action Diffusion)",
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"Ho et al. (Denoising Diffusion Probabilistic Models)",
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"Lipman et al. (Flow Matching for Generative Modeling)",
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"Liu et al. (Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow)",
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"Peebles and Xie (Scalable Diffusion Models with Transformers)"
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]
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},
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{
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"subsection_title": "2.2 Vision-Language-Action Models and Action Chunking",
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"content_bullets": [
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"Summarize transformer and VLA robot policies as the broader setting for the proposed policy.",
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"Discuss action chunking and compact VLA policies as complementary routes to practical control speed.",
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"Position iMF-AttnRes as an action-generation module that can coexist with VLA conditioning and chunked execution."
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],
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"citation_hints": [
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"Brohan et al. (RT-1: Robotics Transformer for Real-World Control at Scale)",
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"Brohan et al. (RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control)",
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"Kim et al. (OpenVLA: An Open-Source Vision-Language-Action Model)",
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"Zhao et al. (Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware)",
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"research paper or technical report introducing 'SmolVLA'"
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]
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},
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{
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"subsection_title": "2.3 Mean-Flow Training for One-Step Generation",
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"content_bullets": [
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"Use the derivations in idea.md to explain the distinction between instantaneous velocity v(x_t,t) and average velocity over [r,t].",
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"Explain the Mean Flow relation between average velocity and instantaneous velocity and why the JVP term appears.",
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"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."
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],
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"citation_hints": [
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"Geng et al. (Mean Flows for One-step Generative Modeling)",
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"Geng et al. (Improved Mean Flows: On the Challenges of Fast-Forward Generative Models)",
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"Lipman et al. (Flow Matching for Generative Modeling)",
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"Liu et al. (Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow)"
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]
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},
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{
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"subsection_title": "2.4 Residual Aggregation and Attention Residuals",
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"content_bullets": [
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"Derive the residual accumulation view in idea.md: x_t = y_0 + ... + y_t and y_{t+1}=f_{t+1}(sum y_s).",
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"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)).",
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"Discuss why learned residual aggregation may help deeper policy networks avoid feature dilution and gradient imbalance."
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],
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"citation_hints": [
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"He et al. (Deep Residual Learning for Image Recognition)",
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"Vaswani et al. (Attention Is All You Need)",
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"research paper or technical report introducing 'Attention Residuals'",
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"research paper or technical report introducing 'PreNorm Transformers'"
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]
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}
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]
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},
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{
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"section_title": "3. Method",
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"subsections": [
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{
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"subsection_title": "3.1 Problem Formulation",
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"content_bullets": [
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"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.",
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"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.",
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"Avoid inventing dataset sizes, optimizer settings, or training details not present in idea.md or experimental_log.md."
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],
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"citation_hints": [
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"research paper or technical report introducing 'behavior cloning for robotics'",
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"research paper or technical report introducing 'Transformer architecture'"
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]
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},
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{
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"subsection_title": "3.2 Improved Mean Flow Action Generation",
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"content_bullets": [
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"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.",
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"Show the Mean Flow identity ̄v = v - (t-r) d̄v/dt and the JVP expression jvp(̄v,(z,r,t),(v,0,1)).",
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"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."
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],
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"citation_hints": [
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"Geng et al. (Mean Flows for One-step Generative Modeling)",
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"Geng et al. (Improved Mean Flows: On the Challenges of Fast-Forward Generative Models)",
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"Lipman et al. (Flow Matching for Generative Modeling)"
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]
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},
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{
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"subsection_title": "3.3 Attention Residual Policy Transformer",
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"content_bullets": [
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"Use the residual decomposition from idea.md to show fixed residual accumulation as a uniform sum over layer outputs.",
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"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.",
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"State that the paper uses AttnRes primarily in the policy transformer in the strongest variants; the full-AttnRes vision ablation underperformed in sim_transfer."
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],
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"citation_hints": [
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"He et al. (Deep Residual Learning for Image Recognition)",
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"Vaswani et al. (Attention Is All You Need)",
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"research paper or technical report introducing 'Attention Residuals'"
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]
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},
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{
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"subsection_title": "3.4 Inference and Control Loop",
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"content_bullets": [
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"Describe one-to-few inference evaluations for iMF-AttnRes, using infer1/infer2/infer3 in socket runs and infer1 in sim_transfer best run.",
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"Contrast this with diffusion-style infer100 baselines in experimental_log.md.",
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"Explain that predicted chunks are executed for exec steps, e.g., exec16 in the best socket and sim_transfer iMF-AttnRes variants."
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],
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"citation_hints": [
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"Chi et al. (Diffusion Policy: Visuomotor Policy Learning via Action Diffusion)",
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"Zhao et al. (Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware)"
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]
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}
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]
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},
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{
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"section_title": "4. Experiments",
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"subsections": [
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{
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"subsection_title": "4.1 Experimental Setup",
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"content_bullets": [
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"Describe RoboIMI socket-insert and sim_transfer environments as simulation benchmarks using only details stated in experimental_log.md.",
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"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.",
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"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."
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],
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"citation_hints": [
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"research paper or technical report introducing 'Diffusion Policy: Visuomotor Policy Learning via Action Diffusion'",
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"research paper or technical report introducing 'ACT action chunking transformer'",
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"research paper or technical report introducing 'SmolVLA'"
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]
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},
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{
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"subsection_title": "4.2 Socket Peg Insertion Results",
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"content_bullets": [
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"Create a LaTeX table from Table 1 in experimental_log.md with the seven socket policies and their reward/speed metrics.",
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"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.",
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"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.",
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"Discuss the nonzero_reward caveat: diffusion-style ph16 has 97/100 nonzero episodes, but much lower median and average reward than iMF-AttnRes."
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],
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"citation_hints": []
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},
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{
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"subsection_title": "4.3 Sim Transfer Results and Ablations",
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"content_bullets": [
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"Create a LaTeX table from Table 2 in experimental_log.md summarizing native diffusion policy, diffusion-style VLA, and iMF ablations.",
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"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.",
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"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."
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],
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"citation_hints": []
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},
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{
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"subsection_title": "4.4 Speed-Quality Tradeoff and Failure Modes",
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"content_bullets": [
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"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.",
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"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.",
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"Emphasize that iMF-AttnRes improves latency but still needs controlled hardware-normalized studies and real-robot transfer validation."
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],
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"citation_hints": []
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}
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]
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},
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{
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"section_title": "5. Limitations",
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"subsections": [
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{
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"subsection_title": "5.1 Scope and Evaluation Limits",
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"content_bullets": [
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"Explicitly satisfy CoRL requirement by describing simulation-only evaluation and lack of real-robot experiments.",
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"State heterogeneous hardware makes speed comparisons imperfect and motivates future hardware-normalized benchmarking.",
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"State that iMF-AttnRes sensitivity to horizon/execution choices and full-AttnRes vision underperformance are current limitations."
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],
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"citation_hints": []
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},
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{
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"subsection_title": "5.2 Future Work",
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"content_bullets": [
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"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.",
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"Mention possible integration with efficient VLA backbones and action tokenization but avoid claiming untested results."
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],
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"citation_hints": []
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}
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]
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},
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{
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"section_title": "6. Conclusion",
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"subsections": [
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{
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"subsection_title": "Conclusion Content",
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"content_bullets": [
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"Summarize that improved Mean Flow plus AttnRes is a practical recipe for low-latency VLA action generation in the reported RoboIMI simulations.",
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"Restate the strongest socket and sim_transfer quantitative evidence without adding new numbers.",
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"Close with a balanced statement that this is promising simulation evidence requiring real-robot validation."
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],
|
|
"citation_hints": []
|
|
},
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{
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"subsection_title": "Concluding Caveats",
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"content_bullets": [
|
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"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": []
|
|
}
|
|
]
|
|
}
|
|
]
|
|
} |