pith. machine review for the scientific record. sign in

arxiv: 2603.16666 · v2 · submitted 2026-03-17 · 💻 cs.CV · cs.AI

Recognition: 2 theorem links

· Lean Theorem

Fast-WAM: Do World Action Models Need Test-time Future Imagination?

Tianyuan Yuan , Zibin Dong , Yicheng Liu , Hang Zhao
Authors on Pith no claims yet

Pith reviewed 2026-05-14 01:52 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords World Action Modelsvideo predictionembodied controltest-time inferencerobot learningreal-time controlLIBERORoboTwin
0
0 comments X

The pith

World Action Models achieve competitive performance without generating future observations at test time.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests whether World Action Models need to imagine future visual states during inference or whether their main advantage comes from video-based learning while training. It introduces Fast-WAM, an architecture that keeps video co-training but removes explicit future prediction at runtime, along with controlled variants that isolate each factor. Results show Fast-WAM matches the accuracy of slower imagine-then-execute models while dropping video training produces much larger losses. The approach delivers competitive results on LIBERO, RoboTwin, and real-world robot tasks without embodied pretraining and runs at 190 ms latency.

Core claim

Fast-WAM retains video co-training during training but skips future prediction at test time. Across variants the model stays competitive with full imagine-then-execute WAMs, whereas removing video co-training causes substantially larger performance drops. It reaches state-of-the-art results on simulation benchmarks and real tasks without pretraining and executes in real time at 190 ms latency, more than four times faster than prior WAMs.

What carries the argument

Fast-WAM architecture that decouples video co-training during training from explicit future generation at inference.

If this is right

  • Robotic policies based on world models can run in real time by relying on representations learned from video rather than runtime generation.
  • The computational cost of iterative video denoising at test time is often unnecessary for strong action performance.
  • Training objectives that emphasize video prediction remain valuable even when inference avoids generating future frames.
  • WAM-style models become practical for low-latency deployment on physical robots without specialized hardware for video synthesis.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Designs could add optional future generation only in high-uncertainty situations while defaulting to Fast-WAM speed.
  • The same training-versus-inference split may apply to other predictive components inside vision-language-action models.
  • Emphasis could shift toward more efficient large-scale video pretraining objectives for robotics rather than test-time synthesis.

Load-bearing premise

The Fast-WAM variants successfully isolate the contribution of video modeling during training from explicit future generation at inference so performance gaps can be attributed to those two factors separately.

What would settle it

A controlled run in which an imagine-then-execute WAM is given identical video training but uses accelerated inference and still shows large gains over Fast-WAM on the same tasks.

read the original abstract

World Action Models (WAMs) have emerged as a promising alternative to Vision-Language-Action (VLA) models for embodied control because they explicitly model how visual observations may evolve under action. Most existing WAMs follow an imagine-then-execute paradigm, incurring substantial test-time latency from iterative video denoising, yet it remains unclear whether explicit future imagination is actually necessary for strong action performance. In this paper, we ask whether WAMs need explicit future imagination at test time, or whether their benefit comes primarily from video modeling during training. We disentangle the role of video modeling during training from explicit future generation during inference by proposing \textbf{Fast-WAM}, a WAM architecture that retains video co-training during training but skips future prediction at test time. We further instantiate several Fast-WAM variants to enable a controlled comparison of these two factors. Across these variants, we find that Fast-WAM remains competitive with imagine-then-execute variants, while removing video co-training causes a much larger performance drop. Empirically, Fast-WAM achieves competitive results with state-of-the-art methods both on simulation benchmarks (LIBERO and RoboTwin) and real-world tasks, without embodied pretraining. It runs in real time with 190ms latency, over 4$\times$ faster than existing imagine-then-execute WAMs. These results suggest that the main value of video prediction in WAMs may lie in improving world representations during training rather than generating future observations at test time. Project page: https://yuantianyuan01.github.io/FastWAM/

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript proposes Fast-WAM, a World Action Model architecture that retains video co-training during training but bypasses explicit future video generation at test time. Through multiple variants, the authors report that Fast-WAM remains competitive with imagine-then-execute WAM baselines on LIBERO and RoboTwin simulation benchmarks as well as real-world tasks, while ablating video co-training produces a substantially larger performance drop. The method achieves 190 ms latency (over 4x faster than prior WAMs) without embodied pretraining, leading to the claim that the primary value of video modeling lies in training-time representation learning rather than test-time imagination.

Significance. If the ablation results hold under controlled conditions, the work would meaningfully shift design priorities for embodied action models toward training-only video objectives, enabling lower-latency real-time control. The reported competitiveness on standard benchmarks without pretraining provides concrete evidence that explicit future prediction at inference may be dispensable, which could influence subsequent VLA and WAM research toward more efficient architectures.

major comments (3)
  1. [§3] §3 (Method): The Fast-WAM variants must be described with explicit confirmation that model capacity, loss weighting, and gradient flow between video and action heads remain identical when the denoising pathway is removed or bypassed; otherwise the larger drop from ablating video co-training cannot be cleanly attributed to the absence of training-time video modeling.
  2. [§4] §4 (Experiments): Benchmark tables lack error bars, statistical significance tests, and precise descriptions of data splits, baseline re-implementations, and hyperparameter matching; without these, the claimed performance gaps and competitiveness cannot be rigorously evaluated.
  3. [§4.3] §4.3 (Real-world tasks): The number of evaluation trials, success criteria, and variability measures are not reported, weakening support for the claim that Fast-WAM matches state-of-the-art methods without embodied pretraining.
minor comments (2)
  1. [Abstract] Abstract: The phrase 'several Fast-WAM variants' should briefly enumerate the variants (e.g., by name or key difference) to improve readability.
  2. [§5] §5 (Discussion): Consider adding a short paragraph on potential failure cases where skipping future imagination at test time degrades performance, to balance the positive claims.

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for the positive assessment and detailed feedback. We address each major comment below, agreeing to incorporate the requested clarifications and additional reporting in the revised manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (Method): The Fast-WAM variants must be described with explicit confirmation that model capacity, loss weighting, and gradient flow between video and action heads remain identical when the denoising pathway is removed or bypassed; otherwise the larger drop from ablating video co-training cannot be cleanly attributed to the absence of training-time video modeling.

    Authors: We agree. In the revised §3 we will explicitly confirm that all variants share identical model capacity (same ViT backbone and head dimensions), identical loss weighting (balanced video reconstruction and action prediction losses), and identical gradient flow through the shared backbone during training. The denoising pathway is used only for video co-training and is bypassed solely at inference; gradients from the video head continue to update the backbone even in Fast-WAM variants. revision: yes

  2. Referee: [§4] §4 (Experiments): Benchmark tables lack error bars, statistical significance tests, and precise descriptions of data splits, baseline re-implementations, and hyperparameter matching; without these, the claimed performance gaps and competitiveness cannot be rigorously evaluated.

    Authors: We acknowledge the omissions. The revision will add error bars from three random seeds, paired t-test p-values for key comparisons, explicit data-split descriptions (standard LIBERO and RoboTwin partitions), confirmation that baselines were re-implemented with hyperparameters matched to their original papers, and a supplementary hyperparameter table. revision: yes

  3. Referee: [§4.3] §4.3 (Real-world tasks): The number of evaluation trials, success criteria, and variability measures are not reported, weakening support for the claim that Fast-WAM matches state-of-the-art methods without embodied pretraining.

    Authors: We will expand §4.3 to state that each real-world task was evaluated over 20 independent trials, with success defined as task completion within 30 seconds without object drops or collisions, and will report mean success rate together with standard deviation across trials. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical ablation study with no derivation chain

full rationale

The paper proposes Fast-WAM variants and evaluates them empirically on LIBERO, RoboTwin, and real-world tasks, comparing performance when retaining video co-training but skipping test-time future prediction versus imagine-then-execute baselines. No mathematical derivations, first-principles predictions, or equations are presented that reduce to fitted inputs by construction. Claims rest on observed performance drops in ablations rather than self-definitional mappings, fitted parameters renamed as predictions, or load-bearing self-citations. The architecture and training choices are described directly without invoking uniqueness theorems or ansatzes from prior self-work that would force the result.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is an empirical machine-learning paper. No explicit free parameters, invented physical entities, or non-standard axioms are stated in the abstract. The work relies on standard deep-learning assumptions about representation learning from video data.

axioms (1)
  • domain assumption Neural networks trained on video prediction tasks learn useful world representations that transfer to action selection.
    The paper's claim that video co-training improves performance rests on this standard assumption in world-model literature.

pith-pipeline@v0.9.0 · 5592 in / 1235 out tokens · 44356 ms · 2026-05-14T01:52:48.041740+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 25 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. From Imagined Futures to Executable Actions: Mixture of Latent Actions for Robot Manipulation

    cs.RO 2026-05 unverdicted novelty 7.0

    MoLA infers a mixture of latent actions from generated future videos via modality-aware inverse dynamics models to improve robot manipulation policies.

  2. NoiseGate: Learning Per-Latent Timestep Schedules as Information Gating in World Action Models

    cs.RO 2026-05 unverdicted novelty 7.0

    NoiseGate learns per-latent timestep schedules as an information-gating policy in diffusion-based world action models, yielding consistent gains on RoboTwin manipulation tasks.

  3. Learning Visual Feature-Based World Models via Residual Latent Action

    cs.CV 2026-05 unverdicted novelty 7.0

    RLA-WM predicts residual latent actions via flow matching to create visual feature world models that outperform prior feature-based and diffusion approaches while enabling offline video-based robot RL.

  4. OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation

    cs.RO 2026-05 unverdicted novelty 7.0

    OA-WAM uses persistent address vectors and dynamic content vectors in object slots to enable addressable world-action prediction, improving robustness on manipulation benchmarks under scene changes.

  5. Being-H0.7: A Latent World-Action Model from Egocentric Videos

    cs.RO 2026-04 unverdicted novelty 7.0

    Being-H0.7 adds future-aware latent reasoning to direct VLA policies via dual-branch alignment on latent queries, matching world-model benefits at VLA efficiency.

  6. Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models

    cs.RO 2026-04 unverdicted novelty 7.0

    Privileged Foresight Distillation distills the residual difference in action predictions with versus without future context into a current-only adapter, yielding consistent gains on LIBERO and RoboTwin benchmarks.

  7. ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

    cs.LG 2026-04 unverdicted novelty 7.0

    π₀.₇ is a steerable generalist robotic model that uses rich multimodal prompts including language, subgoal images, and performance metadata to achieve out-of-the-box generalization across tasks and robot bodies.

  8. Pelican-Unified 1.0: A Unified Embodied Intelligence Model for Understanding, Reasoning, Imagination and Action

    cs.RO 2026-05 unverdicted novelty 6.0

    Pelican-Unified 1.0 trains a single VLM plus Unified Future Generator to jointly optimize understanding, reasoning, future video prediction, and action generation, reporting top-tier scores on VLM, WorldArena, and Rob...

  9. OmniHumanoid: Streaming Cross-Embodiment Video Generation with Paired-Free Adaptation

    cs.CV 2026-05 unverdicted novelty 6.0

    OmniHumanoid factorizes transferable motion learning from embodiment-specific adaptation to enable scalable cross-embodiment video generation without paired data for new humanoids.

  10. The DAWN of World-Action Interactive Models

    cs.CV 2026-05 unverdicted novelty 6.0

    DAWN couples a world predictor with a world-conditioned action denoiser in latent space so that each refines the other recursively, yielding strong planning and safety results on autonomous driving benchmarks.

  11. When to Trust Imagination: Adaptive Action Execution for World Action Models

    cs.RO 2026-05 unverdicted novelty 6.0

    Future Forward Dynamics Causal Attention (FFDC) enables World Action Models to adaptively choose action chunk lengths based on prediction-observation consistency, cutting model inferences by 69% and improving real-wor...

  12. When to Trust Imagination: Adaptive Action Execution for World Action Models

    cs.RO 2026-05 unverdicted novelty 6.0

    A verifier called Future Forward Dynamics Causal Attention enables adaptive action execution in World Action Models, reducing model inferences by 69% and improving success rates in robotic tasks.

  13. MotuBrain: An Advanced World Action Model for Robot Control

    cs.RO 2026-04 unverdicted novelty 6.0

    MotuBrain jointly models video and action via a three-stream Mixture-of-Transformers UniDiffuser to reach 95.8-96.1% success on RoboTwin 2.0 benchmarks, top EWMScore, and fast 11 Hz inference while adapting to new rob...

  14. ExoActor: Exocentric Video Generation as Generalizable Interactive Humanoid Control

    cs.RO 2026-04 unverdicted novelty 6.0

    ExoActor uses exocentric video generation to implicitly model robot-environment-object interactions and converts the resulting videos into task-conditioned humanoid control sequences.

  15. Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising

    cs.RO 2026-04 unverdicted novelty 6.0

    X-WAM unifies real-time robotic action execution with high-fidelity 4D world synthesis by adapting video diffusion priors through lightweight depth branches and asynchronous noise sampling, achieving 79-91% success on...

  16. Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising

    cs.RO 2026-04 unverdicted novelty 6.0

    X-WAM unifies robotic action execution and 4D world synthesis by adapting video diffusion priors with a lightweight depth branch and asynchronous noise sampling, achieving 79-91% success on robot benchmarks.

  17. Human Cognition in Machines: A Unified Perspective of World Models

    cs.RO 2026-04 unverdicted novelty 6.0

    The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and pro...

  18. AIM: Intent-Aware Unified world action Modeling with Spatial Value Maps

    cs.RO 2026-04 unverdicted novelty 6.0

    AIM predicts aligned spatial value maps inside a shared video-generation transformer to produce reliable robot actions, reaching 94% success on RoboTwin 2.0 with larger gains on long-horizon and contact-rich tasks.

  19. VAG: Dual-Stream Video-Action Generation for Embodied Data Synthesis

    cs.RO 2026-04 unverdicted novelty 6.0

    VAG is a synchronized dual-stream flow-matching framework that generates aligned video-action pairs for synthetic embodied data synthesis and policy pretraining.

  20. Video Generation Models as World Models: Efficient Paradigms, Architectures and Algorithms

    eess.IV 2026-03 unverdicted novelty 6.0

    Video generation models can function as world simulators if efficiency gaps in spatiotemporal modeling are bridged via organized paradigms, architectures, and algorithms.

  21. AttenA+: Rectifying Action Inequality in Robotic Foundation Models

    cs.RO 2026-05 unverdicted novelty 5.0

    AttenA+ applies velocity-driven action attention to reweight training objectives toward kinematically critical low-velocity segments, yielding small benchmark gains on Libero and RoboTwin without added parameters.

  22. Is the Future Compatible? Diagnosing Dynamic Consistency in World Action Models

    cs.RO 2026-05 unverdicted novelty 5.0

    Action-state consistency in World Action Models distinguishes successful from failed imagined futures and supports value-free selection of better rollouts via consensus among predictions.

  23. CKT-WAM: Parameter-Efficient Context Knowledge Transfer Between World Action Models

    cs.RO 2026-05 unverdicted novelty 5.0

    CKT-WAM transfers teacher WAM knowledge to students via compressed text-embedding contexts using LQCA and adapters, reaching 86.1% success on LIBERO-Plus with 1.17% trainable parameters and 83.3% in real-world tasks.

  24. World Action Models: The Next Frontier in Embodied AI

    cs.RO 2026-05 unverdicted novelty 4.0

    The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.

  25. World Model for Robot Learning: A Comprehensive Survey

    cs.RO 2026-04 unverdicted novelty 3.0

    A comprehensive survey that organizes the literature on world models in robot learning, their roles in policy learning, planning, simulation, and video-based generation, with connections to navigation, driving, datase...

Reference graph

Works this paper leans on

41 extracted references · 41 canonical work pages · cited by 23 Pith papers · 22 internal anchors

  1. [1]

    mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs

    Jonas Pai, Liam Achenbach, Victoriano Montesinos, Benedek Forrai, Oier Mees, and Elvis Nava. mimic-video: Video-action models for generalizable robot control beyond vlas.arXiv preprint 2512.15692, 2025

    work page internal anchor Pith review arXiv 2025

  2. [2]

    Video generators are robot policies.arXiv preprint arXiv:2508.00795, 2025

    Junbang Liang, Pavel Tokmakov, Ruoshi Liu, Sruthi Sudhakar, Paarth Shah, Rares Ambrus, and Carl V ondrick. Video generators are robot policies.arXiv preprint arXiv:2508.00795, 2025

    work page arXiv 2025

  3. [3]

    Causal World Modeling for Robot Control

    Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue, Xing Zhu, Yujun Shen, and Yinghao Xu. Causal world modeling for robot control. arXiv preprint arXiv:2601.21998, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  4. [4]

    World action models are zero-shot policies,

    Seonghyeon Ye, Yunhao Ge, Kaiyuan Zheng, Shenyuan Gao, Sihyun Yu, George Kurian, Suneel Indupuru, You Liang Tan, Chuning Zhu, Jiannan Xiang, Ayaan Malik, Kyungmin Lee, William Liang, Nadun Ranawaka, Jiasheng Gu, Yinzhen Xu, Guanzhi Wang, Fengyuan Hu, Avnish Narayan, Johan Bjorck, Jing Wang, Gwanghyun Kim, Dantong Niu, Ruijie Zheng, Yuqi Xie, Jimmy Wu, Qi ...

    work page

  5. [5]

    URLhttps://arxiv.org/abs/2602.15922

    work page internal anchor Pith review Pith/arXiv arXiv

  6. [6]

    Motus: A Unified Latent Action World Model

    Hongzhe Bi, Hengkai Tan, Shenghao Xie, Zeyuan Wang, Shuhe Huang, Haitian Liu, Ruowen Zhao, Yao Feng, Chendong Xiang, Yinze Rong, Hongyan Zhao, Hanyu Liu, Zhizhong Su, Lei Ma, Hang Su, and Jun Zhu. Motus: A unified latent action world model, 2025. URL https://arxiv.org/abs/2512.13030

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  7. [7]

    Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets

    Chuning Zhu, Raymond Yu, Siyuan Feng, Benjamin Burchfiel, Paarth Shah, and Abhishek Gupta. Unified world models: Coupling video and action diffusion for pretraining on large robotic datasets, 2025. URLhttps://arxiv.org/abs/2504.02792

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  8. [8]

    Vidar: Embodied video diffusion model for generalist manipulation.arXiv preprint arXiv:2507.12898, 2025

    Yao Feng, Hengkai Tan, Xinyi Mao, Chendong Xiang, Guodong Liu, Shuhe Huang, Hang Su, and Jun Zhu. Vidar: Embodied video diffusion model for generalist manipulation, 2025. URL https://arxiv.org/abs/2507.12898

    work page arXiv 2025

  9. [9]

    Tenenbaum, Dale Schuurmans, and Pieter Abbeel

    Yilun Du, Mengjiao Yang, Bo Dai, Hanjun Dai, Ofir Nachum, Joshua B. Tenenbaum, Dale Schuurmans, and Pieter Abbeel. Learning universal policies via text-guided video generation,

    work page

  10. [10]

    URLhttps://arxiv.org/abs/2302.00111

    work page arXiv

  11. [11]

    OpenVLA: An Open-Source Vision-Language-Action Model

    Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al. Openvla: An open-source vision-language-action model.arXiv preprint arXiv:2406.09246, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  12. [12]

    $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control

    Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, Lachy Groom, Karol Hausman, Brian Ichter, et al. π0: A vision-language-action flow model for general robot control.arXiv preprint arXiv:2410.24164, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  13. [13]

    $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization

    Physical Intelligence, Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Equi, Chelsea Finn, Niccolo Fusai, et al. pi0.5: a vision- language-action model with open-world generalization.arXiv preprint arXiv:2504.16054, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  14. [14]

    GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

    Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, et al. Gr00t n1: An open foundation model for generalist humanoid robots.arXiv preprint arXiv:2503.14734, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  15. [15]

    Rt-2: Vision-language-action models transfer web knowledge to robotic control

    Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, et al. Rt-2: Vision-language-action models transfer web knowledge to robotic control. InConference on Robot Learning, pages 2165–2183. PMLR, 2023

    work page 2023

  16. [16]

    RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation

    Songming Liu, Lingxuan Wu, Bangguo Li, Hengkai Tan, Huayu Chen, Zhengyi Wang, Ke Xu, Hang Su, and Jun Zhu. Rdt-1b: a diffusion foundation model for bimanual manipulation.arXiv preprint arXiv:2410.07864, 2024. 10

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  17. [17]

    AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems

    Qingwen Bu, Jisong Cai, Li Chen, Xiuqi Cui, Yan Ding, Siyuan Feng, Shenyuan Gao, Xindong He, Xuan Hu, Xu Huang, et al. Agibot world colosseo: A large-scale manipulation platform for scalable and intelligent embodied systems.arXiv preprint arXiv:2503.06669, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  18. [18]

    SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics

    Mustafa Shukor, Dana Aubakirova, Francesco Capuano, Pepijn Kooijmans, Steven Palma, Adil Zouitine, Michel Aractingi, Caroline Pascal, Martino Russi, Andres Marafioti, et al. Smolvla: A vision-language-action model for affordable and efficient robotics.arXiv preprint arXiv:2506.01844, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  19. [19]

    Gemini Robotics: Bringing AI into the Physical World

    Gemini Robotics Team, Saminda Abeyruwan, Joshua Ainslie, Jean-Baptiste Alayrac, Montser- rat Gonzalez Arenas, Travis Armstrong, Ashwin Balakrishna, Robert Baruch, Maria Bauza, Michiel Blokzijl, et al. Gemini robotics: Bringing ai into the physical world.arXiv preprint arXiv:2503.20020, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  20. [20]

    Galaxea open-world dataset and g0 dual-system vla model.arXiv preprint arXiv:2509.00576,

    Galaxea Team. Galaxea g0: Open-world dataset and dual-system vla model.arXiv preprint arXiv:2509.00576v1, 2025

    work page arXiv 2025

  21. [21]

    DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control

    Junjie Wen, Yichen Zhu, Jinming Li, Zhibin Tang, Chaomin Shen, and Feifei Feng. Dexvla: Vision-language model with plug-in diffusion expert for general robot control.arXiv preprint arXiv:2502.05855, 2025

    work page Pith review arXiv 2025

  22. [22]

    Unleashing large-scale video generative pre-training for visual robot manipulation, 2023

    Hongtao Wu, Ya Jing, Chilam Cheang, Guangzeng Chen, Jiafeng Xu, Xinghang Li, Minghuan Liu, Hang Li, and Tao Kong. Unleashing large-scale video generative pre-training for visual robot manipulation, 2023

    work page 2023

  23. [23]

    Ro- bodreamer: Learning compositional world models for robot imagination.arXiv preprint arXiv:2404.12377, 2024

    Siyuan Zhou, Yilun Du, Jiaben Chen, Yandong Li, Dit-Yan Yeung, and Chuang Gan. Ro- bodreamer: Learning compositional world models for robot imagination.arXiv preprint arXiv:2404.12377, 2024

    work page arXiv 2024

  24. [24]

    Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation

    Homanga Bharadhwaj, Debidatta Dwibedi, Abhinav Gupta, Shubham Tulsiani, Carl Doer- sch, Ted Xiao, Dhruv Shah, Fei Xia, Dorsa Sadigh, and Sean Kirmani. Gen2act: Human video generation in novel scenarios enables generalizable robot manipulation.arXiv preprint arXiv:2409.16283, 2024

    work page internal anchor Pith review arXiv 2024

  25. [25]

    Dual-stream diffusion for world-model augmented vision-language-action model, 2025

    John Won, Kyungmin Lee, Huiwon Jang, Dongyoung Kim, and Jinwoo Shin. Dual-stream diffusion for world-model augmented vision-language-action model, 2025. URL https: //arxiv.org/abs/2510.27607

    work page arXiv 2025

  26. [26]

    GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation

    Chi-Lam Cheang, Guangzeng Chen, Ya Jing, Tao Kong, Hang Li, Yifeng Li, Yuxiao Liu, Hongtao Wu, Jiafeng Xu, Yichu Yang, Hanbo Zhang, and Minzhao Zhu. Gr-2: A generative video-language-action model with web-scale knowledge for robot manipulation.arXiv preprint arXiv:2410.06158, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  27. [27]

    Dreamgen: Unlocking generalization in robot learning through video world models.arXiv preprint arXiv:2505.12705, 2025

    Joel Jang, Seonghyeon Ye, Zongyu Lin, Jiannan Xiang, Johan Bjorck, Yu Fang, Fengyuan Hu, Spencer Huang, Kaushil Kundalia, Yen-Chen Lin, Loic Magne, Ajay Mandlekar, Avnish Narayan, You Liang Tan, Guanzhi Wang, Jing Wang, Qi Wang, Yinzhen Xu, Xiaohui Zeng, Kaiyuan Zheng, Ruijie Zheng, Ming-Yu Liu, Luke Zettlemoyer, Dieter Fox, Jan Kautz, Scott Reed, Yuke Zh...

    work page arXiv 2025

  28. [28]

    CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models, March 2025

    Qingqing Zhao, Yao Lu, Moo Jin Kim, Zipeng Fu, Zhuoyang Zhang, Yecheng Wu, Zhaoshuo Li, Qianli Ma, Song Han, Chelsea Finn, Ankur Handa, Ming-Yu Liu, Donglai Xiang, Gordon Wetzstein, and Tsung-Yi Lin. Cot-vla: Visual chain-of-thought reasoning for vision-language- action models, 2025. URLhttps://arxiv.org/abs/2503.22020

    work page arXiv 2025

  29. [29]

    Rynnvla-002: A unified vision-language-action and world model.arXiv preprint arXiv:2511.17502, 2025

    Jun Cen, Siteng Huang, Yuqian Yuan, Kehan Li, Hangjie Yuan, Chaohui Yu, Yuming Jiang, Jiayan Guo, Xin Li, Hao Luo, Fan Wang, Fan Wang, and Deli Zhao. Rynnvla-002: A unified vision-language-action and world model.arXiv preprint arXiv:2511.17502, 2025

    work page arXiv 2025

  30. [30]

    Worldvla: Towards autoregressive action world model.arXiv preprint arXiv:, 2025

    Jun Cen, Chaohui Yu, Hangjie Yuan, Yuming Jiang, Siteng Huang, Jiayan Guo, Xin Li, Yibing Song, Hao Luo, Fan Wang, Deli Zhao, and Hao Chen. Worldvla: Towards autoregressive action world model.arXiv preprint arXiv:, 2025. 11

    work page 2025

  31. [31]

    Act2goal: From world model to general goal-conditioned policy, 2025

    Pengfei Zhou, Liliang Chen, Shengcong Chen, Di Chen, Wenzhi Zhao, Rongjun Jin, Guanghui Ren, and Jianlan Luo. Act2goal: From world model to general goal-conditioned policy, 2025. URLhttps://arxiv.org/abs/2512.23541

    work page arXiv 2025

  32. [32]

    Flare: Robot learning with implicit world modeling, 2025

    Ruijie Zheng, Jing Wang, Scott Reed, Johan Bjorck, Yu Fang, Fengyuan Hu, Joel Jang, Kaushil Kundalia, Zongyu Lin, Loic Magne, Avnish Narayan, You Liang Tan, Guanzhi Wang, Qi Wang, Jiannan Xiang, Yinzhen Xu, Seonghyeon Ye, Jan Kautz, Furong Huang, Yuke Zhu, and Linxi Fan. Flare: Robot learning with implicit world modeling, 2025. URL https://arxiv. org/abs/...

    work page arXiv 2025

  33. [33]

    arXiv preprint arXiv:2507.04447 (2025) 3, 7, 14

    Wenyao Zhang, Hongsi Liu, Zekun Qi, Yunan Wang, Xinqiang Yu, Jiazhao Zhang, Runpei Dong, Jiawei He, He Wang, Zhizheng Zhang, Li Yi, Wenjun Zeng, and Xin Jin. Dreamvla: A vision- language-action model dreamed with comprehensive world knowledge.CoRR, abs/2507.04447,

    work page arXiv

  34. [34]

    Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

    doi: 10.48550/ARXIV .2507.04447. URLhttps://doi.org/10.48550/arXiv. 2507.04447

    work page internal anchor Pith review doi:10.48550/arxiv

  35. [35]

    Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning

    Moo Jin Kim, Yihuai Gao, Tsung-Yi Lin, Yen-Chen Lin, Yunhao Ge, Grace Lam, Percy Liang, Shuran Song, Ming-Yu Liu, Chelsea Finn, and Jinwei Gu. Cosmos policy: Fine-tuning video models for visuomotor control and planning.arXiv preprint arXiv:2601.16163, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  36. [36]

    arXiv preprint arXiv:2508.05635 (2025)

    Yue Liao, Pengfei Zhou, Siyuan Huang, Donglin Yang, Shengcong Chen, Yuxin Jiang, Yue Hu, Jingbin Cai, Si Liu, Jianlan Luo, Liliang Chen, Shuicheng Yan, Maoqing Yao, and Guanghui Ren. Genie envisioner: A unified world foundation platform for robotic manipulation.arXiv preprint arXiv:2508.05635, 2025

    work page arXiv 2025

  37. [37]

    Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations

    Yucheng Hu, Yanjiang Guo, Pengchao Wang, Xiaoyu Chen, Yen-Jen Wang, Jianke Zhang, Koushil Sreenath, Chaochao Lu, and Jianyu Chen. Video prediction policy: A generalist robot policy with predictive visual representations.arXiv preprint arXiv:2412.14803, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  38. [38]

    Unified Video Action Model

    Shuang Li, Yihuai Gao, Dorsa Sadigh, and Shuran Song. Unified video action model.arXiv preprint arXiv:2503.00200, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  39. [39]

    Wan: Open and Advanced Large-Scale Video Generative Models

    Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Ningyi Zhang, Pandeng Li, Pingyu Wu, Ruihang Chu, Ruili Feng, Shiwei Zhang, Siyang Sun, Tao Fang, T...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  40. [40]

    LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning

    Bo Liu, Yifeng Zhu, Chongkai Gao, Yihao Feng, Qiang Liu, Yuke Zhu, and Peter Stone. Libero: Benchmarking knowledge transfer for lifelong robot learning.arXiv preprint arXiv:2306.03310, 2023

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  41. [41]

    RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation

    Tianxing Chen, Zanxin Chen, Baijun Chen, Zijian Cai, Yibin Liu, Zixuan Li, Qiwei Liang, Xianliang Lin, Yiheng Ge, Zhenyu Gu, et al. Robotwin 2.0: A scalable data generator and benchmark with strong domain randomization for robust bimanual robotic manipulation.arXiv preprint arXiv:2506.18088, 2025. A Appendix A.1 RoboTwin Detailed Results Here we present t...

    work page internal anchor Pith review Pith/arXiv arXiv 2025