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REVIEW 3 major objections 4 minor 55 references

DreamWAM: Beyond RGB Future Prediction for World Action Models

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read World action models should predict the future in motion, geometry, and semantics during training—not just RGB—and this structured dreaming improves robustness to visual change without altering deployment.

desk verdict Well-matched empirical study; central claim likely holds, but the motion target's fidelity is an unvalidated proxy that should be checked before the mechanism is taken at face value. read the letter →

arxiv 2608.04996 v1 pith:UK4X442V submitted 2026-08-05 cs.RO

Shanglin Yuan , Weiheng Zhao , Xin Shi , Haoyi Jiang , Xianda Guo , Liu Liu , Wenyu Liu , Wei Sui
show 1 more author
Xinggang Wang
This is my paper
classification cs.RO
keywords worldactionmodelsfuturepredictionopticalflowgatedresidualbranchesroboticmanipulationrobustnessvisualdistributionshiftvideodiffusiontransformermulti-viewsupervision
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper argues that a world action model—a policy that learns to act by predicting how the observed world will evolve—should be trained to imagine that future in several complementary forms at once: appearance, motion, geometry, and semantics, rather than pixels alone. The proposed method, DreamWAM, adds these extra views only during training and removes them at deployment, so at run time the robot still takes in RGB and outputs actions exactly like the baseline it improves on. The claim is that this change makes the learned representation preserve the state transitions that actually determine task success, which matters most when the visual scene shifts. Supporting evidence: average success on LIBERO-Plus perturbations rises from 69.16% to 75.47% with joint inference, and real-world success under unseen lighting, background, and layout changes rises from 55.6% to 74.4%.

What carries the argument

Two mechanisms carry the argument. First, RGB video latents and RAFT optical-flow latents are jointly denoised in a shared VideoDiT stream: they are perturbed at the same flow-matching timestep, concatenated along the channel dimension, and predicted together, which makes temporal change an explicit part of the learned future. Second, geometry and semantic features from Depth Anything V3 and DINOv2 are injected through lightweight gated residual branches that add a gated correction to selected VideoDiT layers instead of replacing the native video state, with a prediction head that regresses those aligned features during training. Shared attention between VideoDiT and ActionDiT lets the action branch be shaped by these structured future representations. At inference, motion channels are zero-filled and all non-RGB branches are removed, so nothing changes at deployment except the weights.

What would settle it

Train DreamWAM with the motion target replaced by color-coded static frames or noise that goes through the identical VAE pipeline; if LIBERO-Plus success stays near 75.47%, the motion view is not what carries the gain. A second check: compare against a variant that supervises raw flow vectors in a learned latent space instead of color-coded flow, and see which version keeps the perturbation gains.

Watch

Extended reading notes

Core claim

The paper's central claim is that robust world-action learning depends not only on predicting the future, but on representing it in a form that matters for action. Concretely, it claims that training a VideoDiT-ActionDiT world action model with four future views—RGB, optical flow, depth, and DINOv2 semantic features—reshapes the shared video-action representation so that the policy is more robust to visual distribution shifts, while the extra supervision branches can be switched off at test time with the deployment interface unchanged. The finding is stated in matched comparisons: under unseen LIBERO-Plus perturbations, the no-rollout policy improves from 51.36% to 63.44% and the joint video-action variant from 69.16% to 75.47%, with gains on all seven perturbation dimensions; on a real robot, average success under unseen visual perturbations improves from 55.6% to 74.4%.

Load-bearing premise

The measured gains depend on the motion view, which is built by converting RAFT optical flow into color-coded RGB pictures and encoding those pictures with the Wan2.2 video VAE; if that lossy pipeline does not preserve direction or magnitude of motion, then 'motion' supervision is not the mechanism producing the improvement.

Editorial extensions

If this is right

  • Improvements across no-rollout and joint inference modes, with a smaller no-rollout-to-joint gap (12.02 versus 17.80 percent), suggest structured supervision internalizes part of the action-relevant future that otherwise would need online generation.
  • The all-denoise control (D/D/D) falling below the RGB-only baseline indicates that supervision quantity alone does not help; the route by which each view enters the pretrained video pathway matters.
  • The ablation identifying motion as the most consequential view, with geometry and semantics underperforming when motion is removed, positions motion as a temporal scaffold for the feature-level signals.
  • Because the deployment interface is unchanged, the gains are additive to any future improvement in the underlying video backbone or action decoder.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the mechanism is right, the same training-time structured supervision could be applied with richer targets—metric 3D scene flow, contact maps, or object-centric trajectories—and the residual-branch design suggests a recipe for adding heterogeneous targets to any pretrained video diffusion policy.
  • The results leave open whether the gains stem from the specific semantic content of each view or from the extra temporal coherence the combined objectives impose; a control with random-but-structured targets, such as color-coded noise with the same statistics, would separate these explanations.
  • A practical extension the paper does not test is keeping the motion branch active at test time for partially observed futures, or using the geometry and semantics branches as test-time conditioning, which might push robustness further than the RGB-only deployment they chose.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper introduces DreamWAM, a world action model that supplements RGB future prediction with training-time supervision from motion, geometry, and semantic views. RGB and optical-flow latents are jointly denoised, while depth and DINOv2 features enter through gated residual branches; at inference all beyond-RGB pathways are disabled, so deployment is RGB-only. The authors report consistent gains over matched Fast-WAM baselines on LIBERO, larger gains on the unseen LIBERO-Plus perturbations, and improvements on a real-robot evaluation under visual perturbations. They also present ablations on which views help and on how each view should be injected (full denoising vs. gated residual).

Significance. If the central claim holds, the paper makes a useful contribution: it identifies the representation of the predicted future, not just the act of prediction, as a design variable in world action models, and it provides a concrete architecture that uses structured future supervision while retaining an RGB-only deployment interface. The evaluation is well controlled relative to many prior WAM papers: the Fast-WAM-Joint baseline shares backbone, data, and training protocol, gains appear across all seven LIBERO-Plus perturbation dimensions, and the real-robot experiments test true distribution shifts. The public release of code and models is a further strength. However, the load-bearing mechanism—that the motion view is actually providing motion information—is not validated, and the empirical claims lack uncertainty quantification. The paper is therefore promising but needs additional evidence before the central claim can be accepted.

major comments (3)
  1. [Appendix A.1, Eq. (5); Table 3] The motion view is identified in the ablation as the most consequential single view, yet the paper never validates that the Wan2.2 VAE encoding of color-coded RAFT flow preserves the flow direction or magnitude. The VAE is trained for natural RGB video, and color-coded flow visualizations are far out of distribution; Appendix A.1 explicitly states that z_flow is the VAE encoding of a visualized motion sequence rather than raw metric flow. If the VAE treats the color code as texture, the "motion" objective is not a temporal-dynamics target and the central claim that the form of the future representation matters would not be supported by the motion ablation. I request a concrete fidelity check: decode z_flow and compare it to the original flow visualization or to raw flow fields using an average endpoint error or correlation metric, and also report a control where the motion target is replaced by an appearance-matched but temporally meaningless target (or where a different flow color map is used). Without such evidence, the motion attribution in Table 3 is not established.
  2. [Experimental Setup; Tables 1 and 2; Figure 3] All simulation results average only two random seeds, and no confidence intervals, standard deviations, or significance tests are reported. This is particularly important for the LIBERO results, where the claimed gains are small (97.30 to 98.40 and 98.00 to 98.90) and the per-task rollout count is only 50 per seed. The LIBERO-Plus gains are larger, but a formal comparison (e.g., bootstrap confidence intervals over tasks or per-seed results) is needed to rule out seed noise. The real-robot results are based on 30 trials per setting and are also reported as point estimates; error bars or confidence intervals should be added.
  3. [Experimental Setup; Eqs. (8)-(10)] The claim that Fast-WAM-Joint and DreamWAM are "matched" is not exact: DreamWAM adds 48 flow input channels to the VideoDiT input projection and includes residual branches and prediction heads during training, so the parameter count and the training-time update dynamics differ from the baseline. Although the flow channels are zeroed at inference, the corresponding projection weights are trained, so gains could in principle come from extra capacity rather than from the structured form of the supervision. Please report parameter counts for the matched pairs, and consider an additional control that uses the same extra channels with a non-informative target (e.g., random noise or a static flow-like signal) to isolate the effect of the motion content.
minor comments (4)
  1. [Appendix A.1] The label "DA3geometrylatent" should read "DA3 geometry latent"; also clarify whether the duplicated first transition in the flow video is the first flow field or the first transition, as the current sentence is slightly ambiguous.
  2. [Table 1] The method name "LingBot-VA (2026b)" is inconsistent with the reference list, which cites "A Pragmatic VLA Foundation Model" by Wu et al.; please verify the citation key and use the correct model name (LingBot-VLA) throughout.
  3. [Figure 3] Figure 3 shows success rates without any uncertainty indication; adding error bars or per-trial counts would make the real-robot comparison more interpretable.
  4. [Method, Eq. (2)] The notation "ConcatC" is used but not defined; please define it as concatenation along the channel dimension, and state the channel counts of x_rgb and x_mot explicitly for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central claim rests on matched evaluations against external baselines, and the auxiliary targets are training-time supervisions rather than fitted inputs renamed as predictions.

full rationale

The paper's central claim is that structured future supervision (appearance, motion, geometry, semantics) improves action robustness even though deployment remains RGB-only. This claim is supported by controlled comparisons on LIBERO, LIBERO-Plus, and real-robot perturbations, with the beyond-RGB targets derived offline from future frames in the training set. The targets are not fitted parameters that are later renamed as predictions; they are auxiliary objectives whose effect is measured by ablations. The D/R/R routing choice is justified by the paper's own ablations, but that is model selection rather than circularity: the ablations vary the route while holding the targets fixed, and the central evaluation is on unseen perturbations not used for training. The Fast-WAM backbone is a published baseline that is matched across variants, and no uniqueness theorem or load-bearing self-citation is invoked to force the design. The skeptic's concern that VAE encoding of color-coded flow might make the motion target an appearance proxy is a validity or mechanism risk, not circularity, because the target is still derived from optical flow and the reported gains are measured on held-out distribution shifts. The derivation chain therefore does not reduce to its own inputs, and no specific equation-level circular step can be identified.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

The ledger contains no invented physical entities. The main free parameters are the loss weights and the dimensional choices for the auxiliary targets, none of which are reported with sensitivity analysis. The axioms are the assumptions that compressed flow, depth, and DINO features are useful supervision signals and that the training-time benefit survives the RGB-only inference recipe.

free parameters (5)
  • loss weights lambda_rgb, lambda_mot, lambda_geo, lambda_sem, lambda_act, lambda_g = not reported
    Equation (4) defines the total loss as a weighted sum; the individual weights are not stated in the text and are presumably tuned by hand. They directly control the balance between RGB, motion, geometry, semantic, action, and gate terms.
  • PCA rank for depth and DINO targets = 8
    Appendix A.1 projects DA3 depth features and DINOv2 patch features to 8 channels using a rank-8 PCA basis. The rank is chosen by hand and is not varied in any sensitivity study.
  • auxiliary channel counts = 48 flow channels, 8 depth channels, 8 DINO channels
    The motion latent z_flow has 48 channels, while depth and DINO targets have 8 channels each. These dimensions are fixed design choices and could affect how much information the residual branches and denoising stream carry.
  • temporal aggregation window = 9 frames to 3 latent steps
    Appendix A.1 retains the first frame, averages frames 2 through 5, and averages frames 6 through 9 to align features with the video latent timeline. The aggregation scheme is chosen by hand and not ablated.
  • training hyperparameters = learning rate 1e-5, batch size 16, 5 real-robot epochs, 32-step action policy, 10 denoising steps
    These values are specified in the Experiments and Appendix B. They are inherited from or matched to Fast-WAM and are not varied in the paper.
assumptions (6)
  • domain assumption RAFT optical flow, converted to color-coded visualizations and VAE-encoded, preserves action-relevant motion information.
    Appendix A.1 defines the motion target as the VAE encoding of color-coded RAFT flow. If this lossy visualization drops direction or magnitude information, the motion supervision term in Eq. (4) is weakened.
  • domain assumption Depth Anything V3 and DINOv2 features, compressed via PCA to 8 channels, capture geometry and semantics relevant to manipulation.
    Appendix A.1 uses these frozen encoders to build f_geo and f_sem. The paper does not validate the compressed features independently, only through downstream task success.
  • domain assumption The Wan2.2 video VAE latent space can host flow visualizations without catastrophic domain shift.
    Motion latents are encoded by the same VAE as RGB (Appendix A.1). The paper does not analyze reconstruction fidelity of flow videos, so the joint denoising relies on this transfer working.
  • domain assumption LIBERO-Plus perturbations preserve task semantics and success criteria, so gains reflect robustness rather than adaptation to new tasks.
    The experiments state that no LIBERO-Plus data are used for training and that task semantics remain unchanged. This is a property of the benchmark, not something the paper proves internally.
  • domain assumption Shared attention between VideoDiT and ActionDiT transfers the multi-view future representation to action denoising.
    The World-Action Coupling section assumes that exposing ActionDiT to the VideoDiT representation during training is what carries the benefit. The paper does not provide a causal intervention to isolate this pathway.
  • standard math Standard flow-matching and diffusion denoising assumptions hold for the joint RGB-motion latent and action trajectories.
    The method uses flow matching (Lipman et al. 2022) as the generative objective; the paper relies on its standard convergence and sampling properties without re-deriving them.

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Pith. "Pith review of DreamWAM: Beyond RGB Future Prediction for World Action Models." pith.science (2026). https://pith.science/paper/UK4X442V

@misc{pith2026260804996,
  author       = {Pith},
  title        = {Pith review of: DreamWAM: Beyond RGB Future Prediction for World Action Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UK4X442V}},
  note         = {Machine review of arXiv:2608.04996}
}
read the original abstract

World Action Models (WAMs) learn action-relevant representations by predicting how the observed world will evolve. Most existing WAMs define this future in RGB space, where task-relevant state transitions are entangled with nuisance variations in texture, illumination, background, and viewpoint. We argue that WAMs should explicitly predict action-relevant future state rather than relying on RGB prediction alone. We introduce DreamWAM, which reformulates future prediction as structured world modeling beyond RGB, representing future states through complementary views of appearance, motion, geometry, and semantics. During training, DreamWAM combines joint latent denoising of RGB and motion with lightweight gated residual branches for geometry and semantics. Shared attention between VideoDiT and ActionDiT allows the action branch to learn from these future-state predictions, while all beyond-RGB supervision branches are disabled at inference and deployment remains RGB-only. Across both no-rollout and joint video-action inference, DreamWAM consistently improves the matched RGB-only baselines on LIBERO, from 97.30\% to 98.40\% and from 98.00\% to 98.90\%, respectively. The gains become larger under unseen LIBERO-Plus perturbations, from 51.36\% to 63.44\% and from 69.16\% to 75.47\%. The same robustness extends to real-world manipulation, where DreamWAM attains an average success rate of 74.4\% across unseen changes in lighting, background, and object layout, compared with 55.6\% for Fast-WAM-Joint. These results show that robust world-action learning depends not only on predicting the future, but on representing it in a form that matters for action. The code and models are publicly released at https://github.com/hustvl/DreamWAM.

Figures

Figures reproduced from arXiv: 2608.04996 by the authors.

Figure 1
Figure 1. Dreaming beyond RGB. RGB-only WAMs organize future learning around video appearance, whereas DreamWAM additionally learns motion, geometry, and semantic views of future state. Representative LIBERO rollouts under the same instruction show that the RGB-only baseline succeeds in the original scene but fails under unseen background, dimmed lighting, and layout changes, while DreamWAM completes the task across all four … view at source ↗
Figure 2
Figure 2. Overview of DreamWAM. (a) During training, DreamWAM augments RGB future prediction with motion, geometry, [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Real-world robot evaluation. Top: success rates (%) on four standard tasks and three unseen visual perturbations, with 30 trials per setting. Bottom: representative task and perturbation observations. The perturbations retain the Strawberry Selection instruction and success criterion. The rightmost panel shows a Fast-WAM-Joint failure caused by an inaccurate gripper-to-plate spatial relation. residual injection. The… view at source ↗

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Works this paper leans on

55 extracted references · 4 canonical work pages

  1. [1]

    arXiv preprint arXiv:2603.16666 , year=

    Fast-wam: Do world action models need test-time future imagination? , author=. arXiv preprint arXiv:2603.16666 , year=

    arXiv

  2. [2]

    arXiv preprint arXiv:2506.21539 , year=

    Worldvla: Towards autoregressive action world model , author=. arXiv preprint arXiv:2506.21539 , year=

    arXiv

  3. [3]

    arXiv preprint arXiv:2512.13030 , year=

    Motus: A unified latent action world model , author=. arXiv preprint arXiv:2512.13030 , year=

    arXiv

  4. [4]

    arXiv preprint arXiv:2604.26694 , year=

    Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising , author=. arXiv preprint arXiv:2604.26694 , year=

    arXiv

  5. [5]

    arXiv preprint arXiv:2601.21998 , year=

    Causal World Modeling for Robot Control , author=. arXiv preprint arXiv:2601.21998 , year=

    arXiv

  6. [6]

    arXiv preprint arXiv:2607.13960 , year=

    GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch , author=. arXiv preprint arXiv:2607.13960 , year=

    arXiv

  7. [7]

    arXiv preprint arXiv:2510.12796 , year=

    DriveVLA-W0: World models amplify data scaling law in autonomous driving , author=. arXiv preprint arXiv:2510.12796 , year=

    arXiv

  8. [8]

    arXiv preprint arXiv:2605.15178 , year=

    Sana-wm: Efficient minute-scale world modeling with hybrid linear diffusion transformer , author=. arXiv preprint arXiv:2605.15178 , year=

    arXiv

Show all 55 references
  1. [9]

    arXiv preprint arXiv:2504.02792 , year=

    Unified world models: Coupling video and action diffusion for pretraining on large robotic datasets , author=. arXiv preprint arXiv:2504.02792 , year=

    arXiv

  2. [10]

    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

    Visual point cloud forecasting enables scalable autonomous driving , author=. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

  3. [11]

    arXiv preprint arXiv:2212.06817 , year=

    Rt-1: Robotics transformer for real-world control at scale , author=. arXiv preprint arXiv:2212.06817 , year=

    arXiv

  4. [12]

    Conference on Robot Learning , pages=

    Rt-2: Vision-language-action models transfer web knowledge to robotic control , author=. Conference on Robot Learning , pages=. 2023 , organization=

    2023

  5. [13]

    arXiv preprint arXiv:2405.12213 , year=

    Octo: An open-source generalist robot policy , author=. arXiv preprint arXiv:2405.12213 , year=

    arXiv

  6. [14]

    arXiv preprint arXiv:2606.08288 , year=

    MotionVLA: Injecting Geometric Motion into Vision-Language-Action Model , author=. arXiv preprint arXiv:2606.08288 , year=

    arXiv

  7. [15]

    arXiv preprint arXiv:2406.09246 , year=

    Openvla: An open-source vision-language-action model , author=. arXiv preprint arXiv:2406.09246 , year=

    arXiv

  8. [16]

    arXiv preprint arXiv:2505.06111 , year=

    Univla: Learning to act anywhere with task-centric latent actions , author=. arXiv preprint arXiv:2505.06111 , year=

    arXiv

  9. [17]

    arXiv preprint arXiv:2502.19645 , year=

    Fine-tuning vision-language-action models: Optimizing speed and success , author=. arXiv preprint arXiv:2502.19645 , year=

    arXiv

  10. [18]

    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 , journal=

  11. [19]

    arXiv preprint arXiv:2504.16054 , year=

    arXiv

  12. [20]

    arXiv preprint arXiv:2501.09747 , year=

    Fast: Efficient action tokenization for vision-language-action models , author=. arXiv preprint arXiv:2501.09747 , year=

    arXiv

  13. [21]

    Amin, Ali and Aniceto, Raichelle and Balakrishna, Ashwin and Black, Kevin and Conley, Ken and Connors, Grace and Darpinian, James and Dhabalia, Karan and DiCarlo, Jared and others , journal=

  14. [22]

    arXiv preprint arXiv:2604.15483 , year=

    arXiv

  15. [23]

    Advances in Neural Information Processing Systems , volume=

    Dreamvla: a vision-language-action model dreamed with comprehensive world knowledge , author=. Advances in Neural Information Processing Systems , volume=

  16. [24]

    arXiv preprint arXiv:2510.12276 , year=

    Spatial forcing: Implicit spatial representation alignment for vision-language-action model , author=. arXiv preprint arXiv:2510.12276 , year=

  17. [25]

    arXiv preprint arXiv:2510.13375 , year=

    Depthvla: Enhancing vision-language-action models with depth-aware spatial reasoning , author=. arXiv preprint arXiv:2510.13375 , year=

  18. [26]

    arXiv preprint arXiv:2501.15830 , year=

    Spatialvla: Exploring spatial representations for visual-language-action model , author=. arXiv preprint arXiv:2501.15830 , year=

    arXiv

  19. [27]

    arXiv preprint arXiv:2508.09071 , year=

    Geovla: Empowering 3d representations in vision-language-action models , author=. arXiv preprint arXiv:2508.09071 , year=

    arXiv

  20. [28]

    IEEE Robotics and Automation Letters , volume=

    Pointvla: Injecting the 3d world into vision-language-action models , author=. IEEE Robotics and Automation Letters , volume=. 2026 , publisher=

    2026

  21. [29]

    arXiv preprint arXiv:2603.00466 , year=

    DreamWorld: Unified World Modeling in Video Generation , author=. arXiv preprint arXiv:2603.00466 , year=

  22. [30]

    arXiv preprint arXiv:2502.02492 , year=

    Videojam: Joint appearance-motion representations for enhanced motion generation in video models , author=. arXiv preprint arXiv:2502.02492 , year=

    arXiv

  23. [31]

    Advances in Neural Information Processing Systems , volume=

    Videorepa: Learning physics for video generation through relational alignment with foundation models , author=. Advances in Neural Information Processing Systems , volume=

  24. [32]

    arXiv preprint arXiv:2602.15922 , year=

    World action models are zero-shot policies , author=. arXiv preprint arXiv:2602.15922 , year=

    arXiv

  25. [33]

    arXiv preprint arXiv:2601.16163 , year=

    Cosmos policy: Fine-tuning video models for visuomotor control and planning , author=. arXiv preprint arXiv:2601.16163 , year=

    arXiv

  26. [34]

    arXiv preprint arXiv:2503.14734 , year=

    Gr00t n1: An open foundation model for generalist humanoid robots , author=. arXiv preprint arXiv:2503.14734 , year=

    arXiv

  27. [35]

    arXiv preprint arXiv:2510.10274 , year=

    X-vla: Soft-prompted transformer as scalable cross-embodiment vision-language-action model , author=. arXiv preprint arXiv:2510.10274 , year=

    arXiv

  28. [36]

    arXiv preprint arXiv:2601.03782 , year=

    PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation , author=. arXiv preprint arXiv:2601.03782 , year=

  29. [37]

    arXiv preprint arXiv:2411.19650 , year=

    Cogact: A foundational vision-language-action model for synergizing cognition and action in robotic manipulation , author=. arXiv preprint arXiv:2411.19650 , year=

    arXiv

  30. [38]

    arXiv preprint arXiv:2506.01844 , year=

    Smolvla: A vision-language-action model for affordable and efficient robotics , author=. arXiv preprint arXiv:2506.01844 , year=

    arXiv

  31. [39]

    arXiv preprint arXiv:2602.09849 , year=

    Bagelvla: Enhancing long-horizon manipulation via interleaved vision-language-action generation , author=. arXiv preprint arXiv:2602.09849 , year=

  32. [40]

    arXiv preprint arXiv:2601.18692 , year=

    A Pragmatic VLA Foundation Model , author=. arXiv preprint arXiv:2601.18692 , year=

    arXiv

  33. [41]

    arXiv preprint arXiv:2605.30280 , year=

    Qwen-vla: Unifying vision-language-action modeling across tasks, environments, and robot embodiments , author=. arXiv preprint arXiv:2605.30280 , year=

    arXiv

  34. [42]

    Proceedings of the AAAI Conference on Artificial Intelligence , volume=

    Reconvla: Reconstructive vision-language-action model as effective robot perceiver , author=. Proceedings of the AAAI Conference on Artificial Intelligence , volume=

  35. [43]

    Advances in neural information processing systems , volume=

    Learning universal policies via text-guided video generation , author=. Advances in neural information processing systems , volume=

  36. [44]

    arXiv preprint arXiv:2602.10098 , year=

    Vla-jepa: Enhancing vision-language-action model with latent world model , author=. arXiv preprint arXiv:2602.10098 , year=

  37. [45]

    arXiv preprint arXiv:2604.14732 , year=

    World-value-action model: Implicit planning for vision-language-action systems , author=. arXiv preprint arXiv:2604.14732 , year=

    arXiv

  38. [46]

    Advances in Neural Information Processing Systems , volume=

    Bridgevla: Input-output alignment for efficient 3d manipulation learning with vision-language models , author=. Advances in Neural Information Processing Systems , volume=

  39. [47]

    Advances in Neural Information Processing Systems , volume=

    Libero: Benchmarking knowledge transfer for lifelong robot learning , author=. Advances in Neural Information Processing Systems , volume=

  40. [48]

    arXiv preprint arXiv:2510.13626 , year=

    Libero-plus: In-depth robustness analysis of vision-language-action models , author=. arXiv preprint arXiv:2510.13626 , year=

    arXiv

  41. [49]

    European conference on computer vision , pages=

    Raft: Recurrent all-pairs field transforms for optical flow , author=. European conference on computer vision , pages=. 2020 , organization=

    2020

  42. [50]

    Transactions on Machine Learning Research Journal , year=

    Dinov2: Learning robust visual features without supervision , author=. Transactions on Machine Learning Research Journal , year=

  43. [51]

    arXiv preprint arXiv:2511.10647 , year=

    Depth anything 3: Recovering the visual space from any views , author=. arXiv preprint arXiv:2511.10647 , year=

    arXiv

  44. [52]

    The eleventh international conference on learning representations , year=

    Flow matching for generative modeling , author=. The eleventh international conference on learning representations , year=

  45. [53]

    arXiv preprint arXiv:2503.20314 , year=

    Wan: Open and Advanced Large-Scale Video Generative Models , author=. arXiv preprint arXiv:2503.20314 , year=

    arXiv

  46. [54]

    arXiv preprint arXiv:2410.06940 , year=

    Representation alignment for generation: Training diffusion transformers is easier than you think , author=. arXiv preprint arXiv:2410.06940 , year=

    arXiv

  47. [55]

    generation: Taming optimization dilemma in latent diffusion models , author=

    Reconstruction vs. generation: Taming optimization dilemma in latent diffusion models , author=. Proceedings of the Computer Vision and Pattern Recognition Conference , pages=

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Reviewed August 6, 2026 · model on record in the stance chip above.