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REVIEW 3 major objections 5 minor 28 references

SG-WAM: Text-Grounded and Spatial-aware Semantic Guidance for World-Action Models

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read A VLM planner's text-grounded and spatial-aware semantic foresight steers world-action models to follow instructions, reaching 98.7% average success on LIBERO.

desk verdict A clean, well-specified method for injecting VLM-predicted semantic features into a world-action model, but the 'future foresight' claim is underevidenced and the headline LIBERO margin is too thin to call SOTA without error bars. read the letter →

arxiv 2608.08839 v1 pith:6YPC47WC submitted 2026-08-09 cs.RO cs.CV

Junjie He , Junfeng Li , Zhide Zhong , Haodong Yan , Ruixin Li , Yangyang Zheng , Jiaguan Zhu , Tianran Zhang
show 4 more authors
Yuqiao Du Wen Chen Shunbo Zhou Haoang Li
This is my paper · ORCID
classification cs.ROcs.CV
keywords world-actionmodelssemanticforesightvision-languagemodelplannerinstructiongroundingspatial-awareguidancerobotmanipulationLIBERObenchmarkfeaturealignment
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

World-action models generate future video and actions jointly, but most condition on text embeddings computed independently of the scene, so the generated behavior drifts from the instruction. SG-WAM proposes inserting a vision-language model as a semantic planner that, from the current frame and instruction alone, predicts dense feature maps of what the future should look like: one map aligned with language-grounded image features and one with geometric depth features. These maps are injected into a pretrained video diffusion world model through cross-attention, and the action decoder inherits them through joint attention, so both the imagined video and the executed actions follow the instruction. The paper reports state-of-the-art results on LIBERO (98.7% average success), the highest robustness on LIBERO-Plus perturbed tasks (81.3%), and successful real-robot transfer, with the largest gains when instructed objects must be selected among distractors or handled in a commanded order.

What carries the argument

The load-bearing mechanism is a query-token VLM planner. Base query tokens plus two modality-specific groups predict K=4 future keyframes at evenly spaced offsets in a single forward pass; a resampler cross-attends to query states and image states to produce 256 tokens in each of two feature spaces. The text-grounded target comes from penultimate-layer patch tokens of SigLIP2 (a language-aligned dense visual encoder), and the spatial-aware target from dense features of Depth Anything 3 (a dense geometry encoder), with both detached and used as teacher targets under an MSE/smooth-L1 loss. The two predicted maps are projected and gated into a single guidance, injected into every video-expert block via parallel cross-attention while retaining original text conditioning, and the action expert receives guidance through joint attention with the video expert. Three-stage training first pre-trains the planner, then co-trains it with the video expert, then adds the action expert, always conditioning on the planner's own predictions, which matches deployment.

What would settle it

An experiment that would settle the claim: build a LIBERO variant where the target object named in the instruction is occluded at time t0 and only becomes visible in the future keyframes; if SG-WAM's success does not drop toward the no-guidance baseline, the planner is not actually anticipating the future from the scene, and the gains come from something else (e.g., the instruction alone or static appearance cues).

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Extended reading notes

Core claim

The central claim is that a VLM planner can translate a language instruction and the current observation into a semantic foresight—text-grounded and spatial-aware feature maps of future keyframes—and that injecting this foresight into a world-action model makes both future-video generation and action prediction faithfully follow the instruction. On LIBERO the full model reaches 98.7% average success, edging out LingBot-VA (98.5%); on LIBERO-Plus it tops all baselines with 81.3% average under seven perturbation types; and on a real dual-arm platform it beats GE-Act and FastWAM on all four tasks plus two generalization settings. Ablations show the largest drop when semantic guidance is removed entirely (97.2%), and smaller drops when either the text-grounded or spatial-aware branch is removed.

Load-bearing premise

The whole method stands on the premise that the feature maps of future keyframes extracted by frozen SigLIP2 and Depth Anything 3 are the right and sufficient supervision: a single VLM forward pass over the current frame and instruction can anticipate exactly those features, and they contain the instruction-relevant object identity and geometry needed for the task.

Editorial extensions

If this is right

  • If SG-WAM is right, world-action models can be steered by language without retraining the video backbone from scratch: a pretrained video diffusion model can be conditioned by injected semantic foresight while keeping its generative priors.
  • The single-forward-pass planner means no test-time future sampling or iterative refinement is needed to obtain guidance, keeping inference cost close to a standard WAM.
  • Because the action expert needs no direct guidance injection, the same semantic foresight could be reused across different action decoders attached to the same video backbone.
  • Instruction grounding in feature space rather than in pixel or text space should transfer across appearance shifts, as suggested by the LIBERO-Plus language and lighting perturbations and the real-world height and lighting generalization results.
  • The method's success under language rephrasing (81.7% on that LIBERO-Plus perturbation) suggests that grounding the instruction in the current scene, rather than embedding it independently, is what drives robustness to rephrasing.

Reading between the lines

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

  • A testable extension: since the planner predicts future features from the current frame alone, the same architecture could be trained to predict foresight at multiple horizons and then select the horizon whose downstream action success is highest, effectively doing closed-loop temporal abstraction.
  • The teacher-feature design raises a question the paper does not settle: whether SigLIP2 and Depth Anything 3 features are the minimal sufficient supervision, or whether a single encoder with both properties (e.g., a VLM with dense metric heads) would yield the same gains with fewer tokens.
  • If the gains come mainly from grounding object identity, a lighter substitute—such as an open-vocabulary object detector with geometric priors—might capture much of the benefit at lower VLM cost; the 0.4-point gap between the full model and the no-spatial variant (98.7 vs 98.3) suggests spatial features add a smaller but real increment.
  • The largest real-world gains on ordered, multi-object tasks suggest the method's value concentrates where instructions specify relationships and order; applying it to tasks with sequential language subgoals might compound the benefit.
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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 / 5 minor

Summary. SG-WAM augments world-action models (WAMs) with semantic foresight predicted by a VLM planner. From the current observation and language instruction, the planner outputs two dense feature maps—text-grounded foresight aligned to SigLIP2 features of future keyframes and spatial-aware foresight aligned to Depth Anything 3 features—using learnable query tokens (Eqs. 2–3). These maps are fused via a learned gate and injected into the video expert through cross-attention, while the action expert inherits the guidance through joint attention. Training proceeds in three stages: planner pretraining, planner+video co-training, and full co-training with the action expert. The authors evaluate on LIBERO (98.7% average, Table 1), LIBERO-Plus (81.3% average, Table 2), and a dual-arm real-world setup, with ablations in Table 3 and qualitative instruction-following studies on DROID. The central claim is that text-grounded and spatial-aware foresight makes both future-video generation and action prediction follow the language instruction.

Significance. If the mechanism is as claimed, SG-WAM is a plausible and well-motivated alternative to observation-independent text embeddings in WAMs. The use of frozen teacher encoders on actual future frames as supervision is methodologically sound, and the three-stage training is a sensible way to preserve pretrained generative ability. The LIBERO-Plus robustness gains and the real-world qualitative results are encouraging. However, the paper's central contribution is not yet established: the evidence does not show that the predicted foresight is truly anticipatory rather than a repackaging of current-frame features, and the LIBERO SOTA claim rests on a 0.2-point margin without error bars. The quantitative ablations are also small and unreplicated. If the additional diagnostics requested are provided, the work could be a solid contribution; as it stands, the significance is conditional.

major comments (3)
  1. [Section 4.2 (Table 1)] The SOTA claim on LIBERO rests on a 0.2-point margin (98.7 vs. 98.5) over LingBot-VA, and no standard errors, confidence intervals, or number of seeds are reported for any table in the paper. Given typical run-to-run variance in policy training, this margin may be within noise. Please report mean ± std over at least three seeds for SG-WAM and the strongest baselines, or equivalently provide confidence intervals, and adjust the claim accordingly.
  2. [Section 3.2 (Eq. (3)), Section 4.5 (Table 3)] The paper does not provide evidence that the planner's predictions are actually anticipatory. The training target in Eq. (3) is features extracted from future keyframes, but no comparison is made between the predicted features and current-frame teacher features. In tabletop manipulation the scene is largely static, so SigLIP2 and Depth Anything 3 features of future keyframes may be dominated by static appearance, and the planner could achieve low alignment loss by emitting near-current features with modest instruction grounding. The 'w/o semantic guidance' ablation removes guidance entirely (97.2 vs. 98.7), so it does not isolate the future component. Please add (a) a similarity comparison of predicted features to current vs. future teacher features as a function of keyframe offset, (b) an inference-time ablation that replaces the predicted foresight with current-frame teacher features, and (c) if feasible, per-offset prediction error to demonstrate degradation with horizon. Without such diagnostics, the claim that future anticipation, rather than observation grounding, drives the instruction-following gains is unsupported.
  3. [Section 3.2 (Eq. (3)) and Section 3.3 (Eq. (4))] The claim that the text-grounded and spatial-aware branches provide complementary information is not substantiated by the ablations: removing either branch changes success rate by only 0.4-1.0 points, and no statistical significance is reported. Moreover, the learned gate g in Eq. (4) is not reported; if the sigmoid output is near 0 or 1, the two branches may not be balanced as intended. Please report the learned value of g and quantify the agreement/correlation between the two predicted maps, so that the reader can assess whether the two semantics are actually distinct.
minor comments (5)
  1. [Section 4.1] The implementation states that the planner predicts K=4 future keyframes at evenly spaced offsets, but the offsets are not specified; please report the actual frame offsets (or time horizon) used.
  2. [Section 3.2] The planner is described as applied to each camera view independently with shared weights, but the manuscript does not explain how the foresight maps from multiple camera views are combined in the world model; please clarify the fusion mechanism.
  3. [Section 3.3] The guidance tokens receive a positional encoding of keyframe time and spatial location; please specify how this encoding is constructed (e.g., sinusoidal or learned).
  4. [Section 4.5] The single-stage training ablation is described only as 'single-stage training'; please clarify whether this means all components are trained jointly from random initialization or with some warm-start.
  5. [Figure 4] Real-world success rates are reported without error bars or confidence intervals; since there are only 50 trials per setting, reporting a binomial confidence interval would aid interpretation.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the semantic foresight is supervised by frozen teacher features from actual future frames, and the evaluation compares against external baselines.

full rationale

The paper's central training target is defined in Eq. 3 as an alignment loss between the planner's predictions and features extracted by frozen SigLIP2 and Depth Anything 3 from the actual future keyframes; the teachers are detached and discarded at inference, so no predicted quantity is defined in terms of a fitted success-rate parameter or the model's own outputs. The guidance injection (Eq. 4) and the generation losses (Eq. 5) are standard conditioning and flow-matching objectives, and the ablations in Table 3 vary real components rather than re-fitting a constant. Self-citations such as Yan et al. 2026 (S-VAM), Zhong et al. 2025, Zhong et al. 2026, and Song et al. 2025 appear only as related-work context and are not load-bearing premises; no uniqueness theorem or prior result by the authors is invoked to force the method's design. The claim that future foresight is the cause of the gains is less rigorously supported than the headline suggests (no diagnostic separates future from current-frame teacher features), but that is a correctness or evidence concern, not circularity: the comparison would be against independent external benchmarks and the supervision is not constructed from the target claim. A non-finding is therefore appropriate.

Assumptions & free parameters 8 free parameters · 4 assumptions · 0 invented entities

The central claim rests on hand-chosen training weights and architectural constants (lambda_spa, lambda_plan, lambda_act, K, P, guidance drop, query-token counts, learned gate) rather than on a derivation. The substantive assumptions are that SigLIP2 and Depth Anything 3 future-frame features are valid supervision, that a VLM can anticipate them from one frame, that the pretrained video backbone remains reliable after fine-tuning, and that the benchmark protocols are comparable. No new physical entities are introduced; the semantic foresight maps are learned representations in feature space.

free parameters (8)
  • lambda_spa = 4e-3
    Hand-chosen weight balancing spatial-aware alignment in Eq. 3; directly controls how much geometry enters the guidance.
  • lambda_plan = 0.25
    Weight on the planner alignment loss in stages 2 and 3; chosen by hand and affects how strongly the planner is tied to teacher features.
  • lambda_act = 1.0
    Weight on the action flow-matching loss relative to the video loss in Eq. 5.
  • K = 4
    Number of future keyframes predicted by the planner; chosen to cover a prediction horizon.
  • P = 256
    Number of guidance tokens per semantic map; set to match the token layout of the teacher encoders.
  • guidance_drop_probability = 0.15
    Probability of dropping semantic guidance during training to enable classifier-free guidance at inference.
  • query_token_counts = 32 base + 32 per branch
    Architectural choice for shared and modality-specific query token groups in the VLM planner.
  • learned_gate_g = learned, initial value not reported
    Sigmoid-gated scalar that weights the spatial map in Eq. 4; learned during training.
assumptions (4)
  • domain assumption Frozen teacher encoders SigLIP2 and Depth Anything 3 provide valid and sufficient supervision targets.
    Eq. 3 aligns predicted maps to features of actual future frames; if these features do not carry task semantics and geometry, the guidance cannot be instruction-grounded.
  • domain assumption A VLM with appended query tokens can predict future dense features from a single current observation and instruction in one forward pass.
    Sec. 3.2; the whole method depends on this forward anticipation being reliable at inference.
  • domain assumption The pretrained video diffusion model LTX-Video can be fine-tuned for robot video-action prediction with flow matching and joint attention.
    Sec. 3.3 and Implementation Details; this is inherited from prior WAM work, not re-derived in this paper.
  • domain assumption LIBERO and LIBERO-Plus success rates, as used here, are comparable across methods and are a valid proxy for instruction following.
    Sec. 4.1-4.2; no repeated seeds or statistical tests are reported, so comparability is assumed.

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Cite this review

Pith. "Pith review of SG-WAM: Text-Grounded and Spatial-aware Semantic Guidance for World-Action Models." pith.science (2026). https://pith.science/paper/6YPC47WC

@misc{pith2026260808839,
  author       = {Pith},
  title        = {Pith review of: SG-WAM: Text-Grounded and Spatial-aware Semantic Guidance for World-Action Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6YPC47WC}},
  note         = {Machine review of arXiv:2608.08839}
}
read the original abstract

World-Action Models (WAMs) have emerged as a promising paradigm for robotic manipulation. However, most existing WAMs generate future videos and actions by relying mainly on visual cues rather than language instructions, since off-the-shelf text encoders embed instructions independently of visual observations. As a result, the videos predicted by these WAMs are often semantically misaligned with their corresponding language instructions, which degrades the accuracy of the predicted actions. To overcome this limitation, we propose SG-WAM, a semantic guidance method for world-action models that leverages a vision-language model (VLM) as a semantic planner to enhance the instruction-grounding capacity of world-action models. Specifically, we train a VLM-based planner to predict text-grounded and spatial-aware semantic foresight. The text-grounded semantic foresight grounds the instruction by identifying the correct target objects, and the spatial-aware semantic foresight provides the scene geometry for precise manipulation. We then inject this foresight into the world-action model as high-level semantic guidance, ensuring that both future-video generation and action prediction faithfully follow the language instruction. Extensive experiments in simulation and the real world demonstrate the superiority of our semantic guidance method, showcasing precise manipulation and strong instruction-following capabilities.

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Reference graph

Works this paper leans on

28 extracted references · 2 canonical work pages

  1. [1]

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

    Hongzhe Bi, Hengkai Tan, Shenghao Xie, Zeyuan Wang, Shuhe Huang, Haitian Liu, Ruowen Zhao, Yao Feng, Chendong Xiang, Yinze Rong, et al. Motus: A unified latent action world model.arXiv preprint arXiv:2512.13030,

    arXiv

  2. [3]

    π0.5: a vision-language-action model with open-world generalization

    Kevin Black, Noah Brown, James Darpinian, Karan Dhabalia, Danny Driess, Adnan Esmail, Michael Robert Equi, et al. π0.5: a vision-language-action model with open-world generalization. In9th Annual Conference on Robot Learning, 2025a. Kevin Black, Noah Brown, Danny Driess, Adnan Esmail, Michael Robert Equi, Chelsea, et al.π0: A vision-language- action flow ...

    work page doi:10.15607/rss.2025.xxi.01

  3. [5]

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

    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:2506.21539,

    arXiv

  4. [6]

    Gr-2: A generative video-language-action model with web-scale knowledge for robot manipulation.arXiv preprint arXiv:2410.06158,

    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,

    arXiv

  5. [8]

    Ltx-video: Realtime video latent diffusion.arXiv preprint arXiv:2501.00103,

    Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, Poriya Panet, Sapir Weissbuch, Victor Kulikov, Yaki Bitterman, Zeev Melumian, and Ofir Bibi. Ltx-video: Realtime video latent diffusion.arXiv preprint arXiv:2501.00103,

    arXiv

  6. [9]

    Nora: A small open-sourced generalist vision language action model for embodied tasks.arXiv preprint arXiv:2504.19854,

    Chia-Yu Hung, Qi Sun, Pengfei Hong, Amir Zadeh, Chuan Li, U-Xuan Tan, Navonil Majumder, and Soujanya Poria. Nora: A small open-sourced generalist vision language action model for embodied tasks.arXiv preprint arXiv:2504.19854,

    arXiv

  7. [10]

    Rynnvla-001: Using human demonstrations to improve robot manipulation.arXiv preprint arXiv:2509.15212,

    Yuming Jiang, Siteng Huang, Shengke Xue, Yaxi Zhao, Jun Cen, Sicong Leng, Kehan Li, Jiayan Guo, Kexiang Wang, Mingxiu Chen, Fan Wang, Deli Zhao, and Xin Li. Rynnvla-001: Using human demonstrations to improve robot manipulation.arXiv preprint arXiv:2509.15212,

    arXiv

  8. [11]

    2025.XXI.017

    doi: 10.15607/RSS. 2025.XXI.017. 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,

    arXiv 2025

Show all 28 references
  1. [13]

    Unified video action model.arXiv preprint arXiv:2503.00200,

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

    arXiv

  2. [14]

    Genie envisioner: A unified world foundation platform for robotic manipulation.arXiv preprint arXiv:2508.05635,

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

    arXiv

  3. [15]

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

    Haotong Lin, Sili Chen, Junhao Liew, Donny Y Chen, Zhenyu Li, Guang Shi, Jiashi Feng, and Bingyi Kang. Depth anything 3: Recovering the visual space from any views.arXiv preprint arXiv:2511.10647,

    arXiv

  4. [16]

    Oa-wam: Object-addressable world action model for robust robot manipulation.arXiv preprint arXiv:2605.06481,

    Yushan Liu, Peibo Sun, Shoujie Li, Yifan Xie, Lingfeng Zhang, Xintao Chao, Shiyuan Dong, Fang Chen, Xiao-Ping Zhang, and Wenbo Ding. Oa-wam: Object-addressable world action model for robust robot manipulation.arXiv preprint arXiv:2605.06481,

    arXiv

  5. [17]

    Mask world model: Predicting what matters for robust robot policy learning.arXiv preprint arXiv:2604.19683,

    Yunfan Lou, Xiaowei Chi, Xiaojie Zhang, Zezhong Qian, Chengxuan Li, Rongyu Zhang, Yaoxu Lyu, Guoyu Song, Chuyao Fu, Haoxuan Xu, Pengwei Wang, and Shanghang Zhang. Mask world model: Predicting what matters for robust robot policy learning.arXiv preprint arXiv:2604.19683,

    arXiv

  6. [18]

    Dit4dit: Jointly modeling video dynamics and actions for generalizable robot control.arXiv preprint arXiv:2603.10448,

    Teli Ma, Jia Zheng, Zifan Wang, Chunli Jiang, Andy Cui, Junwei Liang, and Shuo Yang. Dit4dit: Jointly modeling video dynamics and actions for generalizable robot control.arXiv preprint arXiv:2603.10448,

  7. [19]

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

    Karl Pertsch, Kyle Stachowicz, Brian Ichter, Danny Driess, Suraj Nair, Quan Vuong, Oier Mees, Chelsea Finn, and Sergey Levine. Fast: Efficient action tokenization for vision-language-action models.arXiv preprint arXiv:2501.09747,

    arXiv

  8. [21]

    Siglip 2: Multilingual vision-language encoders with improved semantic understanding, localization, and dense features.arXiv preprint arXiv:2502.14786,

    Michael Tschannen, Alexey Gritsenko, Xiao Wang, Muhammad Ferjad Naeem, Ibrahim Alabdulmohsin, Nikhil Parthasarathy, Talfan Evans, Lucas Beyer, Ye Xia, Basil Mustafa, et al. Siglip 2: Multilingual vision-language encoders with improved semantic understanding, localization, and ...

    arXiv

  9. [22]

    S-VAM: Shortcut video-action model by self-distilling geometric and semantic foresight.arXiv preprint arXiv:2603.16195,

    Haodong Yan, Zhide Zhong, Jiaguan Zhu, Junjie He, Weilin Yuan, Wenxuan Song, Xin Gong, Yingjie Cai, Guanyi Zhao, Xu Yan, Bingbing Liu, Ying-Cong Chen, and Haoang Li. S-VAM: Shortcut video-action model by self-distilling geometric and semantic foresight.arXiv preprint arXiv:2603.16195,

  10. [23]

    Maskwam: Unifying mask prompting and prediction for world-action models.arXiv preprint arXiv:2606.13515,

    12 Hanyang Yu, Haitao Lin, Jingbo Zhang, Wenyao Zhang, Chenghao Gu, Heng Li, and Ping Tan. Maskwam: Unifying mask prompting and prediction for world-action models.arXiv preprint arXiv:2606.13515,

    arXiv

  11. [24]

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

    Tianyuan Yuan, Zibin Dong, Yicheng Liu, and Hang Zhao. Fast-wam: Do world action models need test-time future imagination?arXiv preprint arXiv:2603.16666,

    arXiv

  12. [25]

    FlowVLA: Visual chain of thought-based motion reasoning for vision-language-action models.arXiv preprint arXiv:2508.18269,

    Zhide Zhong, Haodong Yan, Junfeng Li, Xiangchen Liu, Xin Gong, Tianran Zhang, Wenxuan Song, Jiayi Chen, Xinhu Zheng, Hesheng Wang, et al. FlowVLA: Visual chain of thought-based motion reasoning for vision-language-action models.arXiv preprint arXiv:2508.18269,

  13. [26]

    DualCoT-VLA: Visual-linguistic chain of thought via parallel reasoning for vision-language-action models.arXiv preprint arXiv:2603.22280,

    Zhide Zhong, Junfeng Li, Junjie He, Haodong Yan, Xin Gong, Guanyi Zhao, Yingjie Cai, Jiantao Gao, Xu Yan, Bingbing Liu, Yingcong Chen, Liuqing Yang, and Haoang Li. DualCoT-VLA: Visual-linguistic chain of thought via parallel reasoning for vision-language-action models.arXiv pr...

  14. [27]

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

    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.arXiv preprint arXiv:2504.02792,

    arXiv

  15. [28]

    Dswam: A dual-system world action foundation model for fine-grained robot manipulation.arXiv preprint arXiv:2607.04927,

    Jian Zhu, Jianjun Zhang, Taiyi Su, Tianbin Liu, Zhangyuan Wang, Kai Xie, Zitai Huang, Chong Ma, Youzhang He, Tianjian Wang, Hanyang Wang, Weihao Ding, and Yi Xu. Dswam: A dual-system world action foundation model for fine-grained robot manipulation.arXiv preprint arXiv:2607.04927,

    arXiv

  16. [2020]

    ReconVLA: Reconstructive vision-language-action model as effective robot perceiver

    Wenxuan Song, Ziyang Zhou, Han Zhao, Jiayi Chen, Pengxiang Ding, Haodong Yan, Yuxin Huang, Feilong Tang, Donglin Wang, and Haoang Li. ReconVLA: Reconstructive vision-language-action model as effective robot perceiver. arXiv preprint arXiv:2508.10333,

    arXiv

  17. [2023]

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

    Qingwen Bu, Yanting Yang, Jisong Cai, Shenyuan Gao, Guanghui Ren, Maoqing Yao, Ping Luo, and Hongyang Li. Univla: Learning to act anywhere with task-centric latent actions.arXiv preprint arXiv:2505.06111,

    arXiv

  18. [2024]

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

    Senyu Fei, Siyin Wang, Junhao Shi, Zihao Dai, Jikun Cai, Pengfang Qian, Li Ji, Xinzhe He, Shiduo Zhang, Zhaoye Fei, Jinlan Fu, Jingjing Gong, and Xipeng Qiu. Libero-plus: In-depth robustness analysis of vision-language-action models.arXiv preprint arXiv:2510.13626,

    arXiv

  19. [2025]

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

    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,

    arXiv

  20. [2026]

    Causal world modeling for robot control.arXiv preprint arXiv:2601.21998,

    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,

    arXiv

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Reviewed August 14, 2026 · model on record in the stance chip above.