{"total":17,"items":[{"citing_arxiv_id":"2607.08639","ref_index":67,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Native Video-Action Pretraining for Generalizable Robot Control","primary_cat":"cs.RO","submitted_at":"2026-07-09T16:15:43+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A video-action foundation model pretrained natively with a causal diffusion transformer and semantic visual-action tokenizer reports improved few-shot robot manipulation and 225 Hz asynchronous closed-loop control.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2607.06403","ref_index":22,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"From Foundation to Application: Improving VLA Models in 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closed-loop robot control, reporting 100% and 91% task success plus large memory savings on one Transformer block.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2607.02195","ref_index":28,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Bridge-WA: Predicting Where and How the World Changes for Robotic Action","primary_cat":"cs.RO","submitted_at":"2026-07-02T14:03:44+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Bridge-WA introduces a lightweight distillation-based world-action model that uses future-change priors to improve robotic task success and robustness without deployment-time dense rollouts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2607.01586","ref_index":30,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"VLAFlow: A Unified Training Framework for Vision-Language-Action Models via Co-training and Future Latent Alignment","primary_cat":"cs.CV","submitted_at":"2026-07-02T01:38:16+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2607.00678","ref_index":41,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"ABot-M0.5: Unified Mobility-and-Manipulation World Action Model","primary_cat":"cs.CV","submitted_at":"2026-07-01T09:21:20+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Aligning temporal granularity, action subspaces, and train-test conditioning yields SOTA long-horizon mobile and fine-grained manipulation success for a unified world-action model.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.22136","ref_index":10,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Wh0: Generative World Models as Scalable Sources of Egocentric Human Hand Manipulation Data","primary_cat":"cs.RO","submitted_at":"2026-06-20T16:31:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Wh0 generates scalable egocentric human manipulation videos with world models and converts them to boost pretrained VLA models' zero-shot dexterous task success from 8.3% to 38.9% on 18 real-world tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20562","ref_index":8,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"MemoryWAM: Efficient World Action Modeling with Persistent Memory","primary_cat":"cs.RO","submitted_at":"2026-06-18T17:59:51+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"MemoryWAM is a world action model with a hybrid memory design using recent frames, anchor frames, and gist tokens for efficient long-horizon robotic manipulation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.16533","ref_index":88,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI","primary_cat":"cs.AI","submitted_at":"2026-06-15T10:37:42+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Kairos learns and maintains control-sufficient world states via a cross-embodiment curriculum, hybrid linear temporal attention, and deployment-aware co-design for Physical AI.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.13674","ref_index":22,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"RepWAM: World Action Modeling with Representation Visual-Action Tokenizers","primary_cat":"cs.CV","submitted_at":"2026-06-11T17:59:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RepWAM introduces representation visual-action tokenizers to pretrain world action models that jointly model future visual states and latent actions under instructions for improved robot manipulation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.12965","ref_index":22,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"EmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space Guidance for Zero-Shot Cross-Embodiment Deployment","primary_cat":"cs.RO","submitted_at":"2026-06-11T06:49:39+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"EmbodiSteer steers embodiment-agnostic Cartesian diffusion policies into joint space with Jacobian-based collision guidance after each denoising step for zero-shot cross-embodiment deployment.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.11187","ref_index":3,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Next Forcing: Causal World Modeling with Multi-Chunk Prediction","primary_cat":"cs.CV","submitted_at":"2026-06-09T17:59:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Next Forcing augments video generation models with auxiliary multi-chunk prediction modules to achieve faster training convergence, higher accuracy at high frame rates, and 2x faster inference on world modeling benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.10040","ref_index":20,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Efficient-WAM: A 1B-Parameter World-Action Model with Low-Cost Future Imagination","primary_cat":"cs.RO","submitted_at":"2026-06-08T18:14:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Efficient-WAM delivers 30x lower latency than prior WAMs at 100 ms per chunk while keeping competitive manipulation performance by treating coarse future video as guidance rather than high-fidelity output.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.27947","ref_index":32,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"SANTS: A State-Adaptive Scheduler for World Action Models","primary_cat":"cs.RO","submitted_at":"2026-05-27T04:40:48+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A state-adaptive noise-trajectory scheduler selects intermediate video conditions for action generation, matching or beating full-denoising WAMs at far lower latency.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15153","ref_index":30,"ref_count":2,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Pelican-Unify 1.0: A Unified Embodied Intelligence Model for Understanding, Reasoning, Imagination and Action","primary_cat":"cs.RO","submitted_at":"2026-05-14T17:50:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A unified embodied foundation model uses one VLM for understanding and reasoning plus a joint video-action future generator, reporting competitive scores on VLM, world modeling, and robot benchmarks without apparent compromise.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06481","ref_index":51,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation","primary_cat":"cs.RO","submitted_at":"2026-05-07T16:06:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"World action verifier: Self-improving world models via forward-inverse asymmetry. arXiv preprint arXiv:2604.01985, 2026. [50] Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. Object-centric learning with slot attention. In Advances in Neural Information Processing Systems (NeurIPS), 2020. arXiv:2006.15055. [51] Hao Luo, Wanpeng Zhang, Yicheng Feng, Sipeng Zheng, Haiweng Xu, Chaoyi Xu, Ziheng Xi, Yuhui Fu, and Zongqing Lu. Being-H0.7: A latent world-action model from egocentric videos.arXiv preprint arXiv:2605.00078, 2026. [52] Qi Lv, Weijie Kong, Hao Li, Jia Zeng, Zherui Qiu, et al. F1: A vision-language-action model bridging understanding and generation to actions."},{"citing_arxiv_id":"2605.06747","ref_index":26,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"HumanNet: Scaling Human-centric Video Learning to One Million Hours","primary_cat":"cs.CV","submitted_at":"2026-05-07T15:21:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"HumanNet is a 1M-hour human-centric video dataset with interaction annotations that enables better vision-language-action model performance than equivalent robot data in a controlled test.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"[24] Hao Luo, Wanpeng Zhang, Yicheng Feng, Sipeng Zheng, Haiweng Xu, Chaoyi Xu, Ziheng Xi, Yuhui Fu, and Zongqing Lu. Being-h0.7: A latent world-action model from egocentric videos, 2026. URLhttps://arxiv.org/ abs/2605.00078. [25] Karttikeya Mangalam, Raiymbek Akshulakov, and Jitendra Malik. Egoschema: A diagnostic benchmark for very long-form video language understanding, 2023. URLhttps://arxiv.org/abs/2308.09126. [26] Microsoft, :, Abdelrahman Abouelenin, Atabak Ashfaq, Adam Atkinson, Hany Awadalla, Nguyen Bach, Jianmin Bao, Alon Benhaim, Martin Cai, Vishrav Chaudhary, Congcong Chen, Dong Chen, Dongdong Chen, Junkun Chen, Weizhu Chen, Yen-Chun Chen, Yi ling Chen, Qi Dai, Xiyang Dai, Ruchao Fan, Mei Gao, Min Gao, Amit Garg, Abhishek Goswami, Junheng Hao, Amr Hendy, Yuxuan Hu, Xin Jin, Mahmoud Khademi, Dongwoo Kim,"}],"limit":50,"offset":0}