{"work":{"id":"a33c4ee0-db06-4f9a-8852-c62e3a72fc27","openalex_id":null,"doi":null,"arxiv_id":"2601.21998","raw_key":null,"title":"Causal World Modeling for Robot Control","authors":null,"authors_text":"Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han","year":2026,"venue":"cs.CV","abstract":"This work highlights that video world modeling, alongside vision-language pre-training, establishes a fresh and independent foundation for robot learning. Intuitively, video world models provide the ability to imagine the near future by understanding the causality between actions and visual dynamics. Inspired by this, we introduce LingBot-VA, an autoregressive diffusion framework that learns frame prediction and policy execution simultaneously. Our model features three carefully crafted designs: (1) a shared latent space, integrating vision and action tokens, driven by a Mixture-of-Transformers (MoT) architecture, (2) a closed-loop rollout mechanism, allowing for ongoing acquisition of environmental feedback with ground-truth observations, (3) an asynchronous inference pipeline, parallelizing action prediction and motor execution to support efficient control. We evaluate our model on both simulation benchmarks and real-world scenarios, where it shows significant promise in long-horizon manipulation, data efficiency in post-training, and strong generalizability to novel configurations. The code and model are made publicly available to facilitate the community.","external_url":"https://arxiv.org/abs/2601.21998","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-07-10T12:47:05.502321+00:00","pith_arxiv_id":"2601.21998","created_at":"2026-05-10T08:12:25.965922+00:00","updated_at":"2026-07-10T12:47:05.502321+00:00","title_quality_ok":true,"display_title":"Causal World Modeling for Robot Control","render_title":"Causal World Modeling for Robot Control"},"hub":{"state":{"work_id":"a33c4ee0-db06-4f9a-8852-c62e3a72fc27","tier":"super_hub","tier_reason":"100+ Pith inbound or 10,000+ external citations","pith_inbound_count":113,"external_cited_by_count":null,"distinct_field_count":8,"first_pith_cited_at":"2026-01-11T21:00:58+00:00","last_pith_cited_at":"2026-07-09T16:15:43+00:00","author_build_status":"needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-08-23T01:49:19.395003+00:00","tier_text":"super_hub"},"tier":"super_hub","role_counts":[{"context_role":"background","n":17},{"context_role":"method","n":2},{"context_role":"baseline","n":1}],"polarity_counts":[{"context_polarity":"background","n":17},{"context_polarity":"use_method","n":2},{"context_polarity":"baseline","n":1}],"runs":{"ask_index":{"job_type":"ask_index","status":"succeeded","result":{"title":"Causal World Modeling for Robot Control","claims":[{"claim_text":"This work highlights that video world modeling, alongside vision-language pre-training, establishes a fresh and independent foundation for robot learning. Intuitively, video world models provide the ability to imagine the near future by understanding the causality between actions and visual dynamics. Inspired by this, we introduce LingBot-VA, an autoregressive diffusion framework that learns frame prediction and policy execution simultaneously. Our model features three carefully crafted designs: (1) a shared latent space, integrating vision and action tokens, driven by a Mixture-of-Transformer","claim_type":"abstract","evidence_strength":"source_metadata"},{"claim_text":"for strong generalization. Other representative VLA models and optimizations include UniVLA [7], VLA-ADP [19], CogACT [20], SmolVLA [21], NORA and NORA-Long [22], WorldVLA and WorldVLA* [8], SP-VLA [23], FlashVLA [24], VLA-Cache [25], FastV and FastV(+OFT) [ 26], SparseVLM [27], and CSP [28]. Parallel efforts emerging as W AMs include Motus [13], LingBot-V A [14], and Fast-W AM [29]. 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As shown in Figure 1, existing WAMs can be broadly grouped into two representative paradigms:(A)future videos and actions are jointly denoised with shared attention [4, 6, 5]; and(B)actions are predicted after, and conditioned on, generated future videos [3, 7, 8]. We also implement a no-video-co-training variant, which serves as a direct control for the role of the training objective itself. Toge","claim_type":"background","confidence":0.8,"evidence_strength":"citation_context"},{"claim_text":"On the one hand,policy modelsfocus on predicting exe- cutable actions for robot control. Vision-Language-Action (VLA) models [1-7] fine-tune pretrained Vision-Language Models (VLMs) to output motor commands, excelling at instruction following and semantic reasoning but lacking the geometric intuition and physical awareness of how actions continuously unfold in the real world [8]. 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The planner is refined by minimizing the cumulative objectiveLNighttime(P): − ∑︁ 𝑘∈ {𝑃,𝑈,𝑇} 𝔼(c,𝜋+,𝜋− )∼D 𝑘 \u0014 log𝜎 \u0012 𝛽log P (𝜋+|c) Pref(𝜋+|c) −𝛽log P (𝜋− |c) Pref(𝜋− |c) \u0013\u0015 .(6) By jointly refiningS and P, nighttime learning converts unsuccessful daytime attempts into valuable supervisory signals, closing the loop between imagination and reality and preparing the system for the next cycle of exploration. 4.4. Dual-Phase Curriculum Evolution To ensure a stable and progressive transition from simple manipulations to complex behaviors,","citing_arxiv_id":"2605.13775"},{"n":1,"role":"method","polarity":"use_method","paper_title":"DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks","primary_cat":"cs.CV","context_text":"Here, the inner-loop model f is formulated as a residual block wrapping a two-layer multi-layer perceptron: fT T Tmlp(x;W) =x+LN(MLP(x;W)) , where LN denotes Layer Normalization and the MLP utilizes a GELU activation with a 4× expansion factor. Once the weights are updated to Wt via gradient descent, the output hidden state is extracted utilizing the query projection: lt =f T T Tmlp(θQzt;W t)(12) 6 Additionally, naively inserting TTT layers into a pre-trained network would dramatically worsen its predictions at the beginning of fine-tuning, we gate TTT with a learned vector α∈R D following standard practice, as Wang et al. (2025): fT T T(zt;W t) =tanh(α)⊗f T T Tmlp(θQzt;W t) +z t,(13) where we initialize all values inαto0.1at the beginning of fine-tuning.","citing_arxiv_id":"2604.16484"},{"n":1,"role":"method","polarity":"use_method","paper_title":"World Action Models are Zero-shot Policies","primary_cat":"cs.RO","context_text":"Yi Ru Wang, Sangho Lee, et al. Molmoact: Action reasoning models that can reason in space.arXiv preprint arXiv:2508.07917, 2025. 4 [59] 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. 7 [60] Shuang Li, Yihuai Gao, Dorsa Sadigh, and Shuran Song. Unified video action model.arXiv preprint arXiv:2503.00200, 2025. 5, 7 [61] Yi Li, Yuquan Deng, Jesse Zhang, Joel Jang, Marius Memmel, Raymond Yu, Caelan Reed Garrett, Fabio Ramos, Dieter Fox, Anqi Li, et al. Hamster: Hierarchical action models for open-world robot manipulation. arXiv preprint arXiv:2502.","citing_arxiv_id":"2602.15922"}]},"error":null,"updated_at":"2026-07-04T06:26:43.132323+00:00"},"identity_refresh":{"job_type":"identity_refresh","status":"succeeded","result":{"items":[{"title":"Qwen3 Technical Report","outcome":"unchanged","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","resolver":"local_arxiv","confidence":0.98,"old_work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e"}],"counts":{"fixed":0,"merged":0,"unchanged":1,"quarantined":0,"needs_external_resolution":0},"errors":[],"attempted":1},"error":null,"updated_at":"2026-07-04T06:27:06.789955+00:00"},"role_polarity":{"job_type":"role_polarity","status":"succeeded","result":{"title":"Causal World Modeling for Robot Control","claims":[{"claim_text":"This work highlights that video world modeling, alongside vision-language pre-training, establishes a fresh and independent foundation for robot learning. 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Despite consistent progress across benchmarks, nearly all existing action models share a core limitation: treatin","claim_type":"background","confidence":0.85,"evidence_strength":"citation_context"},{"claim_text":"environments, showcasing diverse manipulation tasks and embodied interaction scenarios. Furthermore, our framework supports the evaluation of a comprehensive suite of VLA methods. Prominent examples include π0 [13] and π0.5 [53], which leverage the PaliGemma vision-language backbone augmented with mixture-of-experts (MoE) action heads to achieve robust multi-task generalization. We also incorporate LingBot-VA [67], which approaches the task from a generative perspective by employing a video diff","claim_type":"background","confidence":0.85,"evidence_strength":"citation_context"},{"claim_text":"Surveys near-universally discuss the foundational models and works from state-of-the-art teams at META AI with Yann LeCun [64, 52, 208], the Alibaba group [175], Cosmos from NVidia [87], and Berkeley University's two groups with Sergey Levine [75] and Fei-fei Li [200] [178] [70]. Additional states-of-the-art in- novate on architecture by using Vision-Action [99] and Vision-Action-Language models [170], auto-regression models [50], and diffusion [218, 104]. Neverthe- less, a framework for a unifi","claim_type":"background","confidence":0.85,"evidence_strength":"citation_context"},{"claim_text":"For simplicity, we focus on single action chunk generation and omit the outer auto-regressive loop. As shown in Figure 1, existing WAMs can be broadly grouped into two representative paradigms:(A)future videos and actions are jointly denoised with shared attention [4, 6, 5]; and(B)actions are predicted after, and conditioned on, generated future videos [3, 7, 8]. We also implement a no-video-co-training variant, which serves as a direct control for the role of the training objective itself. Toge","claim_type":"background","confidence":0.8,"evidence_strength":"citation_context"},{"claim_text":"On the one hand,policy modelsfocus on predicting exe- cutable actions for robot control. Vision-Language-Action (VLA) models [1-7] fine-tune pretrained Vision-Language Models (VLMs) to output motor commands, excelling at instruction following and semantic reasoning but lacking the geometric intuition and physical awareness of how actions continuously unfold in the real world [8]. World Action Models (WAMs) [8-12] further leverage video generation models to jointly predict future observations and","claim_type":"background","confidence":0.8,"evidence_strength":"citation_context"},{"claim_text":"Unified world models: Coupling video and action diffusion for pretraining on large robotic datasets.arXiv preprint arXiv:2504.02792, 2025. [35] Yanjiang Guo, Yucheng Hu, Jianke Zhang, Yen-Jen Wang, Xiaoyu Chen, Chaochao Lu, and Jianyu Chen. Prediction with action: Visual policy learning via joint denoising process.Ad- vances in Neural Information Processing Systems, 37:112386-112410, 2024. [36] Lin Li, Qihang Zhang, Yiming Luo, Shuai Yang, Ruilin Wang, Fei Han, Mingrui Yu, Zelin Gao, Nan Xue, Xi","claim_type":"background","confidence":0.8,"evidence_strength":"citation_context"}],"why_cited":"Pith tracks Causal World Modeling for Robot Control because it crossed a citation-hub threshold. Current citing contexts most often use it as background evidence (16 contexts).","role_counts":[{"n":16,"context_role":"background"},{"n":2,"context_role":"method"},{"n":1,"context_role":"baseline"}]},"graph":{"co_cited":[{"title":"World Action Models are Zero-shot Policies","work_id":"9a85fc69-74df-450e-94cd-69d186e9e830","shared_citers":65},{"title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","work_id":"f790abdc-a796-482f-a40d-f8ee035ecfc2","shared_citers":55},{"title":"$\\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization","work_id":"d1ad7304-d09a-49bc-809e-846439f6aff9","shared_citers":46},{"title":"Fast-WAM: Do World Action Models Need Test-time Future Imagination?","work_id":"772d0226-9ad6-42c0-9c79-d36d37edbbe4","shared_citers":46},{"title":"Wan: Open and Advanced Large-Scale Video Generative Models","work_id":"ad3ebc3b-4224-46c9-b61d-bcf135da0a7c","shared_citers":46},{"title":"Motus: A Unified Latent Action World Model","work_id":"d0b2d257-524d-4d67-9daf-5fb43e5e977a","shared_citers":45},{"title":"Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning","work_id":"3d63039f-41b0-4a31-af31-6fc10f5c1b1b","shared_citers":44},{"title":"OpenVLA: An Open-Source Vision-Language-Action Model","work_id":"3e7e65c5-5aed-4fe9-8414-2092bcb31cc7","shared_citers":41},{"title":"RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation","work_id":"9b985126-4a2f-4bdf-b014-2a7524ec634e","shared_citers":40},{"title":"GR00T N1: An Open Foundation Model for Generalist Humanoid Robots","work_id":"e2db69c7-ee8a-4cb7-a761-7b8de1dfcf97","shared_citers":32},{"title":"WorldVLA: Towards Autoregressive Action World Model","work_id":"d8c0c873-b2fc-44a5-a0c8-0d4a698783fb","shared_citers":29},{"title":"GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation","work_id":"843ab5eb-2815-4db8-b3bc-890b23fa5ffa","shared_citers":28},{"title":"Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations","work_id":"62dbe235-8473-4190-8686-17e7437de50f","shared_citers":27},{"title":"mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs","work_id":"cd5b191a-8f67-43d3-8816-0ade1b9a7c29","shared_citers":26},{"title":"arXiv preprint arXiv:2603.17240 , year=","work_id":"8736c864-7f93-4004-a1b5-06d98fb2c348","shared_citers":24},{"title":"Genie Envisioner: A Unified World Foundation Platform for Robotic Manipulation","work_id":"440ad435-44ba-4acd-9aeb-21dd3ee04835","shared_citers":24},{"title":"Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets","work_id":"c181dcf1-e774-4216-a30e-e55c3f3a766c","shared_citers":24},{"title":"Flow Matching for Generative Modeling","work_id":"6edb71c4-5d64-40af-a394-9757ea051a36","shared_citers":23},{"title":"RT-1: Robotics Transformer for Real-World Control at Scale","work_id":"e11bda85-8531-46bc-a07f-d0ade3643ab1","shared_citers":22},{"title":"Unified Video Action Model","work_id":"fb4cc512-d1d9-40f4-8854-d35950ad20b3","shared_citers":22},{"title":"Cosmos World Foundation Model Platform for Physical AI","work_id":"a2dba24c-318d-476a-8b21-4289c265810c","shared_citers":21},{"title":"X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model","work_id":"13faca8d-e96d-4e6c-a441-9f2683d11934","shared_citers":21},{"title":"Octo: An Open-Source Generalist Robot Policy","work_id":"f9ca0722-8855-48c3-a27a-0eefb7e19253","shared_citers":20},{"title":"World Simulation with Video Foundation Models for Physical AI","work_id":"1dc393b8-98c3-43bd-8ab0-25d7c2a9705b","shared_citers":20}],"time_series":[{"n":100,"year":2026}],"dependency_candidates":[{"n":1,"role":"baseline","polarity":"baseline","paper_title":"RoboEvolve: Co-Evolving Planner-Simulator for Robotic Manipulation with Limited Data","primary_cat":"cs.RO","context_text":"❸ Transition-level (D𝑇): Given a state pair(𝑓 1, 𝑓 𝑇 ) extracted from𝑉 +, P internalizes causality by predicting the underlying intent, whereP should correctly infer the plan𝜋∗ over misidentified intents to internalize causality. The planner is refined by minimizing the cumulative objectiveLNighttime(P): − ∑︁ 𝑘∈ {𝑃,𝑈,𝑇} 𝔼(c,𝜋+,𝜋− )∼D 𝑘 \u0014 log𝜎 \u0012 𝛽log P (𝜋+|c) Pref(𝜋+|c) −𝛽log P (𝜋− |c) Pref(𝜋− |c) \u0013\u0015 .(6) By jointly refiningS and P, nighttime learning converts unsuccessful daytime attempts into valuable supervisory signals, closing the loop between imagination and reality and preparing the system for the next cycle of exploration. 4.4. Dual-Phase Curriculum Evolution To ensure a stable and progressive transition from simple manipulations to complex behaviors,","citing_arxiv_id":"2605.13775"},{"n":1,"role":"method","polarity":"use_method","paper_title":"DexWorldModel: Causal Latent World Modeling towards Automated Learning of Embodied Tasks","primary_cat":"cs.CV","context_text":"Here, the inner-loop model f is formulated as a residual block wrapping a two-layer multi-layer perceptron: fT T Tmlp(x;W) =x+LN(MLP(x;W)) , where LN denotes Layer Normalization and the MLP utilizes a GELU activation with a 4× expansion factor. Once the weights are updated to Wt via gradient descent, the output hidden state is extracted utilizing the query projection: lt =f T T Tmlp(θQzt;W t)(12) 6 Additionally, naively inserting TTT layers into a pre-trained network would dramatically worsen its predictions at the beginning of fine-tuning, we gate TTT with a learned vector α∈R D following standard practice, as Wang et al. (2025): fT T T(zt;W t) =tanh(α)⊗f T T Tmlp(θQzt;W t) +z t,(13) where we initialize all values inαto0.1at the beginning of fine-tuning.","citing_arxiv_id":"2604.16484"},{"n":1,"role":"method","polarity":"use_method","paper_title":"World Action Models are Zero-shot Policies","primary_cat":"cs.RO","context_text":"Yi Ru Wang, Sangho Lee, et al. Molmoact: Action reasoning models that can reason in space.arXiv preprint arXiv:2508.07917, 2025. 4 [59] 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. 7 [60] Shuang Li, Yihuai Gao, Dorsa Sadigh, and Shuran Song. Unified video action model.arXiv preprint arXiv:2503.00200, 2025. 5, 7 [61] Yi Li, Yuquan Deng, Jesse Zhang, Joel Jang, Marius Memmel, Raymond Yu, Caelan Reed Garrett, Fabio Ramos, Dieter Fox, Anqi Li, et al. Hamster: Hierarchical action models for open-world robot manipulation. arXiv preprint arXiv:2502.","citing_arxiv_id":"2602.15922"}]},"authors":[{"id":"ed66f770-06e6-4062-a926-7cfe637ebfaf","orcid":null,"display_name":"Fei Han","source":"manual","import_confidence":0.72},{"id":"2c41eddc-1008-4bdb-8869-83d09b0857f9","orcid":null,"display_name":"Lin Li","source":"manual","import_confidence":0.72},{"id":"4d7436a1-3590-4224-ad86-8c841d4ae76c","orcid":null,"display_name":"Qihang Zhang","source":"manual","import_confidence":0.72},{"id":"92bb0d9d-ba84-478e-a71c-5b8c5db47a9a","orcid":null,"display_name":"Ruilin Wang","source":"manual","import_confidence":0.72},{"id":"63f1aa22-dbb4-4ae9-9918-d265d6aedc43","orcid":null,"display_name":"Shuai Yang","source":"manual","import_confidence":0.72},{"id":"5d01c36c-b444-4bef-b9e4-ddda018ff00b","orcid":null,"display_name":"Yiming Luo","source":"manual","import_confidence":0.72}]}}