mlsanityData to 7 Oct 2026

PhiZero: A World Model Built Around Physical Language

arXiv 2607.28624 ↗First posted 30 Jul 2026cs.CVcited by 2

On the map: World models → Video-generation world models and simulators

Abstract (arXiv)

We introduce PhiZero, a physical world model built around physical language, a compact discrete representation of world-state transitions. Existing physical world models typically predict future videos directly in pixel space, leaving the underlying world dynamics implicit within high-dimensional visual predictors. Motivated by humans' ability to abstract predictive structure from visual experience and organize it in natural language for explicit reasoning, we learn physical language from in-the-wild videos through self-supervision and use it to explicitly reason about how the physical world evolves. Accordingly, PhiZero adopts a reason-then-render paradigm: it first infers future world evolution as a physical-language sequence and then renders the inferred transitions into videos. Extensive experiments across generation and understanding benchmarks validate the ability of PhiZero to model physically coherent world evolution. We further show its potential for realistic and interactive world modeling, fine-grained action-conditioned simulation, and zero-shot motion transfer.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Video-generation world models and simulators, one of 11 directions in World models. Builds on 93 of its 105 references that are in the map. First posted on arXiv 30 Jul 2026.

Builds on

93 of 105 references.

Closest ancestors · 9

Same direction, newest first

  1. 2026Cosmos 3: Omnimodal World Models for Physical AI1 Jun
  2. 2026YoCausal: How Far is Video Generation from World Model? A Causality Perspective28 May
  3. 2025Astra: General Interactive World Model with Autoregressive Denoising9 Dec
  4. 2025WorldModelBench: Judging Video Generation Models As World Models28 Feb
  5. 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer12 Aug
  6. 2024Pandora: Towards General World Model with Natural Language Actions and Video States12 Jun
  7. 2024Genie: Generative Interactive Environments23 Feb
  8. 2024WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens18 Jan
  9. 2023Learning Interactive Real-World Simulators9 Oct

Other references · 84

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2023BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Modelscited by 6,069
  3. 2025Qwen2.5-VL Technical Reportcited by 5,418
  4. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  5. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
  6. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
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  8. 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,597
  9. 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuningcited by 1,418
  10. 2023LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learningcited by 1,338
  11. 2017The "something something" video database for learning and evaluating visual common sensecited by 1,092
  12. 2025Cosmos World Foundation Model Platform for Physical AIcited by 833
38 more
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  2. 2024Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Thinkcited by 666
  3. 2023ModelScope Text-to-Video Technical Reportcited by 651
  4. 2023Language Model Beats Diffusion -- Tokenizer is Key to Visual Generationcited by 633
  5. 2023VideoMAE V2: Scaling Video Masked Autoencoders with Dual Maskingcited by 516
  6. 2019CLEVRER: CoLlision Events for Video REpresentation and Reasoningcited by 475
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  8. 2025AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systemscited by 390
  9. 2025UniVLA: Learning to Act Anywhere with Task-centric Latent Actionscited by 386
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  11. 2024Latent Action Pretraining from Videoscited by 302
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  13. 2025Motus: A Unified Latent Action World Modelcited by 231
  14. 2018Moments in Time Dataset: one million videos for event understandingcited by 215
  15. 2026LTX-2: Efficient Joint Audio-Visual Foundation Modelcited by 213
  16. 2025World Simulation with Video Foundation Models for Physical AIcited by 183
  17. 2024PhysGen: Rigid-Body Physics-Grounded Image-to-Video Generationcited by 169
  18. 2021Physion: Evaluating Physical Prediction from Vision in Humans and Machinescited by 145
  19. 2024iVideoGPT: Interactive VideoGPTs are Scalable World Modelscited by 136
  20. 2022UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormercited by 116
  21. 2023Learning to Act without Actionscited by 116
  22. 2025AdaWorld: Learning Adaptable World Models with Latent Actionscited by 108
  23. 2024Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenizationcited by 98
  24. 2025VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Modelscited by 88
  25. 2025WISA: World Simulator Assistant for Physics-Aware Text-to-Video Generationcited by 87
  26. 2024Moto: Latent Motion Token as the Bridging Language for Learning Robot Manipulation from Videoscited by 65
  27. 2025Sekai: A Video Dataset towards World Explorationcited by 64
  28. 2022ComPhy: Compositional Physical Reasoning of Objects and Events from Videoscited by 61
  29. 2025Visual Imitation Enables Contextual Humanoid Controlcited by 57
  30. 2023Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Propertiescited by 53
  31. 2025UltraVideo: High-Quality UHD Video Dataset with Comprehensive Captionscited by 52
  32. 2025VLIPP: Towards Physically Plausible Video Generation with Vision and Language Informed Physical Priorcited by 49
  33. 2025IntPhys 2: Benchmarking Intuitive Physics Understanding In Complex Synthetic Environmentscited by 49
  34. 2024MotionCraft: Physics-based Zero-Shot Video Generationcited by 46
  35. 2023Pre-training Contextualized World Models with In-the-wild Videos for Reinforcement Learningcited by 45
  36. 2025VideoWorld: Exploring Knowledge Learning from Unlabeled Videoscited by 42
  37. 2026Learning Latent Action World Models In The Wildcited by 42
  38. 2024Efficient Video Diffusion Models via Content-Frame Motion-Latent Decompositioncited by 41

50 most cited of 84 All 84 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

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  3. 2026World Models Meet Language Models: On the Complementarity of Concrete and Abstract Reasoning
  4. 2026Emergent Semantic Representations in World Models through Physical Interaction without Linguistic Supervision
  5. 2026Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends
  6. 2026Thinking in Dynamics: How Multimodal Large Language Models Perceive, Track, and Reason Dynamics in Physical 4D World
  7. 2026X-Foresight: A Joint Vision-Action Causal Forecasting Network via Predictive World Modeling
  8. 2025Simulating the Visual World with Artificial Intelligence: A Roadmap