ChronoAgentic: A Code-based Multi-Agent World Simulator for Physically Grounded Simulation Construction
On the map: World models → Video-generation world models and simulators
Abstract (arXiv)
Video-based world models generate visually plausible rollouts, but since they infer dynamics in latent states, they enforce no explicit physical constraints: contacts drift, shapes distort, and motion loses consistency. We present ChronoAgentic, a multi-agent framework that instead constructs the world as executable simulation code. The plan agent converts the natural-language prompt into a structured scene plan that the user can inspect and approve. The code agent implements the plan as an executable PyChrono program, grounded in a curated skill library, a generative 3D asset pipeline, and retrieval over the simulator source. After execution, the visual-analysis agent describes the rendered rollout, while deterministic physics checks scan the simulated trajectories for anomalies. The review agent evaluates this execution evidence, and the code agent iteratively repairs the program until it satisfies the plan objectives and physical constraints. On a suite of 80 demos selected from the PhyWorldBench benchmark, ChronoAgentic satisfies the benchmark's full correctness criterion--semantic adherence and physical correctness judged jointly---on 82.5% of demos, against 52.5% for the strongest of ten text-to-video models, scored under the same criterion on their officially released benchmark videos. The same construction loop extends to interactive use, including a live ROS driving environment in a generated city. The project page is available at https://uwsbel.github.io/chrono-agentic-website/.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Video-generation world models and simulators, one of 11 directions in World models. Builds on 20 of its 58 references that are in the map. First posted on arXiv 14 May 2026.
Builds on
20 of 58 references.
Closest ancestors · 2
Same direction, newest first
- 2025Simulating the Visual World with Artificial Intelligence: A Roadmap11 Nov
- 2024Open-Sora: Democratizing Efficient Video Production for All29 Dec
Other references · 18
- 2025Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilitiescited by 3,796
- 2024HunyuanVideo: A Systematic Framework For Large Video Generative Modelscited by 1,660
- 2023Mastering Diverse Domains through World Modelscited by 1,253
- 2025Cosmos World Foundation Model Platform for Physical AIcited by 864
- 2025V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planningcited by 809
- 2023GAIA-1: A Generative World Model for Autonomous Drivingcited by 637
- 2024LTX-Video: Realtime Video Latent Diffusioncited by 581
- 2022MagicVideo: Efficient Video Generation With Latent Diffusion Modelscited by 465
- 2024Open-Sora Plan: Open-Source Large Video Generation Modelcited by 296
- 2025SAM 3D: 3Dfy Anything in Imagescited by 247
- 2025World Simulation with Video Foundation Models for Physical AIcited by 200
- 2025Embodied AI Agents: Modeling the Worldcited by 61
6 more
- 2026SAGE: Scalable Agentic 3D Scene Generation for Embodied AIcited by 50
- 2025EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligencecited by 36
- 2025Robot Learning from a Physical World Modelcited by 20
- 2025SimWorld: An Open-ended Realistic Simulator for Autonomous Agents in Physical and Social Worldscited by 12
- 2025PhysWorld: From Real Videos to World Models of Deformable Objects via Physics-Aware Demonstration Synthesiscited by 10
- 2026PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinementcited by 10
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026Code2Worlds-2: Physically Plausible Agent Harness for Embodied World Simulation
- 2026SimWorlds: A Multi-Agent System for Dynamic 3D Scene Creation
- 2026WorldSolver: Can LLM Agents Simulate the Physical Dynamics via Solver Generation?
- 2026A Physics-Grounded Benchmark for Multi-Agent Dynamics in World Models
- 2026Authoring for Living Worlds: Tool-Constrained LLM Agents for Executable Multi-Actor Scenarios
- 2026Code2Worlds: Empowering Coding LLMs for 4D World Generation
- 2026ExeWorldBench: Benchmarking Code-Based Executable 3D World Modeling
- 2026GS-Agent: Creating 4D Physical Worlds With Generative Simulation