# ChronoAgentic: A Code-based Multi-Agent World Simulator for Physically Grounded Simulation Construction

arXiv: https://arxiv.org/abs/2605.14398 · HTML: https://mlsanity.com/paper/2605.14398/

## 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.

## Placed in

- World models / [Video-generation world models and simulators](https://mlsanity.com/wm/video-generation-world-models-and-simulators/)

## Builds on (most cited first, 20 of 20)

- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Mastering Diverse Domains through World Models](https://mlsanity.com/paper/2301.04104/) (2023)
- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025)
- [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025)
- [Open-Sora: Democratizing Efficient Video Production for All](https://mlsanity.com/paper/2412.20404/) (2024)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [LTX-Video: Realtime Video Latent Diffusion](https://mlsanity.com/paper/2501.00103/) (2024)
- [MagicVideo: Efficient Video Generation With Latent Diffusion Models](https://mlsanity.com/paper/2211.11018/) (2022)
- [Open-Sora Plan: Open-Source Large Video Generation Model](https://mlsanity.com/paper/2412.00131/) (2024)
- [SAM 3D: 3Dfy Anything in Images](https://mlsanity.com/paper/2511.16624/) (2025)
- [World Simulation with Video Foundation Models for Physical AI](https://mlsanity.com/paper/2511.00062/) (2025)
- [Embodied AI Agents: Modeling the World](https://mlsanity.com/paper/2506.22355/) (2025)
- [SAGE: Scalable Agentic 3D Scene Generation for Embodied AI](https://mlsanity.com/paper/2602.10116/) (2026)
- [EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence](https://mlsanity.com/paper/2506.10600/) (2025)
- [Robot Learning from a Physical World Model](https://mlsanity.com/paper/2511.07416/) (2025)
- [Simulating the Visual World with Artificial Intelligence: A Roadmap](https://mlsanity.com/paper/2511.08585/) (2025)
- [SimWorld: An Open-ended Realistic Simulator for Autonomous Agents in Physical and Social Worlds](https://mlsanity.com/paper/2512.01078/) (2025)
- [PhysWorld: From Real Videos to World Models of Deformable Objects via Physics-Aware Demonstration Synthesis](https://mlsanity.com/paper/2510.21447/) (2025)
- [PhysCodeBench: Benchmarking Physics-Aware Symbolic Simulation of 3D Scenes via Self-Corrective Multi-Agent Refinement](https://mlsanity.com/paper/2604.23580/) (2026)

## Built on by (most cited first, 2 of 2)

- [Code Plans, Diffusion Renders: Open-Ended Generative World Modeling](https://mlsanity.com/paper/2609.26458/) (2026)
- [WorldAgent: Verification-Guided Agentic Physical World Construction](https://mlsanity.com/paper/2609.33208/) (2026)

## 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/.