arXiv AI
Aug 21

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

arXiv:2605. 14398v3 Announce Type: replace Abstract: 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.

By Hongyu Wang, Jingquan Wang, Ashvin Anilkumar, Bocheng Zou, Radu Serban, Dan Negrut
arXiv AI
Jun 2

Coding Agent Is Good As World Simulator

arXiv:2605. 14398v2 Announce Type: replace Abstract: World models have emerged as a powerful paradigm for building interactive simulation environments, with recent video-based approaches demonstrating impressive progress in generating visually plausible dynamics.

By Hongyu Wang, Jingquan Wang, Bocheng Zou, Radu Serban, Dan Negrut
arXiv AI
Sep 25

Coding Agents for Generalized Task and Motion Planning Problems

The paper investigates whether large language model–based coding agents can automatically synthesize programs that solve generalized task and motion planning (TAMP) problems across diverse instances. Using Claude Code and Codex, the authors evaluate 980 generated programs on 100 held‑out environments from KinDER and PDDLStream, achieving mean success rates between 56 % and 95 %—higher than hand‑engineered planners and other baselines—while requiring an order of magnitude less computation per instance. The study demonstrates that coding agents can calibrate physical models, test edge cases, and refine strategies, suggesting they are a strong baseline for generalized TAMP.

By Matteo Merler, Bowen Li, Josh Roy, Yichao Liang, Qianwei Wang, Yixuan Huang, Tom Silver