arXiv:2607. 16355v1 Announce Type: cross Abstract: Recent advances in physics-grounded video generation leverage physics simulation as a physical prior to guide video synthesis toward physically plausible outcomes.
By Qirui Li, Jinkun Hao, Yibo Li, Ran Yi, Paul L. Rosin, Yu-Kun Lai
PhyEvolve is a multi‑agent framework that improves physics‑aware video generation by enhancing natural‑language prompts through structured state‑evolution planning. It employs three agents—a Director, a Physical Evolver, and a Composer—to ground scenes, identify physical mechanisms, and construct causally linked state transitions that guide existing generators without retraining. Experiments on four major benchmarks show that PhyEvolve outperforms the prior agentic baseline by an average of 7.9 percentage points, demonstrating that explicit causal planning boosts physical consistency and state continuity in generated videos.
By Yang-Hao Zhou, Lixing Tan, Zhiqi Li, Guang Yang, Xiao Yang
arXiv:2607. 10190v1 Announce Type: cross Abstract: Video-language models (VLMs) have achieved remarkable performance on video understanding and visual question answering, yet they remain unreliable in reasoning about physical plausibility, where understanding object interactions, causal dynamics, and fundamental physical principles is essential.
By Wenyuan Wang, Lianyu Hu, Hao Wang, Yang Liu
PhyEvolve is a multi-agent framework that improves physics-aware video generation by enhancing natural-language prompts through structured state-evolution planning. It employs three agents—a Director, a Physical Evolver, and a Composer—to ground scenes, identify physical mechanisms, and construct causally linked state transitions that guide video generation. The method works with existing generators via prompt replacement and achieves state‑of‑the‑art performance, outperforming the prior agentic baseline by an average of 7.9 percentage points on four major benchmarks.
arXiv:2610.17288v1 Announce Type: new
Abstract: Building physical simulations from video requires coding agents to translate observations into executable models and refine them through tool use. We i...
By Shuai Shao, Jinchen He, Cunxin Fan, Zihan Guo, Shijian Wang, Qihan Ren, Kangning Zhang, Zunnan Xu, Haoyuan Li, Hao Wang, Jianghao Lin, Weiwen Liu, Weinan Zhang
arXiv:2610.34921v1 Announce Type: new
Abstract: Physical reasoning underpins how embodied systems understand and act in the physical world, yet vision-language models (VLMs) remain unreliable on ques...
By Jinyuan Hu, Yilei Jiang, Chen Tang, Minghong Cai, Xinran Liu, Xiangyu Yue