arXiv cs.AI· 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·· 3 天前AI 评分31
PhysCodeGym：面向物理可验证 4D 重建的编程智能体基准
PhysCodeGym: Benchmarking Coding Agents for Physically Verifiable 4D Reconstruction
AI 导读
研究者推出 PhysCodeGym，一个面向物理可验证 4D 重建的编程智能体基准，包含 156 项任务，覆盖刚体、液体、可变形体和风驱动运动。智能体需自主查看视频、编写并运行 Blender 程序，并借助仿真反馈修正场景，评估由 6 项指标结合规则测量与评分判断构成。
正文 · 原文
Authors: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
Abstract:Building physical simulations from video requires coding agents to translate observations into executable models and refine them through tool use. We introduce PhysCodeGym, a benchmark for physically verifiable 4D reconstruction with $156$ tasks covering rigid-body, liquid, deformable, and wind-driven motion. Agents autonomously inspect videos, write and run Blender programs, and revise their scenes using simulation feedback. Six metrics combine rule-based measurements with rubric-based judgments to assess visual and physical reconstruction quality. Program inspection, scene measurements, and counterfactual interventions test physical consistency under original and modified conditions. Experiments with frontier coding agents reveal a tradeoff between artifact quality and completion across reasoning-effort settings, while harness comparisons and agent traces link verification behavior with reconstruction performance. Case studies show that matching visible objects and events can coexist with physical defects. Counterfactual tests further expose latent contact and support failures in reconstructions that pass ordinary physical checks, highlighting the gap between reproducing observations and building reliable physical models. Code is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17288 [cs.AI] |
| (or arXiv:2610.17288v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17288 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Shuai Shao [view email]
[v1]
Mon, 12 Oct 2026 18:44:55 UTC (33,237 KB)
来源：arXiv cs.AI · arxiv.org