aiqbench analysis
PhysCodeGym establishes a rigorous benchmark for coding agents tasked with generating physically verifiable 4D reconstructions from video data, addressing the critical gap between visual fidelity and physical consistency in simulation generation. The study reveals inherent trade-offs between artifact quality and task completion rates when frontier models attempt to write and refine Blender programs using simulation feedback loops. This work advances the field of embodied AI by providing a standardized framework to evaluate how well autonomous agents can translate observational data into executable, physics-grounded digital twins.
PhysCodeGym: Benchmarking Coding Agents for Physically Verifiable 4D Reconstruction
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 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 https://github.com/ShaoShuai0605/PhysCodeGym.
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.
Artificial Intelligence (cs.AI)
arXiv:2610.17288 [cs.AI]
(or arXiv:2610.17288v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2610.17288 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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