PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments

Self-improving AI agents are usually tested under fixed conditions, but real-world deployment demands adapting when the environment itself changes. PACE-Bench introduces 144 source-to-target adaptation challenges across six physics domains, forcing agents to iteratively rewrite working code when the underlying physics is mutated. Testing ten methods reveals that grounded, feedback-driven revision beats memory-based or unguided search, and that even knowing the exact physical change doesn't guarantee success --- redesigning the approach matters more than fine-tuning parameters. This benchmark is valuable for evaluating robust, adaptable AI agents for robotics and simulation. Authors: Yuhao Zhan, Bingxiang He, Zecong Tang, Chaojun Xiao Paper: https://arxiv.org/abs/2608.14441v1