PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments
Self-evolving agents improve future behavior from interaction experience, yet existing evaluations typically optimize under fixed execution conditions and do not test recovery after those conditions change. To address this gap, we introduce PACE-Bench (Physics Adaptation via Code Evolution), a simulator-grounded benchmark of 144 source-to-target adaptation pairs across six physics domains. Each pair links a source environment to a mutated target environment with the same goal and interface. A code-driven design that succeeds in the source fails in the target, where agents must iteratively adap
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 57%ScarfBench: Benchmarking AI Agents for Enterprise Java Framework Migration →
- FuzzyOverlapping authors or contributors · 62%TauricResearch/TradingAgents →
“Shared author/contributor keys: xiao”
- LinkedLinked via arxiv author · 85%Yuhao Zhang →
“PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments”
- LinkedLinked via arxiv author · 85%Bingxiang He →
“PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments”
- LinkedLinked via arxiv author · 85%Zecong Tang →
“PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments”
- LinkedLinked via arxiv author · 85%Chaojun Xiao →
“PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments”