{
  "id": 1403381,
  "title": "PACE-Bench: Benchmarking Physics Adaptation via Code Evolution in Dynamic Environments",
  "url": "https://urgent.news/2026/08/14/pace-bench-benchmarking-physics-adaptation-via-code-evolution-in",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-14T16:25:43.000Z",
  "source": {
    "name": "arXiv cs.AI",
    "slug": "arxiv-cs-ai",
    "url": "https://arxiv.org/abs/2608.14441v1"
  },
  "original_language": "en",
  "account": null,
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}