LongChang：大語言模型能否透過自由探索發現隱藏的物理定律？
Can LLMs Uncover Hidden Physical Laws through Free Exploration?
LongChang 評估大語言模型能否透過模擬實驗，自主發現經改變的物理定律；12 個模型中，只有 GPT-6-Astra 有限度地成功。GPT-6-Astra 的發現率為 16.7%，其餘 11 個模型均為 0%；探索紀錄亦顯示，仍有異常未獲調查，對改變後物理定律的識別亦不完整。
Published on Oct 27
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Abstract
LongChang evaluates whether LLMs can autonomously discover altered physical laws through simulated experimentation, finding that only one model achieved limited success.
Scientific discovery often begins with an unexpected observation that prompts a new research question. In a world with altered physical laws, can large language models (LLMs) notice unexpected behavior, such as heavier objects falling more slowly, and discover the law through experiments without being told what to investigate? To evaluate this ability, we introduce LongChang, an automated framework that creates simulated worlds with altered physics, where one or more laws differ from ordinary physics. The evaluated LLMs act as explorers, manipulating objects and building tools to investigate their own questions without research tasks or hints about these laws. Separate LLM agents generate and review scenes, review tool designs, and evaluate reports for correctness and experimental support using the altered physical laws and exploration records. These records guide scene evolution for further exploration, allowing the full cycle to run without human intervention. Among 12 LLMs, GPT-6-Astra achieves a discovery rate of 16.7%, while the other 11 score 0%. The exploration records reveal uninvestigated anomalies and only partial identification of the altered physical laws.
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來源：HuggingFace Daily Papers（社區熱門論文） · huggingface.co