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arXiv AI By Kautik Mandve, Dileepa Fernando

Explore, Then Commit: Measurement-Efficient Scientific Law Discovery with Language Models

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The paper presents an explore‑then‑commit protocol that uses a large language model to generate hypotheses, a programmatic planner to collect measurements, and a fresh prompt to synthesize a scientific law from fixed observations. In 576 NewtonBench trials across 12 physics modules, the protocol—especially when interpreter‑enabled planners are used—reduces the number of measurements needed and improves root‑mean‑squared logarithmic error for both GPT‑4.1‑mini and GPT‑4.1. The study demonstrates measurement savings in every module, though it notes that the causal components and generalization beyond noiseless direct‑equation tasks remain unresolved.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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