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

Excerpt

arXiv:2610.07620v1 Announce Type: cross Abstract: Scientific law discovery requires selecting measurements and converting evidence into a governing equation. We evaluate an explore-then-commit protocol in which a large language model proposes hypotheses, a programmatic planner gathers measurements, and a fresh prompt synthesizes the final law from fixed observations. The protocol combines structured probes, automatic numerical diagnostics, restricted measurement batches, and optional interpreter