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 access. Across 576 NewtonBench trials, we compare eight configurations on 12 physics modules using GPT-4.1-mini and a medium-difficulty GPT-4.1 replication. On medium tasks, interpreter-enabled planners use 8.6 versus 22.5 measurements per trial for GPT-4.1-mini and 8.9 versus 43.0 for GPT-4.1. Their mean magnitude-based root-mean-squared logarithmic error falls from 2.514 to 0.202 and from 0.626 to 0.149, respectively. An additional audit retains incomplete and invalid submissions in a coverage-sensitive analysis. Observed symbolic-accuracy gains are less consistent across modules, and random acquisition is competitive with disagreement scoring. Measurement savings occur in every module, but unequal batch constraints prevent attributing them solely to acquisition quality. These results support the complete protocol as a promising measurement-efficient configuration, while leaving its causal components and generalization beyond noiseless direct-equation tasks unresolved.
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Cite this article
APA 7
Mandve, K., & Fernando, D. (2026). Explore, Then Commit: Measurement-Efficient Scientific Law Discovery with Language Models. https://omanscience.com/en/articles/explore-then-commit-measurement-efficient-scientific-law-discovery-with-language-models
MLA 9
Mandve, Kautik, and Dileepa Fernando. "Explore, Then Commit: Measurement-Efficient Scientific Law Discovery with Language Models." https://omanscience.com/en/articles/explore-then-commit-measurement-efficient-scientific-law-discovery-with-language-models.
Chicago (author–date)
Mandve, Kautik, and Dileepa Fernando. 2026. "Explore, Then Commit: Measurement-Efficient Scientific Law Discovery with Language Models." https://omanscience.com/en/articles/explore-then-commit-measurement-efficient-scientific-law-discovery-with-language-models.
Harvard
Mandve, K. and Fernando, D. (2026) 'Explore, Then Commit: Measurement-Efficient Scientific Law Discovery with Language Models', Available at: https://omanscience.com/en/articles/explore-then-commit-measurement-efficient-scientific-law-discovery-with-language-models.
Vancouver
Mandve K, Fernando D. Explore, Then Commit: Measurement-Efficient Scientific Law Discovery with Language Models. https://omanscience.com/en/articles/explore-then-commit-measurement-efficient-scientific-law-discovery-with-language-models
IEEE
K. Mandve, and D. Fernando, "Explore, Then Commit: Measurement-Efficient Scientific Law Discovery with Language Models," https://omanscience.com/en/articles/explore-then-commit-measurement-efficient-scientific-law-discovery-with-language-models.