Learning to Outgrow a Theory: Experimental Discovery Beyond the Initial Hypothesis Space

2026-10-05

AI Summary

This study addresses the failure of scientific discovery caused by missing mechanisms under a fixed hypothesis space. We propose an experimental model class revision method that unifies mechanism expressibility and evidence collection into a single sequential decision problem. This approach jointly generates structural edits and diagnostic experiments, triggering theory revision through sequential evidence. To solve this formulation, we introduce a class-level distinguishability objective, anytime-valid sequential evidence, and reinforcement learning-based policies. Evaluated across 400 environments, our method achieves an exact recovery rate of 89.5%, significantly outperforming existing baselines while demonstrating strong transferability. These results establish a new paradigm for automated scientific discovery.

Abstract

Scientific discovery systems typically optimize experiments within a fixed hypothesis space. This creates a failure mode when all available candidates omit the same missing mechanism: candidate disagreement can collapse even while the model class is systematically wrong. We formulate experimental model-class revision, in which a discovery policy jointly proposes a structural edit and a diagnostic experiment that tests whether that edit is necessary. The method couples a class-level distinguishability objective, in which one shared parameterization must explain all selected experiments, with anytime-valid sequential evidence that triggers structural revision only after the current class is rejected. On 400 held-out controlled dynamical environments, the joint policy reaches 89.5% exact recovery with a budget of 32 real experiments, improving the strongest matched baseline by 10.0 percentage points while requiring fewer executed experiments and candidate fits. The learned revision-experiment pairing transfers across unseen mechanism combinations, held-out but expressible primitives, parameter extrapolation, and shifted experiment costs; when the true mechanism is outside the edit grammar, it detects library insufficiency in 88% of cases with a 5.5% false-support rate. Revision gains also transfer to ODEBench and ODEBase model-library tasks, as well as DiscoverPhysics worlds. These results support a view of scientific discovery in which deciding what mechanisms a theory should make expressible and where to collect evidence are treated as a single sequential decision problem.

Problem

• Scientific Discovery
• Hypothesis Space Revision
• Model-class Revision
• Experimental Design
• Dynamical Systems

Innovation

• Scientific Discovery
• Hypothesis Space Revision
• Sequential Decision Making
• Experimental Design
• Model-Class Distinguishability