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

The paper introduces an experimental model‑class revision framework that jointly proposes structural edits to a hypothesis space and diagnostic experiments to test those edits. By coupling a class‑level distinguishability objective with anytime‑valid sequential evidence, the method only revises the model class after the current one is rejected. On 400 controlled dynamical environments, the approach achieves 89.5% exact recovery with 32 experiments, outperforming baselines and transferring well to unseen mechanisms, library insufficiency detection, and other benchmark tasks.

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.