Null Controls Are Necessary but Not Sufficient: Evaluating Acceptance Guards for Scientific Discovery and Interpretability

The paper introduces four executable operations—identifying redundancies (L1), coarse‑graining dynamics (L2), transferring structure (L3), and inferring missing quantities from conservation imbalances (L4)—to modify physical descriptions in automated scientific discovery systems. It evaluates these operations alongside three interpretability settings (null acceptance, positive acceptance, consequence agreement) on chaotic systems, controlled benchmarks, language and protein models, and sparse autoencoders, revealing that descriptive tests can accept empty candidates and accepted candidates may lack verified consequences. The study demonstrates that reliable automated discovery must assess both what a test accepts and the scientific consequences it establishes.

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