Evidence-Gated Research：自适应搜索中统计可控的模型采纳

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Abstract:Adaptive model search is path dependent: once a challenger is adopted, it becomes the reference from which later candidates are generated. A statistically unsupported replacement can therefore alter hypotheses that have not yet been proposed. We introduce Evidence-Gated Research (EGR), a statistical adoption layer for moving-incumbent search. EGR freezes each challenger before decision evidence is revealed, builds anytime-valid evidence across a predeclared set of environments, routes evidence predictably toward unresolved components, composes a persistent candidate e-value, and passes that e-value to an online controller. Under explicit conditional-validity and predictability conditions, the resulting procedure controls false discovery rate for the declared all-environment adoption target even though earlier adoptions change later challengers. In a 5,000-trajectory closed-loop benchmark, development-only e-LOND attains persistent FDR 0.621, whereas no persistent false-adoption path is observed for the audited EGR variants in that finite run. In matched replay over 600 challenger--incumbent pairs, Stagewise EGR preserves fixed-anytime alternative crossing decisions while using 56.1% less decision evidence at the representative threshold. A three-environment public-data study and a 40,000-sample controlled neural benchmark reproduce the evidence-efficiency pattern. These results identify model replacement as a distinct statistical control point in adaptive model development.

Comments:	17 pages, 4 figures. Preprint
Subjects:	Machine Learning (cs.LG)
Cite as:	arXiv:2610.01751 [cs.LG]
	(or arXiv:2610.01751v1 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2610.01751 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yifan Guo [view email]
[v1] Thu, 1 Oct 2026 14:17:55 UTC (380 KB)