ECPO: Evidence-Coupled Policy Optimization for Verifier-Audited Candidate Ranking

Abstract:Ranking systems used for decision support should expose evidence that can be audited independently of the model's ranking scores. We study a closed-roster setting in which a policy jointly emits a Top-$K$ ranking and candidate-linked, span-grounded evidence bundles. Evidence-Coupled Policy Optimization (ECPO) combines listwise utility, certificate validity, and an evidence-cycle reward that removes answer-bearing fields before deterministic recovery. On MAVEN-ERE and RAMS, ECPO improves verifier-audited traceability, rank-blind recovery, and human comparative support relative to matched controls in the tested provenance settings. The gains persist under held-out evaluators, other 8B/14B backbones, and a HotpotQA answer-ranking interface. Ordinary NDCG and runtime remain explicit trade-offs.

42 pages, 1 figure. Revised manuscript; updated authorship and corresponding author information

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2605.21993 [cs.AI]

(or arXiv:2605.21993v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2605.21993 Focus to learn more arXiv-issued DOI via DataCite

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