From Knowledge to Decisions: Repairing Evidence Seeking after Full-Evidence Adaptation

Full-evidence adaptation can transfer non-uniformly across evidence-conditioned competence and interactive evidence acquisition. On HotpotQA, it raises Oracle exact match from 58.6% to 68.0% but lowers Interactive exact match from 40.6% to 32.0%. Restoring inherited Base acquisition while retaining the adapted reader raises Interactive EM to 46.1%, showing that useful reader competence survives independently of the degraded adapted acquisition behavior. We call this a competence–policy transfer gap: adaptation improves evidence-conditioned competence, but that competence is not reliably translated into the decisions required to acquire useful evidence. Functional decoupling does not make inherited acquisition optimal. K2D-ALIGN (Knowledge-to-Decision Alignment) addresses the residual decision problem: when should retained reader competence justify a beneficial deviation from inherited evidence seeking? Matched continuation identifies Base-relative opportunities for learning root proposals; deployment-time admission authorizes replacement or executes exact Base fallback. Admission compares candidate and Base terminal shadow continuations before execution, trading additional model-side inference for conservative replacement. On HotpotQA, K2D-ALIGN improves Interactive EM from 46.1% to 52.3%; under matched initialization, Base-relative supervision contributes a further 3.9 points over the zero-update proposal. On 872 held-out cases, the frozen system gains 2.3 EM points over Base+FE and exceeds an approximately compute-matched Base Best-of-2 control by 3.1 points. Across four evidence-seeking benchmarks, it improves macro-average EM/F1 from 30.7/41.5 to 32.6/43.4. These results connect functional diagnosis to localized acquisition repair while preserving the reader competence that already transferred.