TAFER: Auditing Selective Repair Under Evidence Shift

Language-model outputs can match task targets without being supported by the available evidence. We introduce TAFER, a framework for auditing selective repair under evidence shift. TAFER separates answer-text correctness, answer-or-abstain decisions, and support assessments tied to explicit reference rules. In public fixed-pool comparisons, its deterministic selector chooses the lowest recorded-cost candidate that passes structural citation checks, or abstains when none passes. Shared-pool baselines distinguish feasibility filtering from ranking, while matched evidence interventions examine how removing cited or uncited evidence changes answering behavior. Finite-pool accounting separates candidate unavailability, certificate exclusion, and selection error; risk-coverage analysis makes abstention costs explicit. Results are heterogeneous: changes in a support proxy do not alone establish better task utility, and filtering gains need not imply ranking gains. Behavioral diagnostics distinguish increased abstention from continued answering after evidence removal. Natural-language intervention support uses operational verifier judgments, while candidate-pool support uses a gold citation-ID relation. Neither is treated as human support truth. These analyses characterize selective repair within tested protocols without conflating correctness, support, and abstention.