PLAIT: From Hallucination Scores to Parent-Preserving Audit Plans

Hallucination detectors estimate what may be wrong; human auditing must decide what is worth checking. Under a fixed review budget, these decisions are coupled: once a response and its evidence are open, additional claims become cheaper to inspect. We introduce PLAIT, which converts detector outputs into audit plans that account for these shared costs. A frozen detector, the parent, supplies initial values; generator hidden states learn corrections with a plan-regret objective; and an exact planner jointly selects response, claim, and span actions. A configurable constraint preserves a specified fraction of the parent plan's estimated value, and a separate verifier recomputes feasibility. Across four generators, claim-level PLAIT improves fixed-time recall by 3.54 percentage points over validation-selected exact score fusion. Gains persist after parent replacement and source-disjoint transfer. In a randomized study with 96 participants, PLAIT increases correctly identified unsupported characters per minute by 12.47% over HLA-RICO. Detection quality and audit utility are therefore distinct: useful scores must support good review decisions.