When Revision Backfires: Consequence-Aware Verification for Multi-Role LLM Reasoning

Verifier-guided reasoning is usually drawn as a monotone pipeline: a solver proposes, a verifier checks, a reviser repairs. That picture hides an asymmetry. Sending an answer back for revision is an action, not a prediction: replacing a correct answer with a wrong revision turns a point into zero, whereas keeping a wrong one merely preserves the status quo. Across 18 long multi-role math runs, unnecessary revision, not verdict error in general, is the failure most strongly tied to lost end-to-end value, negative within every run. We recast verification as consequence-aware control and introduce Keeper, a preserve-first policy family that routes only what is worth rewriting and restores the solver answer when evidence for replacing it is weak. On a held-out controller panel, Keeper improves three outcomes by 12.3–14.9 points and turns 17 of 19 repair opportunities into useful actions while degrading none. With the first-pass solver and reviser fixed, it beats a shared checker in all six comparisons while spending 13.5% fewer response tokens than always revising. A same-scale 7B preserve-first student, frozen and preregistered, improves all 12 checkpoints trained after it was frozen and 29 of 35 workflows of a problem-disjoint split with six unseen task families, blocking 99% of harmful revisions; a same-data variant improves every one of those 35 workflows over its gate. Score verifiers by the consequences of the routes they trigger, not by verdicts in isolation.