Fluent but subtly wrong mathematical solutions can contaminate training, evaluation, and reward-labeling data. We present AEGIS-D (Assessment- and Evidence-Gated Inspection System via Debate), a precision-first multi-agent pipeline for blind verification of competition-level mathematics. Five same-model, role-prompted agents exchange structured arguments, while an assessment gate accepts a candidate only when at least three report positive confirming evidence rather than mere non-refutation. On 195 hard-to-verify variants from 58 IMO/USAMO/Putnam-level problems, a single judge achieves 55.1% precision; AEGIS-D achieves 92.5% precision and 63.8% recall, reducing false positives from 24.7 to 3.0 per run (8.2-fold). A repeated two-by-two factorial associates role prompting with the largest observed false-positive reduction and finds a smaller conditional benefit from gating. Paired round and threshold analyses expose the cost-precision trade-off, and single-run transfers to Omni-MATH and OlymMATH provide preliminary evidence beyond the constructed stress test.