Run parallel specialist agents plus a verification step to cut code-review false positives ~26%

Microsoft Research·medium signal

Microsoft Research's CORE pattern uses an LLM proposer to fix code-quality issues and a separate ranker LLM that cut false positives by 25.8%; its revisions passed static analysis in 76.8% of Java files and 59.2% of Python files cleared both a tool and a human reviewer. The generalizable shape (echoed across 2026 multi-agent validation writeups): multiple agents each hunt one class of issue in parallel, then a dedicated verification agent checks each candidate against actual code behavior before it's surfaced. Builder move: don't trust a single agent's review — add an adversarial verifier whose only job is to refute findings, and you suppress the noise that kills trust in AI review.