multi-model-adversarial-reviewlisted

# Multi-model adversarial review A single model reviewing its own work is not an adversarial review, even if you ask it to "be critical." One model's blind spots are usually its reviewer's blind spots too. The pattern that actually catches things: run **two models from different vendors or of different sizes**, then **reconcile** their findings instead of concatenating them. ## Why this works Different models (or the same vendor's different model tiers) are trained differently enough that their errors are only partially correlated. Agreement between them is a real confidence signal. Disagreement is where the interesting findings live. But they are still both language models trained on overlapping data: agreement reduces *stochastic* misses, but it does not clear a *shared* blind spot. For anything safety- or correctness-critical, pair this with at least one non-LLM check (run it, compute it, grep for it). ## The pattern 1. **Identify the artifact.** A diff, a file, a plan, a claim. Decide whether you're reviewing code or prose: the framing changes what "finding" means. 2. **Run your primary reviewer's own pass first** (one or several review lenses; see below). Don't skip this: the second model adds diversity, it doesn't replace your own critique. 3. **In parallel, run a second model** (a different vendor's CLI, or a different tier of your own vendor) with read-only access to the same artifact. Give it a specific focus question, not just "review this." Never send secrets,