Developer Builds Parallel AI Code Review System to Eliminate Anchoring Bias

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A software developer found that standard multi-model AI code review workflows produce misleading results because the second model sees the first model's verdict before forming its own opinion, creating anchoring bias rather than independent analysis. To address this, they built a system called AdversarialDebate, where two language models review the same code simultaneously with zero shared context before committing their positions. The models then engage in a structured debate where every objection must reference a specific counter-claim tied to the actual artifact. Testing across 411 debates on 70 public pull requests showed 81% of debate claims matched documented outcomes, with near-zero fabrications. Across 360 reviews in version 0.2.0, the system reported a 0% theater rate at a total cost of $0.42.

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