Developer builds AI review system to eliminate false consensus between LLMs

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A software developer identified a critical flaw in multi-model AI review pipelines: when a second AI model sees the first model's output before forming its own assessment, it tends to validate rather than independently analyze, a phenomenon the developer calls anchoring bias in inference pipelines. To counter this, the developer built a system called AdversarialDebate, which mechanically prevents reviewer B from accessing reviewer A's conclusions until B has fully committed its own independent position. The developer argues this mirrors the logic behind double-blind peer review in academic publishing, a structural safeguard designed specifically to prevent social pressure from corrupting independent judgment. Testing revealed that without such enforcement, up to 89% of apparent AI-to-AI debate can consist of pre-generated text with no genuine position changes or evidence exchange. The developer notes this reasoning architecture problem extends well beyond code review, applying to domains such as incident response, security audits, change management, and medical second opinions.

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