A developer built AdversarialDebate, an open-source multi-model code review system in which two LLMs analyze a pull request in parallel with no shared context, commit independent verdicts, and then de…
A developer added a fourth model, Mistral Small 3.2, midway through a field test of AdversarialDebate, changing the experiment's outcome. The addition revealed that maximum diversity can lead to capit…
A developer's field test of an adversarial debate system found that a homogeneous pair of the same model outperformed heterogeneous pairs, with GPT+GPT scoring 0.688 versus 0.357 for GPT+Gemini and 0.…
A developer built two LLM-based systems, PlannerCritic and AdversarialDebate, to detect when an LLM's judgment is wrong. Both systems proved reliable but shared a blind spot: their safety metrics impr…
A developer's field test of the AdversarialDebate multi-agent system found that the best-performing model pair, DeepSeek + Mistral, achieved a 97% verdict rate but also a 65% capitulation rate, where …
A developer building the AdversarialDebate multi-agent system found that its debates were 'dead' despite working infrastructure, and traced the root cause to the prompt design, which allowed models to…
A developer has released AdversarialDebate, an open-source system that enforces structural independence between AI reviewers by preventing the second model from seeing the first model's output until i…
A developer built AdversarialDebate, an open-source review engine that forces two LLMs to analyze artifacts independently before debating, to address the structural bias in typical AI 'second opinions…