multi-agent debate

Multi-agent debate improves decision quality through structured argumentation between LLM agents. **Mechanism**: Multiple agents take positions, present arguments, critique each other, refine positions through rounds, converge on conclusion. **Debate formats**: Point-counterpoint, panel discussion, adversarial critique, Socratic questioning. **Roles**: Proposer (suggests solutions), critic (finds flaws), synthesizer (combines insights), judge (evaluates arguments). **Why it works**: Different agents catch different errors, adversarial pressure improves quality, diverse perspectives emerge, explicit reasoning is more verifiable. **Implementation**: Multiple model instances with different system prompts, structured conversation protocol, judge selects final answer. **Use cases**: Complex decisions, fact-checking, brainstorming refinement, ethical analysis, red-teaming. **Benchmarks**: Improves accuracy on reasoning tasks, especially when models have complementary strengths. **Variations**: Society of mind architectures, role-playing simulations, competitive game theory scenarios. **Trade-offs**: Much higher computational cost, complex orchestration, may not converge on some topics. Powerful technique for high-stakes decisions requiring multiple perspectives.