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This paper reveals that aggregating complete reasoning traces from multiple LLM agents, rather than just their final answers, can correct errors even when agents unanimously agree, introducing the 'aggregation paradox' and the Self-Consistent Mixture of Agents method.
Proposes MMoA, a novel AI-agent framework that incorporates recurrence mechanisms for a memoried mixture-of-agent architecture. The paper introduces a method to improve agent collaboration and memory in multi-agent systems.