让多个AI代理协作记忆，提升推理能力。
Enhancing Reasoning with Collaboration and Memory
- 设计不同背景的AI代理协同推理，用记忆库积累经验。
- 随机选例有时比精心设计还有效，但不当使用会干扰模型。
- 适合研究多智能体系统与认知增强的学者参考。
我们设想一个持续协作学习系统，由一群大语言模型代理共同解决推理问题，并通过集体构建的记忆库随经验积累提升性能。本文为该系统奠定基础，研究了思维链推理风格、多代理协作与记忆库之间的互操作性。不同于自一致性中相同的代理，本文引入具有不同示例的多样化上下文代理，以及一个摘要代理替代投票机制。生成冻结与持续学习的记忆库，搭配固定、随机及基于相似性的检索机制。系统性研究表明，在两个大模型和三个具象推理任务上，随机示例选择常优于更严谨的方法；在某些任务中，引入任何示例反而会干扰弱模型与强模型的表现。
原文摘要 · Abstract (English)
We envision a continuous collaborative learning system where groups of LLM agents work together to solve reasoning problems, drawing on memory they collectively build to improve performance as they gain experience. This work establishes the foundations for such a system by studying the interoperability of chain-of-thought reasoning styles, multi-agent collaboration, and memory banks. Extending beyond the identical agents of self-consistency, we introduce varied-context agents with diverse exemplars and a summarizer agent in place of voting. We generate frozen and continuously learned memory banks of exemplars and pair them with fixed, random, and similarity-based retrieval mechanisms. Our systematic study reveals where various methods contribute to reasoning performance of two LLMs on three grounded reasoning tasks, showing that random exemplar selection can often beat more principled approaches, and in some tasks, inclusion of any exemplars serves only to distract both weak and strong models.
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