研究人员开发了用于优化大型语言模型（LLM）驱动的多智能体系统（MAS）提示的新颖方法。其中一种方法MASPOB使用老虎机算法和图神经网络来有效地优化提示，解决了高评估成本和提示相互依赖性等挑战。另一种方法统一了时间和结构信用分配，分解误差信号以改进基于LLM的MAS优化并降低查询复杂性。第三个系统Market Regime Council（MRC）将Shapley信用应用于基于LLM的投资组合管理中的动态代理加权，在加密资产交易中表现出卓越的性能。 AI
影响 这些进展提供了更有效和可解释的方式来增强复杂AI系统的性能和决策能力。
排序理由 该集群包含多篇学术论文，详细介绍了基于LLM的多智能体系统的新研究方法。
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arXiv:2603.02630v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have achieved great success in many real-world applications, especially the one serving as the cognitive backbone of Multi-Agent Systems (MAS) to orchestrate complex workflows in practice. Sinc…
arXiv:2605.30227v1 Announce Type: cross Abstract: While Multi-Agent Systems (MAS) empower Large Language Models to tackle complex reasoning tasks through collaborative interaction, optimizing their dynamics remains a formidable challenge due to the discrete, non-differentiable na…
While Multi-Agent Systems (MAS) empower Large Language Models to tackle complex reasoning tasks through collaborative interaction, optimizing their dynamics remains a formidable challenge due to the discrete, non-differentiable nature of the computation graph and the sparsity of …
arXiv:2605.24490v1 Announce Type: new Abstract: Multi-agent LLM decision systems for portfolio management still lack a principled way to assign credit across specialist agents, remain vulnerable to cold-start dominance under regime shifts, and offer limited transparency into how …