English(EN) Scaling Multi-Agent Systems with Prospect-State Propagation

新方法使用前景理论扩展LLM多智能体系统

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 22:30

研究人员引入了用于多智能体系统的前景状态传播（PspMAS），这是一种旨在增强基于LLM的多智能体系统可扩展性的新颖方法。该方法通过将智能体的状态解耦为紧凑的前景状态和富有表现力的语义状态来解决令牌消耗的挑战。前景状态受前景理论的启发，通过轻量级传播器维持智能体异质性，而语义状态则利用LLM进行感知、推理和决策，从而创建了一个互补且可扩展的模拟解决方案。 AI

影响通过管理令牌消耗和智能体异质性，增强了基于LLM的多智能体系统的可扩展性。

排序理由该条目是发表在arXiv上的研究论文，详细介绍了一种用于多智能体系统的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

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arXiv cs.MA (Multiagent) TIER_1 English(EN) · Fakhri Karray · 2026-09-07 22:30

使用前景状态传播扩展多智能体系统

Current LLM-based multi-agent systems (MAS) periodically compress intermediate states to reduce inference-time token consumption, thereby attempting to incorporate more agents. However, naive scaling strategies face challenges. For example, in economic simulations, large-scale MA…

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使用前景状态传播扩展多智能体系统

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