扩展多智能体系统：一种用于改善智能体交互的智能中间件
Scaling Multi-agent Systems: A Smart Middleware for Improving Agent Interactions
AI总结：
提出认知织物节点（CFN）中间件，通过强化学习和优化算法动态管理智能体间通信，在HotPotQA和MuSiQue数据集上性能提升超过10%。
摘要：
随着基于大语言模型的多智能体系统从实验性试点演变为复杂、持久的生态系统，直接智能体间通信的局限性日益明显。当前架构存在上下文碎片化、随机幻觉、僵化的安全边界和低效的拓扑管理等问题。本文引入认知织物节点（CFN），一种新颖的中间件层，在智能体之间创建无处不在的“认知织物”。与传统消息队列或服务网格不同，CFN不仅仅是直通机制；它们是活跃的、智能的中介。该架构的核心是将记忆从简单存储提升为主动功能基板，为其他四个关键能力提供信息：拓扑选择、语义基础、安全策略执行和提示转换。我们提出每个功能应由学习模块管理，利用强化学习和优化算法动态提升系统性能。通过拦截、分析和重写智能体间通信，认知织物确保单个智能体保持轻量，同时生态系统实现连贯性、安全性和语义对齐。我们在多智能体环境中对HotPotQA和MuSiQue数据集评估了CFN的有效性，并证明与直接智能体间通信相比，CFN在两个数据集上的性能提升超过10%。
英文摘要：
As Large Language Model (LLM) based Multi-Agent Systems (MAS) evolve from experimental pilots to complex, persistent ecosystems, the limitations of direct agent-to-agent communication have become increasingly apparent. Current architectures suffer from fragmented context, stochastic hallucinations, rigid security boundaries, and inefficient topology management. This paper introduces Cognitive Fabric Nodes (CFN), a novel middleware layer that creates an omnipresent "Cognitive Fabric" between agents. Unlike traditional message queues or service meshes, CFNs are not merely pass-through mechanisms; they are active, intelligent intermediaries. Central to this architecture is the elevation of Memory from simple storage to an active functional substrate that informs four other critical capabilities: Topology Selection, Semantic Grounding, Security Policy Enforcement, and Prompt Transformation. We propose that each of these functions be governed by learning modules utilizing Reinforcement Learning (RL) and optimization algorithms to improve system performance dynamically. By intercepting, analyzing, and rewriting inter-agent communication, the Cognitive Fabric ensures that individual agents remain lightweight while the ecosystem achieves coherence, safety, and semantic alignment. We evaluate the effectiveness of the CFN on the HotPotQA and MuSiQue datasets in a multi-agent environment and demonstrate that the CFN improves performance by more than 10\% on both datasets over direct agent to agent communication.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
