FedAgentKE：异构智能体的联邦语义知识演化
FedAgentKE: Federated Semantic Knowledge Evolution for Heterogeneous Agents
AI总结：
研究异构智能体孤立运行问题，提出 FedAgentKE 框架，通过语义知识蒸馏、聚合和Adapt实现联邦语义知识演化，实验验证其在跨框架和跨任务设置下能改进智能体协作，为未来协作智能体生态系统发展提供潜力。
AI中文摘要：
基于大语言模型的智能体越来越依赖推理、工具使用和迭代执行，但现有智能体框架大多孤立运行。近期基于内存的智能体系统虽通过本地检索和工作流重用改进单个智能体，但本地经验在孤立框架中仍碎片化，限制跨框架知识 transfer 和协作推理演化。我们提出 FedAgentKE，通过语义的知识蒸馏、聚合和Adapt优化语义通信。实验表明，该框架在跨框架和跨任务设置下均有一致改进，凸显联邦语义知识演化对未来协作智能体生态系统的潜力。
英文摘要：
Large language model (LLM)-based agents increasingly rely on reasoning, tool use, and iterative execution, yet existing agent frameworks still operate largely in isolation. While recent memory-based agent systems improve individual agents through local retrieval and workflow reuse, local experiences remain fragmented across isolated agent frameworks, limiting cross-framework knowledge transfer and collaborative reasoning evolution. We propose FedAgentKE, a lightweight framework for Federated Semantic Knowledge Evolution across heterogeneous agents. FedAgentKE enables distributed agent frameworks to collaboratively evolve transferable reasoning abstractions through iterative semantic knowledge distillation, aggregation, and adaptation without sharing raw reasoning trajectories. Experiments demonstrate consistent improvements under both cross-framework and cross-task settings, highlighting the potential of federated semantic knowledge evolution for future collaborative agent ecosystems.
