Federation over Text: Insight Sharing for Multi-Agent Reasoning

תקציר מקורי באנגליתarXiv:2604.16778v3 Announce Type: replace Abstract: Modern agents specialize in varying domains while there is no clear approach combining different domain skills. We propose a federated learning-like framework, Federation over Text (FoT), that enables multiple clients solving different tasks to collectively generate a shared library of metacognitive insights by iteratively federating their local reasoning processes without sharing actual problem instances. Instead of federation over gradients (e.g., as in distributed training), FoT operates at the semantic level without any gradient optimization or supervision signal. At each round, client LLM agents independently apply arbitrary local reasoning and self-improvement procedures to their own tasks and share the resulting reasoning traces wi