Federation over Text: Insight Sharing for Multi-Agent Reasoning

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 with a central server. The server then aggregates, distills, and consolidates knowledge across tasks and domains into a shared insight library, which can be reused by current and future agents to improve their reasoning. Experiments show that FoT improves reasoning effectiveness and efficiency across real-world daily tasks, cross-domain collaboration, and research insight discovery, achieving an average performance gain of 11.9 absolute percentage points while reducing client-generated task-completion tokens by 5.5%.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2604.16778 [cs.LG]

(or arXiv:2604.16778v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2604.16778 Focus to learn more arXiv-issued DOI via DataCite

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.