The paper proposes typed federated artifacts—schema‑validated objects with per‑field privacy and dispute resolution—to enable tool‑routing knowledge sharing among frozen, heterogeneous LLM agents. By replacing flat text prompts with typed fields, the authors achieve near‑centralized routing performance on StableToolBench while reducing data size to 20 MB JSON per client. The study also highlights that a simple TF‑IDF classifier can outperform LLM routing on labeled benchmarks, indicating limitations in current evaluation methods.
By Abhijit Chakraborty, Ni Trieu, Vivek Gupta
arXiv:2605. 26542v2 Announce Type: replace-cross Abstract: Tool-using agents increasingly operate in open-ended deployment environments, where they compose file systems, web APIs, code interpreters, and enterprise services at runtime.
By Xiaochong Jiang, Shiqi Yang, Ziwei Li, Lifei Liu, Haoran Yu, Yichen Liu
The paper introduces Routed Graph Handoff, a lightweight LLM router that chooses between a typed dependency graph and natural language for each delegation in multi‑agent LLM systems. On four benchmarks with over 1,050 trajectories, the routed system matches or surpasses natural‑language‑only performance, achieving significant compression and accuracy gains. The approach requires a graph‑aware executor prompt, and an oracle analysis suggests further potential for execution‑time adaptive routing.
By Pratyay Banerjee, Ankit Chadha
arXiv:2608.21375v1 Announce Type: new
Abstract: Heterogeneous agentic retrieval-augmented generation (RAG) systems increasingly orchestrate external APIs, internal databases, vector stores, and graph...
By Yong-eun Cho
arXiv:2607. 26618v1 Announce Type: new Abstract: Federated PEFT enables LLMs to collaboratively adapt to decentralized private data without sharing raw examples.
By Donghang Duan, Xu Zheng, Lizong Zhang, Chong Mu, Meng Han
The paper introduces Tool Primitives, a design that replaces rigid API schemas with natural language interfaces for tool calling, enabling seamless inter-tool communication. It builds ToolFace, a repository of over 25,000 functions that LLMs can dynamically retrieve, and HEART, a harness engineering framework that orchestrates tool use with planning, routing, and verification. Experiments show HEART outperforms fine‑tuned models and leading commercial LLMs while cutting API costs by up to 85%.
By Haibo Jin, Suijin Wang, Xucheng Yu, Haojing Luo, Haohan Wang
arXiv:2606. 19135v1 Announce Type: cross Abstract: As large language models (LLMs) advance and multi-agent systems aim to overcome the limits of standalone agents, robust communication protocols are becoming essential infrastructure for distributed agent networks.
By Linus Sander, Habtom Kahsay Gidey, Alexander Lenz, Alois Knoll
arXiv:2608. 15311v1 Announce Type: new Abstract: Federated instruction fine-tuning enables Large Language Models (LLMs) to adapt to decentralized, privacy-sensitive data without requiring data sharing.
By Ankita Sharma, Bahar Farahani, Sanaz Rahimi Moosavi, Amir Rrahmani, Farshad Firouzi, Krishnendu Chakrabarty
Federated PEFT enables LLMs to collaboratively adapt to decentralized private data without sharing raw examples. However, task heterogeneity across clients can cause cross-task interference and gradient conflicts during aggregation.
arXiv:2606. 03143v1 Announce Type: new Abstract: Modern LLM agents increasingly rely on skill libraries to handle complex tasks, making skill evolution a primary driver of self-improvement.
By Jingbo Yang, Guanyu Yao, Yang Zhang, Ramana Rao Kompella, Gaowen Liu, Shiyu Chang
arXiv:2606. 16891v1 Announce Type: cross Abstract: Federated Learning is rapidly evolving beyond the exchange of traditional model weights and gradients, yet existing definitions fail to capture the full scope of modern payloads like synthetic data and federated analytics.
By Alvaro Javier Vargas Guerrero, Xinguang Wang, Quang Manh Doan, Guy Nagels
arXiv:2609.32192v2 Announce Type: replace
Abstract: Multi-agent workflows require task-relevant information to be shared across agents, while irrelevant, stale, unverified, or incompatible informatio...
By Sen Zhao, Ruiqi Kong, Zuyu Zhang, Lifeng Shen, Xinyu He, Ding Zou, Xu Zhang, Qinghua Zhang