arXiv:2602. 00911v2 Announce Type: replace Abstract: The unit of collaboration in federated learning determines what guarantees are even expressible.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
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.
arXiv:2607. 26618v1 Announce Type: new Abstract: Federated PEFT enables LLMs to collaboratively adapt to decentralized private data without sharing raw examples.
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.
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.
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.