多智能体LLM工作流的离线评估与迭代优化新框架，即将亮相ACL 2026，助力复杂协作场景调优。
arXiv:2605.18032v1 Announce Type: new Abstract: Multi-agent LLM workflows -- systems composed of multiple role-specific LLM calls -- often outperform …
提出数据流导向的强化学习框架，降低智能体LLM训练成本并支持多策略协同，大幅提升可扩展性。
arXiv:2605.15565v1 Announce Type: cross Abstract: Reinforcement learning (RL) is increasingly used to improve the reasoning, coding, and tool-use capa…
针对多智能体LLM协调中序列微调导致上下文分布偏移的缺陷，提出信任区域微调方法，有效提升团队协同表现。
arXiv:2605.15207v1 Announce Type: new Abstract: Multi-agent LLM systems have shown promise for complex reasoning, yet recent evaluations reveal they o…