Paper

UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems

Unreadunread

AI summary

arXiv:2605. 26646v1 Announce Type: new Abstract: LLM-based multi-agent systems decompose complex tasks into interacting roles, but most remain manually orchestrated by prompts, tools, and control rules, while agents are rarely optimized through a unified reinforcement learning interface.

Reading status

Priority

About this paper

Authors: Yiqun Chen, Wei Yang, Erhan Zhang, Shijie Wang, Qi Liu, Zechun Niu, Bin Zhang, Haitao Li, Rui Li, Lingyong Yan, Jinyuan Feng, Biqing Qi, Xiaochi Wei, Yan Gao, Yi Wu, Yao Hu, Jiaxin Mao
Released: 2026-05-27 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, class, eval, rate, control, without, full, model. Source: arxiv cs.AI (Artificial Intelligence).
arXiv: 2605.26646

Ask Claude about this paper

Posts a comment with @claude so the runner spins up a Claude Code session scoped to UnityMAS-O: A General RL Optimization Framework for LLM-Based Multi-Agent Systems. Replies appear in the conversation below.

Comments

No comments yet.

Related records

No links yet.