arXiv:2604. 17612v3 Announce Type: replace-cross Abstract: Multi-agent systems built on large language models (LLMs) are difficult to reason about.
By Benedikt Bollig, Matthias F\"ugger, Thomas Nowak
The paper investigates zero‑shot coordination (ZSC) among large language model (LLM) agents, a scenario where independently developed agents must coordinate without prior shared scaffolding. Experiments in minimal, controlled environments reveal that current frontier LLMs struggle to coordinate, mainly due to limited understanding of the coordination problem and weak reasoning about partners’ beliefs. The authors introduce Coordination‑Friendly Definitions (CFDs) as a principled, automatically discoverable approach to improve robust coordination among LLM agents.
By Adrian Hayler, Shashank Reddy Chirra, Andrei Lupu, Johannes Forkel, Bidipta Sarkar, Siheng Feng, Jakob Foerster
Structured Authority Injection (SAI) is a protocol that enables persistent human control over large language model (LLM) agents by routing operator corrections through typed, schema‑validated Model Context Protocol (MCP) tool calls and maintaining a ranked authority context in each prompt. It defines three override classes—Constraint, Priority, and Strategy—each with distinct persistence semantics and a strict precedence order enforced via a directed authority channel. Evaluations across nine LLMs and eight scenarios show reduced ambiguous directive interpretation, mitigated authority decay, and lower prompt overhead in the strongest models, though benefits vary by model.
By Ryan Tran, YueMing Zhang, Lingfeng Tao, Nan Yang, Shengjie Xu, Yu Yin, Shuai Xu, Wenlu Zhang, Hailu Xu
The paper introduces a new benchmark that evaluates large language models (LLMs) on their agentic mathematical reasoning rather than just final answers. It aligns problem‑solving behaviors with a taxonomy of reusable mathematical atomic capabilities and includes planning, action, and feedback tasks in both textual and multimodal settings. Experiments show that models with similar end‑to‑end accuracy can have very different agentic profiles, highlighting the importance of process‑level evaluation.
By Jiayi Kuang, Yinghui Li, Yunze Song, Keyu Chen, Zhifeng Shen, Yangning Li, Yidong Wang, Di Yin, Ruizhi Qiao, Xing Sun, Kai Jin, Ying Shen, Liang Lin, Philip S. Yu
arXiv:2505. 11765v5 Announce Type: replace-cross Abstract: Agents powered by advanced large language models (LLMs) have demonstrated impressive capabilities across diverse complex applications.
By Shijun Li, Hilaf Hasson, Joydeep Ghosh
The paper introduces Degrees of Freedom (DoF), a continuous framework that measures how much decision-making is delegated to agents in large language model–based multi‑agent systems. By varying DoF, the authors explore a spectrum between fully centralized and fully decentralized coordination, finding that intermediate settings often outperform both extremes across several task domains. The study shows that task characteristics and retained experience influence how coordination granularity affects performance, offering empirical guidance for designing hybrid coordination regimes.
By Yiqing Peng, Hehai Lin, Zixuan Wang, Dongyi Zhang, Zhengjie Zhu, Yi Han, Yangyang Zheng, Hao Wang, Qian Li, Chengwei Qin