arXiv AI June 2, 2026 By Yao Guan, Lin Wang, Zhihu Lu, Ziyi Wang, Wenzhu Yan, Qiang Duan

MOC: Multi-Order Communication in LLM-based Multi-Agent Systems

arXiv:2606. 02359v1 Announce Type: new Abstract: Despite the remarkable progress of Large Language Model (LLM) based Multi-Agent Systems, most research focuses on optimizing coordination topology while largely underexploring the equally critical problem: how to transmit and optimize messages among agents effectively?

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Aug 14

Discovering Efficient and Explainable Communication Topologies for LLM-based Multi-Agent Systems via Causal Inference

arXiv:2608. 12921v1 Announce Type: cross Abstract: The performance of large language model (LLM)-based multi-agent systems (MAS) largely depends on effective communication topologies.

By Junzhi Li, Peng He, Qirui Ji, Wei Wang, Lixiang Liu, Chuxiong Sun

llms agents benchmarks

arXiv AI

Jun 4

Streaming Communication in Multi-Agent Reasoning

arXiv:2606. 05158v1 Announce Type: cross Abstract: Multi-agent reasoning systems adopt a "generate-then-transfer" paradigm that forces end-to-end latency to scale linearly with pipeline depth.

By Zhen Yang, Xiaogang Xu, Wen Wang, Cong Chen, Xander Xu, Ying-Cong Chen

llms agents benchmarks

arXiv AI

Sep 10

Inference-Time Graph Engineering for Multi-Agent LLM Workflows

arXiv:2609.05774v1 Announce Type: new Abstract: Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestrati...

By Katherine Tieu, Dongqi Fu, Yinglong Xia, Hong Li, Hong Yan, Jingrui He

llms agents reinforcement-learning

arXiv AI

Jul 16

Benefits and Limitations of Communication in Multi-Agent Reasoning

arXiv:2510. 13903v2 Announce Type: replace-cross Abstract: Chain-of-thought prompting has popularized step-by-step reasoning in large language models, yet model performance still degrades as problem complexity and context length grow.

By Michael Rizvi-Martel, Satwik Bhattamishra, Neil Rathi, Guillaume Rabusseau, Michael Hahn

llms agents benchmarks

arXiv AI

Jun 6

What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems

arXiv:2606. 05304v1 Announce Type: new Abstract: Multi-agent systems (MAS) built on large language models are typically organized around roles, pipelines, and turn schedules, while the content that agents pass to one another is often left as unconstrained natural language.

By Chen Huang, Yuhao Wu, Wenxuan Zhang

llms agents

arXiv AI

Sep 2

Control-Data Flow Separation: Stable Prompt Optimization in Multi-Agent LLMs

The paper introduces control‑data flow separation to improve prompt optimization in multi‑agent large language model systems. By representing execution protocols as typed, validated program objects and keeping task‑relevant content as unstructured language, the method prevents prompt edits from corrupting critical routing, formatting, or termination signals. Experiments on synthetic reasoning, collaborative review generation, and insurance rating workflows show that this approach maintains 100% protocol validity while consistently enhancing task performance.

By Wentao Zhang, Syed Shariyar Murtaza, Junaid Ahmad Bhatti, Utkarsh Soni, Yifan Nie, Eugene Wen, Yuntian Deng

llms agents

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Related stories

Discovering Efficient and Explainable Communication Topologies for LLM-based Multi-Agent Systems via Causal Inference

Streaming Communication in Multi-Agent Reasoning

Inference-Time Graph Engineering for Multi-Agent LLM Workflows

Benefits and Limitations of Communication in Multi-Agent Reasoning

What Should Agents Say? Action-state Communication for Efficient Multi-Agent Systems

Control-Data Flow Separation: Stable Prompt Optimization in Multi-Agent LLMs