Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems

• URL: http://arxiv.org/abs/2502.11098v1
• Date: Sun, 16 Feb 2025 12:26:58 GMT
• Title: Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM Multi-Agent Systems
• Authors: Zhao Wang, Sota Moriyama, Wei-Yao Wang, Briti Gangopadhyay, Shingo Takamatsu,
• Abstract summary: textitTalk Structurally, Act Hierarchically (TalkHier) is a novel framework that introduces a structured communication protocol for context-rich exchanges.<n>textitTalkHier surpasses various types of SoTA, including inference scaling model (OpenAI-o1), open-source multi-agent models (e.g., AgentVerse)
• Score: 10.67359331022116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in LLM-based multi-agent (LLM-MA) systems have shown promise, yet significant challenges remain in managing communication and refinement when agents collaborate on complex tasks. In this paper, we propose \textit{Talk Structurally, Act Hierarchically (TalkHier)}, a novel framework that introduces a structured communication protocol for context-rich exchanges and a hierarchical refinement system to address issues such as incorrect outputs, falsehoods, and biases. \textit{TalkHier} surpasses various types of SoTA, including inference scaling model (OpenAI-o1), open-source multi-agent models (e.g., AgentVerse), and majority voting strategies on current LLM and single-agent baselines (e.g., ReAct, GPT4o), across diverse tasks, including open-domain question answering, domain-specific selective questioning, and practical advertisement text generation. These results highlight its potential to set a new standard for LLM-MA systems, paving the way for more effective, adaptable, and collaborative multi-agent frameworks. The code is available https://github.com/sony/talkhier.

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