Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM
  Multi-Agent Systems
Authors: name: Zhao Wang, name: Sota Moriyama, name: Wei-Yao Wang, name: Briti Gangopadhyay, name: Shingo Takamatsu

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
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. 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.