title: Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM
    Multi-Agent Systems
summary: 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.
paper_authors._id: 67b411e45e634139c0d86a1e
paper_authors.name: Zhao Wang
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-02-28T05:58:41.057Z
paper_authors._id: 67c150247372d4d6c4aa5f2d
paper_authors.avatarUrl: /avatars/c847859068ad868b3313e14fa850f3b6.svg
paper_authors.fullname: wang
paper_authors.type: user
paper_authors.user: wang1946may7
paper_authors._id: 67b411e45e634139c0d86a1f
paper_authors.name: Sota Moriyama
paper_authors._id: 67b411e45e634139c0d86a20
paper_authors.name: Wei-Yao Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-06T09:27:59.018Z
paper_authors._id: 66fbb4eac5e1b393cf3266d8
paper_authors.avatarUrl: /avatars/a9cff3c9466849dc19cad0bd37fb0459.svg
paper_authors.fullname: Wei-Yao Wang
paper_authors.type: user
paper_authors.user: SwyWang
paper_authors._id: 67b411e45e634139c0d86a21
paper_authors.name: Briti Gangopadhyay
paper_authors._id: 67b411e45e634139c0d86a22
paper_authors.name: Shingo Takamatsu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-19T15:47:05.161Z
paper_authors._id: 630776fafd79b417f1bd6268
paper_authors.avatarUrl: /avatars/7712ec70dc56124bd123ba48db11e239.svg
paper_authors.fullname: Shingo Takamatsu
paper_authors.type: user
paper_authors.user: ts0184
paper_title: Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM
    Multi-Agent Systems
paper_summary: 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.
paper_ai_keywords: LLM-based multi-agent (LLM-MA) systems structured communication protocol hierarchical refinement system inference scaling model (OpenAI-o1) AgentVerse ReAct GPT4o open-domain question answering domain-specific selective questioning practical advertisement text generation