Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM
  Multi-Agent Systems
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.
67b411e45e634139c0d86a1e
Zhao Wang
extracted_pending
2025-02-28T05:58:41.057Z
67c150247372d4d6c4aa5f2d
/avatars/c847859068ad868b3313e14fa850f3b6.svg
wang
user
wang1946may7
67b411e45e634139c0d86a1f
Sota Moriyama
67b411e45e634139c0d86a20
Wei-Yao Wang
claimed_verified
2025-03-06T09:27:59.018Z
66fbb4eac5e1b393cf3266d8
/avatars/a9cff3c9466849dc19cad0bd37fb0459.svg
Wei-Yao Wang
user
SwyWang
67b411e45e634139c0d86a21
Briti Gangopadhyay
67b411e45e634139c0d86a22
Shingo Takamatsu
admin_assigned
2025-02-19T15:47:05.161Z
630776fafd79b417f1bd6268
/avatars/7712ec70dc56124bd123ba48db11e239.svg
Shingo Takamatsu
user
ts0184
Talk Structurally, Act Hierarchically: A Collaborative Framework for LLM
  Multi-Agent Systems
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.
TalkHier, a novel framework with structured communication and hierarchical refinement, enhances LLM-MA systems across various tasks, surpassing existing methods.
LMM-MA systems structured communication protocol hierarchical refinement system inference scaling model AgentVerse majority voting strategies ReAct GPT4o open-domain question answering domain-specific selective questioning practical advertisement text generation