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

arXiv: https://arxiv.org/abs/2502.11098 · HTML: https://mlsanity.com/paper/2502.11098/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 29 of its 46 references that are in the map.
- First posted on arXiv 16 Feb 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 29 of 29)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Self-Refine: Iterative Refinement with Self-Feedback](https://mlsanity.com/paper/2303.17651/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Code as Policies: Language Model Programs for Embodied Control](https://mlsanity.com/paper/2209.07753/) (2022)
- [Large Language Model based Multi-Agents: A Survey of Progress and Challenges](https://mlsanity.com/paper/2402.01680/) (2024)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectives](https://mlsanity.com/paper/2312.11970/) (2023)
- [Scaling Large Language Model-based Multi-Agent Collaboration](https://mlsanity.com/paper/2406.07155/) (2024)
- [Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?](https://mlsanity.com/paper/2402.18272/) (2024)
- [Cut the Crap: An Economical Communication Pipeline for LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2410.02506/) (2024)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [Exploring Large Language Model based Intelligent Agents: Definitions, Methods, and Prospects](https://mlsanity.com/paper/2401.03428/) (2024)
- [LLM Multi-Agent Systems: Challenges and Open Problems](https://mlsanity.com/paper/2402.03578/) (2024)
- [Small LLMs Are Weak Tool Learners: A Multi-LLM Agent](https://mlsanity.com/paper/2401.07324/) (2024)
- [What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering](https://mlsanity.com/paper/2406.12334/) (2024)
- [Determinants of LLM-assisted Decision-Making](https://mlsanity.com/paper/2402.17385/) (2024)
- [Idea2Img: Iterative Self-Refinement with GPT-4V(ision) for Automatic Image Design and Generation](https://mlsanity.com/paper/2310.08541/) (2023)
- [LLM Harmony: Multi-Agent Communication for Problem Solving](https://mlsanity.com/paper/2401.01312/) (2024)
- [CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving](https://mlsanity.com/paper/2404.17729/) (2024)
- [Exploring the Reliability of Large Language Models as Customized Evaluators for Diverse NLP Tasks](https://mlsanity.com/paper/2310.19740/) (2023)
- [LLM-based Multi-Agent Systems: Techniques and Business Perspectives](https://mlsanity.com/paper/2411.14033/) (2024)
- [Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions](https://mlsanity.com/paper/2402.01108/) (2024)
- [Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents](https://mlsanity.com/paper/2501.00430/) (2024)
- [Computational Experiments Meet Large Language Model Based Agents: A Survey and Perspective](https://mlsanity.com/paper/2402.00262/) (2024)
- [Towards Collaborative Intelligence: Propagating Intentions and Reasoning for Multi-Agent Coordination with Large Language Models](https://mlsanity.com/paper/2407.12532/) (2024)
- [Multi-LLM Text Summarization](https://mlsanity.com/paper/2412.15487/) (2024)
- [GrowSpace: Learning How to Shape Plants](https://mlsanity.com/paper/2110.08307/) (2021)

## Built on by (most cited first, 17 of 17)

- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [Latent Collaboration in Multi-Agent Systems](https://mlsanity.com/paper/2511.20639/) (2025)
- [A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents](https://mlsanity.com/paper/2602.06052/) (2026)
- [Thought Communication in Multiagent Collaboration](https://mlsanity.com/paper/2510.20733/) (2025)
- [LatentMem: Customizing Latent Memory for Multi-Agent Systems](https://mlsanity.com/paper/2602.03036/) (2026)
- [CodeAgents: A Token-Efficient Framework for Codified Multi-Agent Reasoning in LLMs](https://mlsanity.com/paper/2507.03254/) (2025)
- [The Vision Wormhole: Latent-Space Communication in Heterogeneous Multi-Agent Systems](https://mlsanity.com/paper/2602.15382/) (2026)
- [LegalWiz: A Multi-Agent Generation Framework for Contradiction Detection in Legal Documents](https://mlsanity.com/paper/2510.03418/) (2025)
- [PrivAct: Internalizing Contextual Privacy Preservation via Multi-Agent Preference Training](https://mlsanity.com/paper/2602.13840/) (2026)
- [MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2605.06623/) (2026)
- [Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems](https://mlsanity.com/paper/2605.14892/) (2026)
- [Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives](https://mlsanity.com/paper/2506.09656/) (2025)
- [StateBridge: Training-free Hidden-state Alignment for Latent Communication in LLM Multi-Agent Systems](https://mlsanity.com/paper/2608.13317/) (2026)
- [WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation](https://mlsanity.com/paper/2510.15306/) (2025)
- [A Hybrid Multi-Agent Prompting Approach for Simplifying Complex Sentences](https://mlsanity.com/paper/2506.11681/) (2025)
- [Rethinking Multi-Agent Collaboration: When More Is Less](https://mlsanity.com/paper/2609.19759/) (2026)
- [Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection](https://mlsanity.com/paper/2609.38274/) (2026)

## Abstract (arXiv)

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 textitTalk 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. textitTalkHier 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.