Title: AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
Venue: NeurIPS 2025
Accepted: yes
Keywords: LLM Agent

MAS

RAG

DAG
Abstract: The rapid advancement of Large Language Models (LLMs) has catalyzed the development of multi-agent systems, where multiple LLM-based agents collaborate to solve complex tasks.   However, existing systems predominantly rely on centralized coordination, which introduces scalability bottlenecks, limits adaptability, and creates single points of failure.   Additionally, concerns over privacy and proprietary knowledge sharing hinder cross-organizational collaboration, leading to siloed expertise.   To address these challenges, we propose AgentNet, a decentralized, Retrieval-Augmented Generation (RAG)-based framework that enables LLM-based agents to autonomously evolve their capabilities and collaborate efficiently in a Directed Acyclic Graph (DAG)-structured network.   Unlike traditional multi-agent systems that depend on static role assignments or centralized control, AgentNet allows agents to specialize dynamically, adjust their connectivity, and route tasks without relying on predefined workflows.
AgentNet’s core design is built upon several key innovations: (1) Fully Decentralized Paradigm: Removing the central orchestrator, allowing agents to coordinate and specialize autonomously, fostering fault tolerance and emergent collective intelligence. (2) Dynamically Evolving Graph Topology: Real-time adaptation of agent connections based on task demands, ensuring scalability and resilience.
(3) Adaptive Learning for Expertise Refinement: A retrieval-based memory system that enables agents to continuously update and refine their specialized skills.
By eliminating centralized control, AgentNet enhances fault tolerance, promotes scalable specialization, and enables privacy-preserving collaboration across organizations.      Through decentralized coordination and minimal data exchange, agents can leverage diverse knowledge sources while safeguarding sensitive information.      Experimental results demonstrate that AgentNet outperforms traditional centralized multi-agent systems, significantly improving efficiency, adaptability, and scalability in dynamic environments, making it a promising foundation for next-generation autonomous, privacy-respecting multi-agent ecosystems.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose a new multi-agent system (MAS) with 1) decentralized agent coordination, 2) a dynamic agent communication and cooperation pattern, and 3) an agent skill refinement mechanism based on a retrieval-based memory system. Some experimental results show that the proposed MAS can outperform many existing single agents and MAS on different tasks, and other ablation studies show that the design of routing and evolution are essential to the whole system.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1) The design of the new MAS is flexible and requires less human prior knowledge when building a system for specific tasks.\n2) The experimental results seem promising and outperform many existing (multi-)agent mechanisms.\n\n\nWeaknesses:\n1) The system's design may have limitations and may require additional clarification on its generality.\n  As the proposed MAS focuses on the decentralized setting with DAG design, it is unclear how the system can handle cases requiring cyclic dependencies. For example, if a task consists of subtask A, B, and C, and subtask A requires special domain knowledge of Agent 1, solving subtask B requires the result of subtask A and special domain knowledge of Agent 2, and solving subtask C requires the result of subtask B and special domain knowledge of Agent 1.\n  - It is unclear from the paper whether it is possible to construct a warm-up dataset (referred to as a training set in the appendix) that can help different tasks or whether each special kind of task requires a special dataset to warm up the system again.\n\n2) The presentation of the paper can be improved.\n  - It is unclear what is the relation between the context ($c_f$ or $c_{t_{m+1}}$) and the agent capability vector $c_i^m$.\n  - It is unclear how to find the initial agent. Although Section 3.4 tries to discuss the mechanism, it is still confusing even for a basic understanding of whether such a mechanism is an independent module or reusing the forward/split function of a special agent.\n  - Too many vague words are used to describe the mechanism but give no information, e.g., \"a sophisticated mechanism\" and \"carefully designed prompts.\"\n\n3) It is unclear how the capabilities/abilities of the agent are initialized.\n  - The definitions of \"capability\" and \"abilities\" are difficult for readers to distinguish.\n  - It is unclear whether the experiments cover the scenario of a different (disjoint) set of capabilities/abilities.\n\nQUESTIONS:\nPlease refer to the Weaknesses above. Some other additional questions are as follows:\n1. As shown in the Appendix E case studies, does it mean the task always starts with Agent 0? \n2. What if agents are coming to different answers to a specific step? How does the multi-agent framework handle that inconsistency or hallucination? Will the latter one always rewrite the earlier one?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a decentralized method designed for MAS grounded in LLMs. This is achieved through a dynamic directed acyclic graph for task routing, fostering enhanced system robustness and emergent intelligence. Furthermore, the framework incorporates a dynamic agent graph topology, where agent nodes and connections are adjusted in real-time based on task requirements and agent capabilities. Finally, a retrieval-augmented memory system is employed to support continuous skill refinement and agent specialization. The experiments are presented on three datasets.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths：\n1. The proposed method uses a decentralized graph-topology multi-agent system for collaborative task execution and evolution. Agents become more specialized and perform distinct roles, which seems meaningful.\n2. The experiment is relatively comprehensive and demonstrates the efficiency, adaptability, and scalability of the method in a dynamic environment.\n\nWeaknesses:\n1. In Figure 1, the excessive adjectives for multi-agent are confusing. Some terms are unexplained, like 'hierarchical'. There are also same meaning words, such as 'fixed' and 'static'. Although the three keywords presented for Pre-Defined Agents and Self-Evolving Agents are refined key features, they can't form a contrast. For example, 'static' corresponds to 'decentralized', but in the key features, 'static' corresponds to 'dynamic'.\n2. The computation of some variables is unclear. In formula 2, it is not explained how the success rate $S$ is calculated. In formula 5, the reasoning function uses the historical context $c_{t_{m+1}}$ as input to generate the reasoning output. What specific historical content is included in $c_{t_{m+1}}$? Additionally, how is the $p_t$ in the task triplet constructed?\n3. In Table 1, why does the AFLOW method outperform AgentNet on the Qwen-turbo backbone?\n4. Some hyperparameters need further discussion, such as $\\alpha$, $\\beta$, and $\\theta_w$.\n5. Figures 1, 2, and 5, as well as Table 2, seem not to be cited in the main paper. \n6. On line 5 from the bottom of page 8, there is a missing number at the '-' position.\n\nQUESTIONS:\nPlease see the above weaknesses.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces AgentNet, a decentralized framework for coordinating multiple LLM agents through a dynamically evolving Directed Acyclic Graph (DAG). Unlike most prior multi-agent systems that rely on a central orchestrator or static roles, AgentNet allows agents to autonomously evolve, specialize, and collaborate using local decision-making, retrieval-augmented memory, and dynamic topology adaptation.\n\nSTRENGTHS AND WEAKNESSES:\nPros:\n1. AgentNet updates the communication graph (routing edge weights) and the agent capability vectors simultaneously, based on per-task success. This allows the system to evolve a specialized, sparse DAG from an initially fully connected graph. This is a clean and elegant form of structure learning.\n2. The paper includes extensive evaluations on MATH, BBH, and API-Bank with different backbones (DeepSeek, GPT-4o-mini, Qwen-turbo). The heterogeneity studies and router ablations are insightful, demonstrating how both agent diversity and router design impact overall system performance.\n\nCons:\n1. While AgentNet claims scalability via decentralization, maintaining per-agent memory modules, capability vectors, and dynamically updated DAG connections could become computationally and memory-intensive in very large-scale systems. The actual runtime and cost implications are not analyzed.\n2. For me, it is still not clear why decentralized coordination is better than a centralized controller or planner. Any experimental justification?\n3. For the mult-agent baselines, it is not clear the major difference with GPTSwarm as it also share highly similar motivation in designing dynamic, specialized multi-agent systems with learnable topologies.\n\nQUESTIONS:\n1. To what extent does the performance gain stem from RAG rather than decentralization or topology learning?\n2. Given that routing decisions rely on LLM decoding (prompt-based), how stable is the induced topology across repeated runs of the same task?\n\nLIMITATIONS:\nThe motivation of fully decentralized pradigm is not convincing to me, though it will be more suitable for the topology optimization.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes AgentNet, a decentralized framework for LLM-based multi-agent systems (MAS). It addresses limitations of centralized architectures (e.g., scalability bottlenecks, single points of failure) by enabling agents to coordinate via a dynamic Directed Acyclic Graph (DAG) with retrieval-augmented generation (RAG). Key innovations include: 1) fully decentralized task routing without a central controller; 2) adaptive graph topology that evolves based on task demands; 3) RAG-based memory for continuous skill refinement. Experiments show AgentNet outperforms single-agent and centralized MAS baselines in task accuracy, especially in dynamic environments. It also demonstrates robust privacy preservation and cross-organizational collaboration via localized data processing.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n+ Novel Decentralized Design: Eliminates central orchestrators, enhancing fault tolerance and scalability. The dynamic DAG allows agents to self-organize, addressing rigidity in traditional MAS.\n\n+ Adaptive Learning Mechanisms: RAG-based memory and evolutionary task routing enable agents to specialize over time, improving performance in complex tasks.\n\n+ Dynamic agent graph topology\n\n+ The paper is characterized by a clear structure and standardized academic writing.\n\n**Weaknesses**\n\nThe paper is generally well-executed, with a clear algorithmic description. However, I have the following concerns:\n\n* **Similarity to existing methods**: Some modules resemble prior work. For example, the *Decentralized Network Topology* is highly similar to GPTSwarm and AgentPrune. The idea of *Adaptive Learning and Specialization*, based on RAG, has been explored in many previous studies and appears quite similar to the approach in ExpeL. While I acknowledge the authors' contributions to decentralization, it seems that decentralized ideas have also been adopted in dialogue-driven systems like AutoGen.\n\n* **Concerns about empirical results**: Compared to the AFlow algorithm proposed in 2024, the performance improvements of AgentNet do not appear to be particularly significant.\n\n* **Questions about training cost**: Perhaps I missed it, but I’m curious about the training overhead of AgentNet. Compared to AFlow, this might be one of AgentNet's advantages.\n\nQUESTIONS:\nSee `Weaknesses`.\n\nOverall, I acknowledge the substantial engineering effort behind this work, so I would lean toward accepting the paper.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}