AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
Authors: Yingxuan Yang
Huacan Chai
Shuai Shao
Yuanyi Song
Siyuan Qi
Renting Rui
Weinan Zhang
Venue: NeurIPS 2025 poster ()
Keywords: LLM Agent
MAS
RAG
DAG
Primary area: applications

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.

AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
Yingxuan Yang', Huacan Chai'; Shuai Shao', Yuanyi Song', Siyuan Qi', Renting Rui', Weinan Zhang Shanghai Jiao Tong University, SShanghai Innovation Institute { (zoeyyx, fatcat, wnzhang ) @s jtu. edu. cn
Abstract
The rapid advancement of large language models (LLMs) has enabled the devel- opment of multi-agent systems where multiple LLM-based agents collaborate on complex tasks. However, existing systems often rely centralized coordination, leading scalability bottlenecks, reduced adaptability, and single points of failure. Privacy and proprietary knowledge concerns further hinder oraanizagaaizttonanll collaboration, resulting siloed expertise. We propose AgentNet, a decentralized, Betrieval-Augment Generation (RAG)-based framework that enables LLM- -bsed agents to specialize, evolve, and collaborate autonomously in a dynamically struc- tured Directed Acyclic Graph (DAG). Unlike prior approaches with static roles centralized control, AgentNet allows agents to adjust connectivity and route tasks based on local expertise and context. AgentNet introduces three key innovations: (1) fully decentralized coordination mechanism that eliminates the need for a central orchestrator, enhancing robustness and emergent intelligence; (2) dynamic agent graph topology that adapts in real time to task demands, ensuring scalability and resilience; and (3) eetrieval-based memory system for agents that supports continual skill refinement and specialization. By minimizing centralized control collabora-
and data exchange, AgentNet enables fault-tolerant, tion across organizations. Experiments show that AgentNet achieves higher task accuracy than both single-agent and centralized multi-agent baselines.
1 Introduction
By leveraging collective intelligence through parallel decision-making or workflow collaboration, LLM-based Multi-Agent Systems (MAS) have emerged as a promising framework for tackling com- plex real- world problems 20 26 27). However, most MAS following the workflow collaboration paradigm rely heavily on a centralized controller or a static, predefined workflow to allocate tasks among agents with fixed roles 22 9 22 25 29 While such designs simplify orchestration, they also introduce inherent ceestraints-includee
limited scalability, a single point of failure, and challenges oross-organizztiinnal collaboration due to privacy and proprietary knowledge concerns.
more critical drawback arises from the inability of these systems to adapt to real-time fluctuations in agent performance or rapidly changing task requirements. Relying central controller inflates deployment complexity and restricts dynamic role reassignment, rendering the system vulnerable when the controller fails or becomes overloaded. Furthermore, rigid role definitions prevent agents from flexibly leveraging their full expertise in dynamic environments, ultimately undermining both efficiency and scalability. Taken together, these limitations highlight the need for more decentralized,
"These authors contributed equally to this work. i Weinan Zhang is the corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Pre-Defined vs Self- Evolving Multi-Agent System
Pre- Pre-Defined Agents
Self-Evolving Agents (AgentNet)
hierarchical, static, single point failure
Adaptive, decentralized, fault-tolerant
C8
Task
Central Controller
Re-roette
Agent A
Agent
Agent X
Agent Fixed Role
(Execuorr)
Executor) Ruuter
Fixed Role)
Agent Z Fixeed Role
Agent C
Esecuter
Router
Answer
Key Features:
Key Features:
Static role assignments
Dynamic expertise development
Centralized lask management
Autonomous task routing
Rigid workflows
Experience-driven adaption
Single point of failure risk
Decentralized coordination
Figure Conceptual comparison between traditional Pre-Defined Multi-Agent Systems and the proposed Self-Evolving AgentNet. While conventional systems rely on hierarchical and static structures with centralized control, AgentNet features adaptive, decentralized coordination and dynamic expertise evolution, enabling robust and scalable performance.
fault-tolerant approaches that support dynamic task allocation, enhance adaptability, and safeguard privacy across organizational boundaries.
Beyond the scalability and failure-tolerance issues previously discussed, centralized architectures become even more problematic when organizations attempt to collaborate at scale [27] 19]. Each iestitution-be it enterprise, research lab, or government agency typically holds proprietary expertise, sensitive data, or both. In a centralized setup, concerns over data ownership, privacy regu- lations, and inconsistent governance often create barriers that prevent free exchange of knowledge. As result, LLM-basee agents contributed by multiple organizations remain siloed, unable to fully capitalize on each other's specialized capabilities or datasets. This fragmentation not only hampers collective intelligence but also highlights the urgency of developing secure, decentralized collabora- tion mechanisms. By enabling each participant maintain and share only the minimal necessary information, these mechanisms address data confidentiality requirements while still allowing for richer, more collaborative multi-agent ecosystem.
To address these challenges in multi-agent systems, we propose AgentNet, a novel framework designed foster adaptíve agent evolution, optimize task coordination, and preserve privacy. In real-world deployments, each agent in a multi-agent system may have its own role, along with private databases, tools, and other unique resources. The quality and domain of these databases directly influence the agent's capability through training parameters or demonstrations. In AgentNet, we model this reality using a RAG-based mechanism, enabling each agent maintain distinct knowledge, perform unique updates, and retrieve information effectively. By climinating the reliance on a central orchestrator, AgentNet enables agents to dynamically reconfigure their connections and redistribute tasks, forming a self organizing, fault-tolerant architecture. Within this architecture, tasks are efficiently routed via Directed Acyelic Graph (DAG) [I which supports flexible collaboration and prevents cyclic dependencies.
Unlike traditional MAS frameworks that fix each agent's role (as shown in Figure 1] AgentNet memory mechanism to refine agent expertise over time. In real-world deployments, each agent multi-agent system may have its own role, along with private databases, tools, and other unique resources. The quality and domain of these private data sources directly influence the agent's capability through training parameters or demonstrations. To model this reality, each agent in AgentNet maintains a Limiedd-capacity pool of successful task trajectories and domain-specnt relevant trajectories through fhw-shot learning to improve its decision-makinn maainn and adapt its expertise. To prevent memory overflow, agents autonomously prune less pertinent trajectories, ensuring the retention of valuable knowledge. This dynamic specialization strategy not only streamlines task allocation and agent adaptation but also supports a highly scalable and privacy-preserving environment for multi-agent collaboration.
incorporates a retrieval-based RAG [3 6
knowledge. When a new task arises, the agent retrieves the most

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Initialization
System Design
Task Processing
Evolved AgentNet
Fell connectionn Incistration
T.lleck status frewzed Zxpllt Sisecutte
Graph based cornaction
Empty neuter
Acston To Agenet by Task Task-Abilty Hatzh
Execudor pool with afuste traecttoiie
Amatss Soncuter woo!
Roster pool with private trajectortes
central controllee
Agrnt
Agant BANUUe
DAG Routing
Agent
Agunt
Agant 1 Eleoeiss
Agent
Agani
Agant
Farnars
HAG Peols
Feee Shott Learang
Faarardd
Agent-3 Tamalall
Agent
Agant EuTaRO
Agent
Agent4
Ability Decay & Comrinsous Learnin
Agant
Jobly
Leocata
after encation
Ainal&mseer
Figure 2: Illutration of AgentNet. Initially, agents are fully connected and equipped with exccutors and routers. The system eliminates the need for central controller, using a DAG for dynamic task routing and agents leveraging RAG pools and few-shot learning. In the evolved phase, the network adapts with agents developing private trajectories and diversified abilities.
AgentNet's core design is built upon several key innovations:
Fully Decentralized Paradigm: By removing the need for a central orchestrator, AgentNet fosters emergent collective intelligence. Decision-making authority is distributed across all agents, thereby eliminating single points of failure and allowing each agent to coordinate, delegate, and specialize as conditions evolve. This approach leads to a self-organizing and fault-tolerant architecture that can rapidly respond to new tasks and unforeseen chal- lenges. This decentralized setup also encourages emergent collective intelligence- in other words, agents can collectively discover and refine optimal strategies rather than waiting for instructions from central controller.
Dynamically Evolving Graph Topology: AgentNet employs network structure in which both nodes (agents) and edges (agent-to-agent connections) adapt in real time based on task demands and agent performance. Rather than relying on fixed workflows, the system continuously reconfigures its topology to optimize information flow and task distribution, ensuring scalability and resilience in complex, changing environments.
Adaptive Learning Mechanism for Expertise Refinement: AgentNet's third innovation is its retrieval- barseeal-based memory system, enabling agents to capture and update knowledge from successful task trajectories. This mechanism continuously refines each agent's specialized skills without altering the network's 's topology, allowing agents to avoid over-reliance outdated information and sustain high performance in dynamic scenarios.
Moreover, each of these three innovations inherently enhances data privacy. By eliminating a central orchestrator, every agent stores and processes knowledge locally, sharing only minimal task-relevant metadata. The dynamic graph topology further confines data flow to necessary agent-to-agent interactions, reducing the exposure of sensitive information. Meanwhile, the retrieval- memory mechanism restricts how much and how long data retained, pruning outdated trajectories so that only high-value knowledge persists. Together, these design choices safeguard privacy and intellectual property, particularly crucial for cross-organizational collaborations.
Our experimental evaluation shows that AgentNet significantly outperforms traditional LLM-based multi aulti-agent frameworks in dynamic environments, demonstrating improved task efficiency, specializa- tion stability, and adaptive learning speed. These results highlight the effectiveness of decentralized evolutionary coordination in large-scale AI ecosystems.
2 Related Work
2.1 LLM-basee Multi-Agent Systems
LLM -baaedd multi-agent systems (LaMAS) have rapidly evolved, with early frameworks like AutoGen 25 and MetaGPT [9] establishing structured, centralized workflows. These systems enabled effective
coordination but suffered from scalability issues, single points of failure, and limited adaptability. Subsequent work, such as AgentScope (5] and MegaAgent [22), introduced modular and hierarchical
3

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designs to improve robustness. However, they remain centrally orchestrated and typically rely on single LLMs, with static task workflows that hinder dynamic adaptation. AgentNet departs from this centralized paradigm by introducing fully decentralized architecture. Agents specialize dynamically, collaborate via DAG-structured network, and evolve their expertise through retrieval-based memory, enabling scalable, fault-tolerant coordination.
2.2 Evolutionary and Adaptive Agent Systems
Inspired by biological evolution, several frameworks optimize agent behaviors through prompt evolution A [2 topology adaptation 35 15 and role specialization [3 [6]. While promising.
most operate under centralized control and focus on individual agents rather than system-level
decentralization. Recent advances such AgentSquare [18] and EvoMAC [TO] explore automated
workflow design and self-adaptive strategies, yet often lack mechanisms for scalable, decentralized coordination. AgentNet addresses this gap by combining evolutionary learning with decentralized control. It enables heterogeneous agents to adapt roles and strategies in real time, supporting scalable and dynamic collaboration across large agent networks.
3 Methodology
3.1 Preliminary of AgentNet
Unlike traditional MAS frameworks with fixed agent roles and rigid workflows using central co- ordinators, AgentNet creates privacy-preserving, collective intelligence multi-agent system with high scalability and failure-tolerance by leveraging an innovative framework, consisting of a fully decentralized network architecture, dynamic task allocation mechanism, and an adaptive agent learning method, as illustrated in Figure 2
We begin with brief introduction of AgentNet, including notation and basic architectures of agents employed. Formally, we define AgentNet as tuple 9 = (A, E). where A = {a1, 02 ..., an) represents the set of autonomous agents, C = {e1, C2 ...., On J represents each agent's 's ability, and CC A A represents the communication connections between agents, specifically ea E E referring to unidirectional connection from Agent to Agent aj. For each agent a, E A contains two key components. rou, is an agent router, responsible for analyzing received routing queries and making routing decisions. ere; is an agent executor, responsible for responding executing queries through operations and tools. The two components mentioned above are underpinned by substantial LLM that leverages its extensive knowledge and understanding solve specific problems. Furthermore, both rou; and exe; in a, maintain fixed-size memory modules M,," and Mr respectively, providing a; with powerful adaptive evolutionary capabilities by storing and utilizing the agent's ' experiences through the RAG mechanism.
For optimization, AgentNet will be given series of tasks denoted T = (11, 12.,, (M) to resolve, along with an evaluation function Eval(-). The optimization goal of AgentNet is to maximize the evaluated score by E-va((-) for the solution output by AgentNet, specifically optimizing A and E, as the following formula:
g = (A", (A" = arg max (G,
A.E
3.2 Decentralized Network Topology
Mathematically, we represent the architecture of AgentNet as 9m = (Am, Em) when given the m + 1-th task tm+11 after completing task tm. where Am {a ay ...., ....,,;; ai represents the states of agents after task fmm and mm C Am x Am represents the set of directed edges between agents and each edge err means a directed edge from a!" to a." A weight matrix wm will be maintained throughout all the tasks before fm+1 to weight the connection between agents, namely (i. After completing tm+1, Wm-1 can be updated using the following formula from Wm:
Wm++ 1 (i, a W. (i, j) (1 (1-a) S(a," .ay mm ,mm+1),
(2)
where a E [0, 1] is a decay factor that balances historical performance with recent interactions, and S(a5++1 ay m-1 (m+1) is success metric for task 4m+1 routed from agent a; a to a ait+,, This adaptive weighting mechanism ensures that the network continuously refines its structure based on operational experience.

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As shown in Figure AgentNet employs dual- role architecture where each agent a, consists of router rou; for task distribution and an executor exe; for task execution. Details about these mod- ules will be discussed in the following sections. Crucially, the router enables AgentNet's decentral- ized structure, each agent independently makes routing decisions without relying on any central- ized coordinator. This decentralized approach con- trasts traditional L.LM-basd multi-agent systems, which typically rely on a central controller for task allocation. In AgentNet, agents autonomously de- cide task routing based on local information and specific task requirements. This ensures distributed greision-making eliminates single control points, and achieves full decentralization.
Agent Internal Workflow
Router Memory
Router
Task from Agent
Task to
desoriatinn
Agent
Executor
Trajectory
Memory
Agent
Agent B
Agent C
Figure 3: Dual-role agent architecture.
Over tasks, the weight matrix Wm will evolve based on collaborative success, and the edges with lower weight than a
threshold Ow are periodically pruned:
E Em+1 ((a?+
: ag a wm+1(i,jj)
> }-
This pruning mechanism ensures that the network maintains efficient pathways while climinating unproductive connections, optimizing both communication overhead and routing efficiency.
3.3 Adaptive Learning and Specialization
AgentNet's adaptive learning mechanism allows agents to continuously improve and naturally specialize through task experiences, without explicit role assignments. This capability distinguishes AgentNet from static multi-agent systems, enabling it to adapt dynamically to evolving requirements. Agents utilize the ReAct (Reasoning + Acting) framework (28] [4], reasoning carefully about task queries and contexts before executing actions. To enhance their reasoning, agents employ Retrieval- Augmented Generation (RAG) 13 34 retrieving relevant fragments from past experiences stored in memory.
Specifically, each agent a; E A maintains two dedicated memory modules: Mr for routing and Mixe for execution. These modules store fragments of trajectories corresponding only to steps involving the agent itself, rather than full task trajectories involving all agents. For each memory type E (rou, exe]. each memory fragment f (o", c. a²) consists of an observation of (task query), context c" (partial task history), and action a" (agent's 's response). Upon receiving a new task fm+1. the agent retrieves the l most relevant fragments from each memory module, formally defined as:
Select (MT,, (m+1, k)) = arg max FCM
sim (embed(of, cf), (o ( Goetz
(4)
SEF
Here, embed(-) a semantic embedding function that projects the input context into a high- dimensional vector space, and the fragments with the highest relevance are retrieved to inform the agent's reasoning or action for both routing and execution processes.
Both the reasoning and acting processes are enhanced by the retrieval of historical task fragments, allowing the agent make better decisions based on prior experiences. The reasoning function for each module type is modeled as:
Ra, (1 Freason
(otoon(Otmm: (Otm 1 Cr 1 UT}
(5)
where Freason represents the large language model that serves as the backbone of the LLM Agent, processing the inputs to generate reasoned decisions. The reasoning function R., takes the current observation and question o the historical context Ctm4. representing the partial task trajectory and interactions up to the current point, and the retrieved fragments / as input to generate the reasoning output. The fragments allow the agent to reason based on prior experiences that are most relevant to the current situation.
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Tark gro pro.nggtiinn followaa BMG wehenn uptited
Finite State Machine Allocation
State Transition Table
Prohibited
Berward
Tarword
Pre statt
Split
Forward
Enecurte
Agres
Forvard
Spiit
Tebliadt
bapk
Plami Tock fog Jark For Soigh
ADRE
What agentZ see?
Execcaee
Forward
Task lesplited atterbegg baine
fnnerdeed
Tesh
Twriss easc.eed
tock2 the inswer forcas-ll
question a answer tosklls
Env
Figure 4: Details of Dynamic Task Allocation.
Once the reasoning process has been completed, the agent executes the chosen action. The action is informed by the reasoning output, which can be expressed as:
Au, (fm+1, Faa(O0m41 Clm+1 Ra, (fm=+1,)) r). (ff))
(6)
where Fact represents the large language model that serves as the backbone of the LLM Agent, translating reasoning into concrete operations. The Au, (lm+1: r) function utilizes the reasoning output Ra, (fm+1, r) along with the retrieved memory fragments to determine the appropriate action. The specific action depends on the module type: for rou, the router module may produce actions such as forwarding the task to another agent or splitting it into subtasks; for r ere, the executor module generates a single-step operation or response to directly address the final answer.
Agents employ a dynamic memory management strategy, evaluating stored trajectories based on task context, historical usage, frequency, recency, and uniqueness. When memory module reaches capacity (Cmax), agents remove the least useful trajectories to maintain a high-quality memory pool through a prompt-based reasoning process. This adaptive process enables agents to naturally specialize over time, optimizing performance across diverse tasks.
3.4 Dynamic Task Allocation
The dynamic task allocation mechanism in AgentNet enables efficient distribution of tasks without centralized coordination, creating responsive system that optimizes both performance and load balancing. Each task / E formally represented as tuple (0r, Pt), where Or contains the task description in natural language, is a vector of capability requirements, and pt denotes the priority level. To efficiently process a new task 1 after completing task lm. AgentNet employs a sophisticated mechanism to select the most suitable initial agent. Agent capability representation and matching form the foundation of task allocation. Each agent a." after completing task fm. possesses a capability vector c that is dynamically updated through task performance during system operation. In the initial allocation phase, the system selects an entry agent for fm+1 using the following formula:
asnitial argmax (sim(Ctm41 cU )
(7)
a, a.m.m
where Ct = + (0t...))) represents the capability requirements of task (m++1 cu denotes the capability vector of agent a;, and sim(:.) is similarity function measuring the match between task requirements and agent capabilities. The capability requirements are determined through different methodologies depending on task complexity:
1 atoomii (bm+ (1 1);
for atomic tasks
Cl
compound (1 for compound tasks.
(8)
For atomic tasks, the system uses function atoomic that maps task properties to capability re- quirements based on predefined heuristics. For compound tasks, the function & compouud utilizes an instruction set with carefully designed prompts to guide the large language model in analyzing task descriptions and inferring the required capability vectors. Agents are then ranked by their capability matching scores, and the highest-scoring agent selected as the initial executor.
Once a task is assigned to the initial agent, the agent determines how to process it based on the reasoning results from its router module rou; As shown in Figure the agent can perform of three operations:
1. Forward (Orwa): Transfer the task unchanged to another more suitable agent, maintaining the task's original state and preserving the Directed Acyclic Graph (DAG) property of the

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Table Performance comparison of different methods various tasks. In all multi-agent methods, agents for each method to ensure fair comparison. The best result bold, while second is underlined.
Backbone
Category
Method
MATH(Acc/%0
BBH(Acc/%) %)
API-Bank(Acc/
Direct
47.86
69.00
26.00
React
77.14
88.00
29.00
Single Agent
Synapse
89.22
92.00
28.00
Self- Colss-Cssssteccyy
88.00
85.00
29.00
DeepSeek-V3
Self-Refinement
87.14
84.00
25.00
MorghAgent
39.29
56.00
16.00
Multi-Agent
MetaGPT
92.14
64.00
22.00
AFLOW
91.67
88.00
28.00
GPTSwarm
72.14
90.00
21.00
AgentNet
92.86
94.00
30.00
Direct
31.43
59.00
15.00
React
55.71
80.00
24.00
Single Agent
Synapse
77.14
79.00
22.00
Self-Consie Consistennyy
54.28
85.00
22.00
GPT-4o-mini
Self Refineeeit
68.57
81.00
23.00
MorghAgent
80.71
56.00
16,00
Multi-Agent
MetaGPT
73.57
53,00
19.00
AFLOW
85.00
75.00
21.00
GPTSwarm
85.00
86.00
13.00
AgentNet
85.00
86,00
29.00
Direct
37.85
57.00
27.00
React
53.57
69.00
23.00
Single Agent
Synapse
67.14
68.00
24.00
Self CanfCCssistncyy
64.28
70.00
28.00
Qwen-turho
Self-Refinement
76.43
74.00
23.00
MorghAgent
16.43
56.00
9.00
Multi-Agent
MetaGPT
63.57
51.00
20.00
AFLOW
82.14
57.00
22.00
GPTSwarm
79.29
75.00
30.00
AgentNet
81.43
92.00
32.00
routing path. Forwarding decisions based on analyzing the gap between the current agent's capabilities and the task requirements, as well as evaluating the capability vectors of other agents in the network.
2. Split (O (Ospla): Decompose the task into subtasks, execute portions matching the agent's expertise, and route the remaining subtasks to an appropriate agents. Subtask routing follows this formula:
argmax (sim(((o:m4)))) )
apert
a.E.Am Ma.)
where ((otm+1) represents the capability requirements derived from the observation of subtask j. determined through the current agent's 's task decomposition reasoning, and Am {a,} denotes the set of all agents excluding the current one.
3. Execute (Oxxe)): Complete the entire task without further delegation.
key design feature in the system is that when an agent chooses to split task, it only forwards the results of the subtasks it has completed, and not the reasoning behind the decomposition. This prevents the transfer of unnecessary information and ensures that task decomposition emoos made by one agent do not propagate other agents in the network.
The agent capability vector cos" is updated based on task execution history and success rates, using the following formula:
c m+1 3 cot" +11 3) Acm+1
(10)
where 3 [0, 1] is decay factor balancing historical capabilities with newly acquired ones, and Act+I represents the capability contribution demonstrated by the agent in task fm+ calculated by analyzing the types operations successfully executed by the agent and the quality of results.
The task state updates only when an agent completes a part-by executing splitting it. Upon subtask completion, the agent updates the context and passes the next agent:
contextypdated = contextangiinal @ result(a,,
(11)
While the task is simply forwarded from one agent to another, state remains unchanged, preserving the Directed Acyclic Graph (DAG) structure of the routing path. This prevents infinite loops and
7

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ensures effective task progression across agents. Through this dynamic task allocation mechanism, AgentNet adaptively optimizes task flow based on task characteristics and evolving agent capabilities.
4
Experiment
4.1
Main Results
Table summarizee performance across Math, Coding, Logical QA tasks and API-Calling tasks, detailed settings and implementation details are provided in Section C For Math, Logical QAs and API-Calling tasks, accuracy is reported; for Coding, we report average test case pass rate and full problem pass ratio. Compared to single-agent methods (e.g., Synapse, ReAct), AgentNet achieves competitive or superior performance across all tasks. While ReAct performs well on Math and Logic, its static prompting strategy limits generalization to more complex tasks. Against multi-agent baselines, AgentNet consistently outperforms centralized frameworks such as MetaGPT, which suffers from limited scalability- e.g. only 53.00% accuracy on Logical QA. MorphAgent underperforms on Coding tasks, as it generates self-constructed test cases during training, resulting in invalid or uncompilable outputs. AgentNet's decentralized coordination and -aggeeeed memory contribute its robustness across domains, particularly in tasks requiring contextual understanding and adaptive role specialization.
4.2 Experiments on Heterogeneous Agents
To investigate the impact of agent diversity performance, we designed heterogeneity experiment across different settings on the BBH task. Agents were tested under four configurations: fully homogeneous (identical models and capabilities), LLM heterogeneity (different language models, same capabilities)), skill heterogeneity (same model but varied capabilities), and a combination of both. This design allows to isolate and analyze how model-level and capability-level diversity influence multi-agent collaboration.
Table 2: BBH Accuracy under Different Heterogeneity Settings (Acc%)
Setting
Fully Homogeneous
Skill Hetero.
LLM Hetero.
Both Hetero.
Agents
0.86
0.84
0.81
0.81
Agents
0.79
0.86
0.85
0.85
Results in Thhle that the impact of heterogeneity on performance depends team size. With agents, the fully homogeneous setting performs best, while introducing either model or skill diversity reduces accuracy, suggesting uniform reasoning is more effective in small teams. However, with 5 agents, heterogeneous configurations outperform the homogeneous one, indicating that diversity enhances collaboration and complementary reasoning in larger teams. Overall, heterogeneity may introduce coordination overhead in small groups but offers clear benefits at larger scales.
4.3 Ablation Study
Router Effectiveness in AgentNet To evaluate AgentNet's decentralized router, experiments were conducted comparing AgentNet with ablation configurations: "Totally Random", "Random Operations", "Random Next Agent ID" and centralized "Global Router". Each router manages external routing (selecting the next agent) and internal routing (deciding to forward, split, or exccute).
Performance tested the BBH task (train- ing: 627 problems, testing: 100 problems), with results in Figure 5 AgentNet outperforms ran- domized methods, achieving 82. 14% accuracy during training and 86.00% during testing. Ran- domizing operations (forward//slidexxeuut) af- fects task execution more directly, randomizing next agent ID primarily results in suboptimal task delegation but does not disrupt task completion as severely. These results underscore the critical role of effective routing and suggest that optimiz- ing routing decisions can significantly enhance multi-agent system performance.
Agentiket's Router Perforaace on BBH
The Tonz
Truis 0h2/po 110021
SL 40802.00
Accoracy
24.0
>0
Fardor fandon Cootstore
Jindon Agert Gyrs Nauter
Figure 5: AgentNet's Router Performance on the BBH (Backbone: gpt-4o-mini)
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Impact Evolution Phase Results in Ta- ble clearly indicate that AgentNet significantly improves performance compared to the non- evolution baseline. On the MATH task, Agent- Net achieves score of 85.00 versus 77.86. For the function-calling task, performance improves notably from 23.00 to 32.00, and BBH task ac- curacy rises from 76% 86%.
Table Performance Comparison of AgentNet vs. Without evolution (Backbone: gpt-4o-ming mini)
3 Agents
MATH
API-Bank
BBH
(Acc)
Acc
(Acc)
w/o evolution
77.86
23.00
76.00
AgentNet
85.00
32.00
86.00
These results confirm that AgentNet's adaptive learning during the evolution phase effectively enhances agent specialization and task performance, demonstrating its essential role in the system's ' optimization and overall efficiency.
4.4 Analysis
Scalability and Robustness of the System As shown in Figure 6 both training and testing performance of AgentNet improve slightly with increased number of agents and a larger executor pool on the BBH task. Training accuracy rose from 80.38 (3 agents, 30 executors) to 81.18 (9 agents, 40 executors), while testing performance ranged from 80 to 86, peaking with 40 executors. These incremental gains highlight AgentNet's ability to scale efficiently through decentralized coordination. While performance improves with more resources, the diminishing returns suggest an optimal configuration exists. Overall, the experiment confirms that AgentNet's 's adaptive design supports scalable, efficient, and fault-tolerant multi-agent systems.
AgentNet Performance Different Parameters on
90.0
Performance
22.3
35.2
BC.
$6.00
81.50
82.00
82.46 83.00
82.00
Train (627ps)
Text (100ps)
82.00
22.
[3, 30)
13, 40)
13, 501
[3, 60]
(5, 40)
(7. 40)
(9, 4n)
AgentNet Parameter Settings (Agents, Executor Pool)
Figure 6: AgentNet Performance with Different Net Parameters. Experiments were conducted with routers without pool limit, where (A, B) represents A the number of agents and B as the upper limit of the executor pool, with performance evaluated on the BBH.
Evolution of Agents Networks The evolution of the agent network in our experiment is illustrated in 7 7 which demonstrates the transition a multi-agent system composed of 5 agents running on the BBH (627 pieces) benchmark. The figure captures the network at three key stages: the initial state, an intermediate state, and the final evolved state.
1
Stoge BeBlee eneuuiatio Lendaa indiurtion
network sbildisee chieiizzee epuanitee
Cheractetatte
agan cimilattion and anippatton
ferer butic
Figure 7: Evolution Example of Agents Networks.
In the initial state, the network is fully connected with uniform connection values of 1.00. indicating equal capabilities among all agents. At this stage, there is no specialization, and all agents are equally equipped handle tasks. As the network evolves, agents begin specialize, and connection values vary, reflecting the strength of collaboration. Stronger connections indicate tighter cooperation, while weaker ones suggest less interaction. This evolution shows how agents naturally adapt and form more efficient collaboration patterns. By the final stage, the network exhibits clear specialization, with agents taking on distinct roles. The connection values further emphasize the growing cooper- ation between specialized agents, improving task performance. This progression demonstrates the

---

effectiveness of decentralized coordination, where evolving collaboration enhances task allocation, scalability, and fault tolerance.
Autonomous Specialization of Agents Based on the observed results in Figure g the experiments demonstrate that AgentNet's multi-agent system can naturally specialize agents decentralized environment.
Rosccing
Fasson
Seawoin
Seasoring
Maternstiaal
Molrensatual Infenecce
Methomation infrencee
Nathoratiol
Speiill
Langage
paruage
Lowledge
Aggents
Aggants
Aginnt
Agint
Agant 3
Agant 4. Agint
Agant
Agunt
Agint
Figure 8: Autonomous Specialization under Different Agent Sets. The upper limit of the executor pool fixed 40, regardless of the number of agents. Figure shows agents' fahilities after training 627 questions BBH.
With varying numbers of agents and fixed executor pool of 40 pieces, the ability scores across different tasks such reasoning, language, knowledge, and sequence showed significant variation. As the number of agents increased, specialization became more evident, particularly in complex tasks, with certain agents excelling in specific areas while others focused different abilities. This highlights AgentNet's ss capacity to dynamically refine agent expertise and optimize performance in a decentralized, task-driven system.
Case Studies This case study is presented to illustrate the differences between the two methods, based on results obtained using GPT-4o-mini the BBH dataset. The left image shows the trajectory produced by the ReAct method, while the right image illustrates the trajectory generated by AgentNet. In the case of ReAct, the lack of collective reasoning results in incorrect response after single-step inference, highlighting the limitations the method in handling tasks that require more complex reasoning. In contrast, AgentNet uses multi-step workflow where agents without the necessary expertise are bypassed, while those with the relevant skills divide the task into smaller steps, leading more accurate final solution.
Question:
AgentNet Agents)
Which sentence has the correct adjective order?
Agent 0: Decision: Forward Next Agent: Agent
Options:
Agent 1: Decision: Forward Next Agent: Agent
A. old-fashioned circular green cardhoard exercise
Agent 2: Decision: Forward Next Agent: Agent
computer
Agent 3: Decision: Split
B. old-fashioned green exercise circular cardboard
Executable: Adjective order analysis
computer
Delegate: Final answer another agent
ReAct
Findings:
Reasoning: (B) was selected because follows a common pattern in English, where opinion adjectives (c.g., fodd-fashioned") precede color
Quantity, opinion, size, age, shape, color, origin, material, purpose Correct Option: (A)
Correct order:
Next Agent: Agent 4
adjectives (e.g.. "green"" and shape adjectives (e.g., "circular").
Agent 4: Decision: Execute Action: Confirmed option (A)
Action: (B) Truth:(A)
Ground
Final Outcome: (A) Truth:(A)
Ground
Figure ReAct Response with Reasoning
Figure 10: AgentNet Task Brcakdown
5 Conclusion
In conclusion, AgentNet provides an effective approach to addressing the limitations of traditional centralized multi-agen agent systems. With its decentralized architecture, dynamic task allocation, and adaptive learning mechanisms, AgentNet improves scalability, fault tolerance, and task efficiency in collaborative environments. Its
features further ensure secure cooperation across organizations. Our experimental results highlight the advantages of this approach, demonstrating im- provements in task efficiency, adaptability, and specialization. AgentNet offers practical framework for developing more flexible and secure multi-agent systems in dynamic, real-wor wolld settings.
10

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6 Acknowledgment
This research was supported by Bytedance through a sponsored project that facilitated the execution and completion of this work. The Shanghai Jiao Tong University team is partially supported by National Natural Science Foundation of China (62322603).
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A Algorithm of AgentNet
A.1 Pseudocode of AgentNet
The AgentNet System is designed to optimize task allocation and agent coordination within multi- agent environment. This algorithm orchestrates the interaction between tasks and agents, aiming to efficiently distribute tasks based on agent capabilities and historical performance.
Algorithm AgentNet System
Require: Task set T (t1, t,....., tM
Ensure: Optimized network G" = (A)
1: Initialize A = {ai, 02, ). &, fel, (2,,,,,... wo, Mç and Mşfure Va, A
2: for each task 1m+1 E 7 do
3: 1. Task allocation and processing
4: Ctm.11 ((ttm)))) acurr arg max,; EA... sim Ctm CU
5: bosk_state- + (0tm41: 0. Ptm...)).. visited +- 0. finished + false
6:
while not finished and acurr $ visited do visited +- visited U (acurr)
7:
8:
fragments"on &- Melect(Mrron, cupr tm+ k)
9:
action +- Fact (0t (Otm=1> -1 Clm+1 : Freason (1 Otm+1: fragments"""" fragments if action O wd then
10:
11:
Opurr & arg m ((Gewr)) sim Cr 1 %k)
12:
else if action sptit then sublasks + Decompose' TaroooseTTaa((.... Task(Im+1)
13:
task state.contezt
task sonteet
ProcessSubtasks(suss (subtakkss acurr, Am)
finished AlSSubtaskSoomlleted (subbraa
16:
else
task_ state.contezt
task_ state.conteat
Execute (asku (curr, 1m+ Select (Mose, tm+ k)) finished true end if end while
19:
21:
// 2. Network update
22:
for each interacting pair (a,, 0,) do
23:
Wm+1(i,j) + a. wm (1, + (1 - a) S(a,tti, .aatt! ,aa 1mm+1) end for
24:
25:
Em++1 + (aa .a ..3+t) wm++i(ij)) (i,j) > 0w)
Agent capability and memory update
27:
for each participating agent do
28:
cm+ B cvLh +(1 - (1-)) Act+t, Update My and More
end for
30:
end for
31:
return G (A,E)
This algorithm provides a systematic approach to managing agent-task interactions, enhancing coordi- nation efficiency, and supporting adaptive learning and specialization within complex environments.
A.2 Scalability Analysis AgentNet
We analyze the scalability and computational efficiency of AgentNet when deployed with large number of agents. As shown Figure 6 AgentNet maintains high and stable performance as the scale of the multi-agent network increases, demonstrating its strong scalability and robustness under network expansion.
After each round of task execution, AgentNet updates its parameters through several lightweight operations. Assuming there are agents and each agent's memory contains entries (while ignoring asynchronous parallelism), the computational costs are follows:
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Agent memory updates: Updating each agent's memory requires ((m) time, leading to total cost of (km).
Capability vector updates: Each agent's ' capability vector update takes ((1) time, resulting in a total cost of (kk).
Topology updates: If a task execution trajectory involves agents (i.e., (t 1) edges), only the communication edges among these agents need to be updated, which costs (t), and typically / & k.
Communication overhead: Each agent only communicates with directly connected agents, avoiding expensive global broadcast operations.
Taken together, the overall time complexity of AgentNet's memory and parameter updates (kk)
in large-scale applications, which highlights its high scalability.
Moreover, thanks its fully decentralized design, each agent in AgentNet can be deployed on separate devices, enabling asynchronous execution of subtasks and independent updates of memory and capability vectors. This design substantially reduces both time and bandwidth overhead in large-scale distributed environments.
B Supplementary Review of Related Literature
B.1 LLM-based Multi-Agent Systems
The development of LLM-based multi-agent systems (LaMAS) (27 has advanced rapidly in recent years. Early frameworks, such as AutoGen [25] and MetaGPT [9. made significant strides in establishing foundational architectures for orchestrating multiple LLM agents through structured workflows. AutoGen provided a flexible framework for defining agent interactions, while MetaGPT incorporated software development principles to enhance collaboration. These centralized frameworks proved effective for managing multi-agent interactions. However, they also faced inherent challenges, including limited scalability, single points of failure, and difficulty dynamically adapting to evolving tasks or incorporating new expertise.
In response these limitations, more recent frameworks such as AgentScope and MegaAgent [22] have focused on improving robustness and scalability. AgentScope introduced modular de- sign patterns enhance system reliability, while MegaAgent employed hierarchical structures to scale agent interactions. Although these frameworks offer improvements, they still operate under centralized control paradigms, with master agent delegating tasks, which continues to lead to scalability bottlenecks and single points of failure. Moreover, existing LaMAS implementations predominantly utilize single-source LLMs, lacking the integration heterogeneous models. Their workflows are typically static, unable to dynamically allocate resources based on task complexity, further constraining adaptability.
In contrast, AgentNet introduces novel decentralized approach, addressing these challenges by enabling agents to autonomously refine their expertise and dynamically allocate resources. AgentNet supports scalable, fault-tolerant collaboration without reliance on central orchestrator, overcoming the limitations of centralized frameworks.
B.2 Evolutionary Agent Systems
Inspired by natural evolution, recent researchers have explored evolutionary approaches to automate and optimize agent behaviors and workflows in LaMAS. Existing efforts can be broadly categorized into the following areas:
Prompt Evolution and Optimization Techniques such PromptBreeder 4. DsPy 12
and AgentPrune 30 apply evolutionary algorithms to iteratively refine prompt generation, improving task performance through better input design.
Inter-Agent Topology Optimization - Systems like GPTSwarm 35 DyLAN 15), and G-Designer 31 focus evolving the structural organization of agent interactions. These works aim to optimize communication patterns, task allocation, and collaboration efficiency within multi-agent networks.
15

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Agent Role and Persona Specialization Frameworks such as Agent Verse and Mor- phAgent [ [6] refine agent roles and profiles, enabling more effective specialization and coordination among agents in complex tasks.
While these evolutionary approaches have shown promise, they primarily focus individual agent adaptation rather than collective coordination. Additionally, they still tend to operate within central- ized control structures, which limits their scalability and dynamic adaptability. Recent frameworks like AgentSquare [8] and AFlow (32) have begun to formalize automated design processes for agen- tic systems, improving system-level orchestration and workflow automation. Another key direction is self-adaptive agent architectures, where agents adjust their strategies in real-time based on feedback and accumulated experience. For example. EvoMAC [IO] combines reinforcement learning with evolutionary algorithms to optimize agent decision Decision-making and policy updates.
However, these approaches are often limited to single-agent adaptation and lack mechanisms for decentralized specialization and coordination across large-scale agent collectives. While EvoMAC and other systems focus on optimizing individual agents, they are not designed for scalable, multi-agent, decentralized collaboration. In contrast, AgentNet integrates evolutionary learning with decentralized control, enabling heterogeneous agents to dynamically evolve their roles, adapt their strategies in real-time, and collaborate flexibly across large-scale multi-agent system. This integration of evolutionary learning with decentralized control makes AgentNet more suitable framework for real-time, adaptive, and scalable multi-agent collaboration.
C Experimental Setup
Tasks and Benchmarks
We evaluate methods using several benchmarks across three task cate- gories, along with custom constructed training and test sets for each benchmark:
Mathematics: This task involves mathematical problem and is evaluated using MATH 8 which includes problems with different types. The training set consists of 100 examples per type (total of 700 problems), while the test set consists of 20 examples per type (total of 140 problems).
Logical Question Answering: This task tests reasoning and logical question answering abilities using the BBH (Big-Bench Hard) benchmark [21]. The training set follows the MorphAgent setup, selecting 627 examples from 20 tasks. For testing, each task has examples of varying difficulty, totaling 100 test problems.
Function-Calling This benchmark evaluates the agent's ability perform tool-augmented task planning and API usage, based on the API-Bank dataset [14]. We construct a training set of 100 tasks and a test of 100 tasks, randomly sampled from the full API-Bank corpus. Since the original dataset does not include category labels, we annotate each task using GPT-4o-mini to assign one of the seven task types: health, account, schedule, information. housework. finance, and others. Each task is further categorized into one of three difficulty levels, determined by prompt complexity and required toolchain length.
Metries A range of evaluation metrics have been adopted for different tasks. For the Mathematics and the Logical Question Answering tasks, the accuracy metric is utilized to evaluate the consistency of the output answer with the true answer within the specified format. For the Coding task. the average test case pass rate (i.e., the ratio of the number of passed test cases to the total number of test cases) and the ratio of problems passed across all test cases have been employed as the evaluation metrics.
Baselines
We compare AgentNet with two categories of baselines: single-agent and multi-agent
frameworks:
Single-agent frameworks: These methods involve a single agent solving tasks indepen- dently without collaboration or coordination with other agents.
Direct: A baseline approach where the LLM directly generates outputs.
Chain of Thought: prompting technique that elicits step-by-step reasoning from
language models [24].
16

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Task Delegazion with
Agent
Fanwort: D Rices anaweeth
Task In
AgentA
Agent
Task
Fo Fonwer:: Lon, roperance
Sigit Aanipoli Fralor
Agent
Agent
Agent
Task Out
Task In
Agent
Figure 11: Illustration of the execution process (passing and forwarding) of tasks in AgentNet
Synapse: A trajectory-as-exxet as-exemplar prompting method, which prompts the LLM with complete trajectories of the abstracted states and actions to improve multi-step decision-
making. R3
Self-Consistency: A decoding strategy that samples multiple reasoning paths and selects the most consistent answer through majority voting, enhancing reliability (23].
Self-Refinement: An iterative approach where models critically evaluate and improve their own solutions over multiple passes, progressively enhancing solution quality [77]
Multi-agent frameworks:
These methods involve multiple agents working collaboratively solve tasks, each contributing different aspects of the task-solving process.
MetaGPT: A software development framework where specialized agents (like product manager, architect, engineer) collaborate in a waterfall workflow to complete complex engineering tasks
AFLOW: A framework that optimizes agent workflows using Monte Carlo Tree Search over code-represented workflows with exccution feedback [32].
GPTSwarm: A framework modeling agents computational graphs with automatic
optimization of both prompts and agent collaboration patterns [35
MorphAgent: A framework featuring self- evolving agent profiles that dynamically
optimize individual expertise in the profile through three metrics [16]
Parameter Configuration In our implementation, we configure the LLM API with temperature of 0.0, a maximum token limit of 2048, and a top-p value 1.0, ensuring consistent results throughout our experiments and enabling reliable comparisons and analysis. For the memory pool experiment, we utilize the htA////ellargeeee-vl... model to compute the similarity between task queries and database trajectories.
D AgentNet Experiment Configuration
D.1 Task to Ability Mapping in AgentNet
This table illustrates the mapping between diverse set of cognitive tasks and their corresponding core cognitive abilities. Each task is linked to one or more foundational abilitics as reasoning, inference, mathematical competence, linguistic understanding- -hihh represented using distinct color encodings to enhance visual clarity and interpretability.
Task in BBH
Abilities
boolcan_cxpressionn expressions
boolean_
reasoning
logical wee_eddiio_tthreeeeeeee
ttrre_objects,
reasoning
logical wwwddcttionfiveeeooooooo
reasoning
logical_ Dogical_deductton.sco dww.duttionseeeennooeeeeeee
reasoning
17

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Task
Abilities
causal_ jungemmjddement
reasoning, inference
formal fallacies
reasoning, inference
reasoning, sequence
tradking_shuffeeooohhctsseeeeee five five_objects
reasoning, sequence
rracking_shuffleddde cuuuflleddobjeett otw_ooccsssseeeeoorrreeeeeee seven_objects
reasoning, sequence mathematical
multistep_arithmettie two
geometric_shapes
mathematical, spatial
object_ object_counting
mathematical, spatial
word_sorting
mathematical
date_umde date_unndestanding
mathematical, language
lyck_llanguages
mathematical, language
disambiguation
language
hyperbaton
language
error detection
language
mnvie_recommendation
knowledge
sportss_understanding
knowledge
penguins_ing ji__lss________leeeeee
knowledge
reasoning_asee aaatoaacooooooduddtttteaaaaa colored_objects
knowledge
ruin names
language, knowledge
sequerel_sqquences
sequence
navigate
spatial
web of lies
inference
snarks
inference
The construction of this task-ability mapping is motivated by the objective of optimizing agent-task aligmment within multi-agent system. By adopting single-level ability classification, we establish tractable yet expressive abstraction of agent competencies that facilitates efficient task routing and capability differentiation.
This initial flat taxonomy serves foundational layer for scalable skill orchestration. It provides a principled basis for agent coordination and lays the groundwork for future extensions toward hierarchical ability framework-enablle finer-grained specialization and more precise agent-task matching complex environments.
D.2 Initial Configuratio
The initial configuration specifies the setup for each experiment conducted within the AgentNet framework. This YAML-based configuration file defines global experimental parameters, default agent initialization, and the structure of the agent communication network.
experiment_ccnffig
LLM: gpt-4o-mini
dataset: bigbenchhard
task_num: 100
agent_num: 3
forvard_path_max mappath_maxaaaaaeea men_ttthhh_nnhh
max max_executionn conettiinnsssssss ## for tuning
user_react: True
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---

default aygen__confin adefault agent_cconiit
LLM: gpt-4o-mini
abilities:
reasoning: 0,6
nathematical: 0.6
language: 0.6
knowledge: 0.6
sequence: 0.6
spatial: 0.6
inference: 0.6
executtor__emorr__tee Limit: 40
embeddingg_cache___ee limit: 1000
router_wemory_limit:: -1
decay_rate: 0.1
decay_interval: 10
rotter_ratrisval__mm:
executor_retrieval_nmm
agont_graph_contip:
graph_type: complete
Listing I: Experiment Initial Configuration YAML
The configuration includes three primary components:
Global Experiment Settings (experiment_conf/)) This section controls the overall experiment, including:
The underlying I.LM used by agents (LLM).
The evaluation dataset (dataset).
Number of tasks be dispatched (task_num) and number of agents (agent_num)
Routing and execution limits (forward_pathe path max peegghh lex__xxecuiion eet.etttoonttmmss)
Whether the React output format before taking action.
is enabled, as it allows for reasoning
Default Agent Parameters (default (default_agent this shared configuration unless overridden. It includes:
All agents are initialized with
A uniform initialization across all ability dimensions (c.g.. reasoning, language, spatial, etc.).
Memory and caching
limits
(executor_memall
leteeddinn__cche___ee
Temporal dynamics of memory decay (decay_rate, decay_interval
Retrieval settings for routing and execution modules.
Agent Communication Graph
Defines the topology of inter- agent communication. In this example, à complete graph is used, enabling each agent to communicate with all others.
D.3 AgentNet Prompt Specification
The Agent Prompt Specification defines the structured input schema received by each agent in the AgentNet system. This schema ensures that all relevant contextual, historical, and task-specific information explicitly provided before an agent begins reasoning or execution.
Rather than free-form prompts, AgentNet adopts a structured prompt interface, where each field conveys distinct aspect of the task environment. This design improves interpretability, modularity, and control, making it casier to route tasks, compare decisions, and analyze agent behavior.
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Table 5: Schema definition for AgentNet prompting.
Field
Description
ma jor_pprr
The overarching goal or task shared among agents the system.
experiences
Past task examples or performance records relevant to the current task.
task_context
Previously completed subtasks and results, used determine next steps.
current_agent, info
Information about the current agent, including its abilities and status.
task_type
The category or nature of the task (e.g., reasoning, language, etc.).
task_description
A detailed description of the specific problem to be addressed.
agent_info
Information about other agents in the system, including their abilities and prior performances.
constraints
Format or structural requirements for the expected result output.
thought
Reasoning or intermediate planning used to guide execution of the task.
This input schema acts the interface contract between the system and each agent, standardizing how agents perceive their operating context. It also lays the foundation for future work agent generalization, multi-agent coordination, and prompt optimization.
E
Limitations and Future Work
Despite AgentNet implementing a fully distributed, adaptive learning multi-agent system (MAS) with dynamic task allocation, several important limitations remain that require further exploration in future work.
One key challenge is how to improve task performance in heterogeneous agent environments. In real- world applications, agents often vary sigmificantly in terms of model capabilities, workflow structures, tools, and available data. The impact of such heterogeneity AgentNet's performance, especially in terms of task coordination and resource allocation, remains an open question. Understanding how to adapt the system to handle such variations efficiently will be erucial for its scalability and effectiveness in complex environments.
Secondly, the decision-maker making process of the router within each agent, particularly in relation to exploration and discovery, requires more in-depth study. Currently, the router selects agents from relatively small pool of predefined candidates. However, in larger-scale systems involving hundreds or potentially thousands of agents, the challenge of accurately identifying the most suitable agent for task delegation becomes significantly more complex. This problem is further compounded in heterogencous settings, where different agents may possess distinct strengths and weaknesses. To address this, future research could focus developing more sophisticated routing mechanisms that can autonomously identify and delegate tasks to the most appropriate agents, even in large and diverse agent pools.
Additionally, promising direction for future work involves designing incentives that encourage router to explore agents beyond the predefined candidate set. By enabling AgentNet to dynamically discover new agents or specialized capabilities, such an approach would enhance its adaptability and scalability, ultimately improving the system's 's overall performance and autonomy.
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Ethics Statement
This study did not involve human participants, animal subjects, or the use of personal data. All datasets and benchmarks employed are publicly available and used in accordance with their respective licenses. Therefore, no ethics approval was required.
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Reviews:
[{"review_id": "txD8rI178G", "reviewer": "Reviewer_9hci", "summary": "This 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.", "questions": "See `Weaknesses`.\n\nOverall, I acknowledge the substantial engineering effort behind this work, so I would lean toward accepting the paper.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The author's response resolved my concerns about the method's innovation, experimental results, and training costs. So I raised the score from 4 to 5 accordingly.", "rebuttal": ">**Q1.Comparison With Existing Methods**\n\n**A1**\nWe sincerely thank the reviewer for raising the concern regarding the similarity of some modules with prior work. While we share a common motivation with earlier efforts in building robust multi-agent systems, AgentNet **introduces unique innovations** that set it apart from existing approaches.\n\n1. **Our innovation in decentralized multi-agent systems**:\n\n    Our fully decentralized design stems from practical considerations. As more companies develop their own agents, we foresee agents interacting across the internet in a decentralized manner, making fully decentralized MASs increasingly necessary. AgentNet delegates routing decisions to each independent agent, allowing them to adaptively make routing choices based on dynamic environments, thereby removing the reliance on a central coordinator.\n\n    In contrast, GPTSwarm determines routing decisions at the system level, and its agents do not execute sequentially but rather perform tasks individually and then engage in parallel discussions. This is fundamentally different from AgentNet’s sequential execution. AgentPrune, on the other hand, adopts a one-shot pruning method with a relatively complex optimization process, and it lacks adaptability to dynamic real-world environments. Furthermore, unlike frameworks like AutoGen that focus on fixed workflows and predefined message paths, AgentNet enables agents to autonomously decide routing decisions, based on their own and their neighbors' state and expertise. This results in a more flexible, decentralized approach to task allocation.\n2. **Why RAG is applied in AgentNet**:\n    AgentNet highlights the importance of private knowledge and experience updates for each agent. In real-world deployments, each agent in a multi-agent system may have its own role, along with private databases, tools, and other unique resources. The quality and domain of these databases directly influence the agent’s capability through training parameters or demonstrations. In AgentNet, we model this reality using a RAG-based mechanism, enabling each agent to maintain distinct knowledge, perform unique updates, and retrieve information effectively.\n\nWe appreciate the reviewer’s thoughtful concern, and we will include a more detailed clarification of these points in the next revision.\n\n\n\n---\n\n>**Q2. Experimental results**\n\n**A2**\n\nWe acknowledge that our method does not outperform all existing approaches on the MATH dataset when using Qwen-turbo as the backbone LLM. However, we would like to clarify the following points:\n\n1. **State-of-the-art performance across multiple datasets and backbones.**\nWith the same backbone LLM (Qwen-turbo), AgentNet achieves SOTA performance on the other two datasets (BBH and API-Bank). Moreover, on the MATH dataset, AgentNet also reaches or surpasses SOTA performance when using other backbone LLMs (DeepSeek-V3 and GPT-4o-mini). This demonstrates AgentNet’s outstanding ability compared to other baselines in handling mathematical problems, logical reasoning, and other complex tasks, as well as its strong synergy with Qwen-turbo. We quote the original experimental results (AgentNet vs. AFlow) below:\n\n```\n    * DeepSeek-V3: 92.86% vs. 91.67% (MATH)\n    * GPT-4o-mini: 85.00% vs. 85.00% (MATH)\n    * Qwen-turbo: 92.00% vs. 57.00% (BBH), 32.00% vs. 22.00% (API-Bank)\n```\n\n\n2. **Further analysis of AFlow vs. AgentNet.**\n    With respect, AFlow’s MCTS-based workflow is naturally well-suited for mathematical problems that require step-by-step reasoning, which explains its relatively strong performance on the MATH dataset. However, on BBH, which represents more diverse real-world logical reasoning tasks, AFlow’s performance is comparatively weaker.\n\n    Furthermore, AFlow need a substantial computational and economic cost, as it requires a large number of LLM tokens during workflow search and optimization. This heavy cost results in only a ~1% performance gain on MATH. In contrast, AgentNet achieves comparable or even superior performance using only the task execution tokens required by AFlow, demonstrating its efficiency and effectiveness.\n\n\n\n\n---\n\n>**Q3. Concerns about empirical results**\n\n**A3**\nThank you for raising this excellent question!\n\nAt a high level, both AgentNet and AFlow require a warm-up phase on a training dataset disjoint from the test set, followed by responding to each query in the test set. \n\n* AgentNet consists of 3 agents. For each query, each agent performs the equivalent of 2 LLM calls and 4 memory retrievals (1 LLM call and 2 memory retrievals each for the router and executor). AgentNet performs 1 warm-up round on the training dataset.\n* AFlow, during warm-up, requires on average 6 LLM calls per query per round. It performs 20 warm-up rounds on the training dataset, with its best performance usually reached at round 5.\n\nCompared with AFlow, the major cost of AgentNet lies in memory construction during initialization. However, its runtime cost is significantly lower because:\n```\n1. It does not need to maintain a global state\n2. Its decisions are localized, resulting in lower communication overhead\n3. The dynamic pruning mechanism effectively controls memory growth\n```\n\nIn the warm-up stage, AgentNet only needs to roll out one round on the warm-up dataset, instead of exploring the large search space that AFlow requires. Moreover, our approach is designed to be deployment-friendly: the running network state itself naturally aligns with the warm-up state."}, {"review_id": "5Q4rHPolbM", "reviewer": "Reviewer_dvJt", "summary": "This 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.", "questions": "1. 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?", "limitations": "The motivation of fully decentralized pradigm is not convincing to me, though it will be more suitable for the topology optimization.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Pros:\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.", "was_revised": true, "final_justification": "In the authors' response, most concerns have been addressed, but the author cannot provide more experimental results to support the scalability. Hence I only raise my confidence of the review.", "rebuttal": ">**Q1.Cost When AgentNet Scales**\n        \n**A1**\n    \nWe sincerely thank the reviewer for raising this concern regarding scalability and for the careful evaluation of our work.  \n\n1. As shown in **Figure 7**, the performance of AgentNet continues to maintain as the multi-agent network scales up, which strongly demonstrates the usability and high performance of AgentNet under network scaling.  \n   \n2. More concretely, when a round of task execution is completed and AgentNet’s parameters are updated (assuming there are k agents and each agent’s memory contains m entries, ignoring asynchronous parallelism):  \n    *   **Agent memory updates**: Updating each agent’s memory requires O(m) time, resulting in a total cost of **O(km)**.  \n    *   **Capability vector updates**: Each agent’s capability vector update is O(1), leading to a total cost of **O(k)**.  \n    * **Topology updates**: If the task execution trajectory involves t agents (i.e., \\(t-1\\) edges), only the communication edges among these t agents need to be updated after the task is completed. This costs **O(t)**, and in practice, t << k.  \n    * **Communication overhead**: Each agent only communicates with directly connected agents, avoiding expensive broadcast operations.  \n\n   Taken together, the time complexity of AgentNet’s memory and parameter updates is only **O(km) **under large-scale applications, demonstrating its high scalability.\n\n3. Furthermore, because of fully decentralized design, in real-world large-scale applications, each agent in AgentNet can be deployed on different devices and can asynchronously execute subtasks and update its memory and capability vectors without consuming the time or bandwidth of other devices. This greatly reduces time overhead in large-scale deployments.  \n\nDue to current resource limitations, we will include a detailed comparison of LLM token consumption and other runtime costs between AgentNet and the baselines in future versions. We once again thank the reviewer for raising this important concern.  \n\n\n---\n\n>**Q2. Why decentralized better than centralized**\n    \n**A2**\n\nWe sincerely thank the reviewer for raising this important question regarding the advantages of decentralization. We clarify the motivation and provide experimental and theoretical justification as follows:\n\n1. **Diversity and privacy protection.**  \n   In real-world multi-agent systems, participating agents are often highly heterogeneous, belonging to different institutions or organizations, each with their own data, tools, and expertise boundaries. A decentralized framework allows each agent to maintain control over its proprietary knowledge while still contributing to collaborative problem-solving. This protects **data privacy and organizational boundaries**, which is increasingly critical in large-scale, cross-institutional deployments. In contrast, centralized controllers often require access to global information, which raises concerns over data confidentiality and ownership\n    \n2. **Broader capability coverage via collective intelligence.**  \nBeyond privacy, decentralization also enables the system to fully exploit **heterogeneity at both the model and skill level**.   Each agent has its own knowledge boundaries and reasoning style;   forcing a single centralized controller to coordinate all roles inevitably biases task allocation and may underutilize agents’ specialized expertise. Decentralization, by contrast, allows agents to dynamically self‑organize and leverage complementary strengths through local decision‑making.\n\n3. **Scalability, adaptability, and fault tolerance.**  \n   Central controllers create bottlenecks (scalability), single points of failure, and rigidity in role assignment. In contrast, decentralized routing allows dynamic role reassignment and graceful handling of agent failures or overloads, improving both adaptability and robustness.\n\n4. **Experiments on Heterogeneous Agents (Section 4.2)**  \n   To specifically study the effect of diversity, Section 4.2 evaluates BBH accuracy under four settings—**Fully Homogeneous**, **Skill Heterogeneity**, **LLM Heterogeneity**, and **Both Heterogeneity** (Table 2).  \n   - **3 agents:** Fully Homogeneous achieves **0.86**, while Skill/LLM/Both Heterogeneity reach **0.84 / 0.81 / 0.81**, suggesting uniform reasoning can be more effective in very small teams.  \n   - **5 agents:** Heterogeneous configurations outperform the homogeneous one—Homogeneous **0.79** vs. Skill/LLM/Both Heterogeneity **0.86 / 0.85 / 0.85**—indicating that diversity enhances collaboration and complementary reasoning at larger scales.  \n   These results support decentralization’s key advantage: it naturally accommodates heterogeneous agents and lets them contribute their strengths without forcing global information sharing, leading to better performance as team size grows.\n\nDecentralization addresses privacy and governance constraints while enabling dynamic adaptation and capability diversity. **Multi‑agent system research remains a rapidly evolving field, and the relative merits of centralized vs. decentralized coordination are not yet fully settled**. We will continue to explore these questions in future work. We sincerely thank the reviewer again for the thoughtful comments.  \n\n\n---\n    \n>**Q3. Difference from GPTSwarm**\n    \n**A3**\nAgentNet’s innovation in decentralized multi-agent systems lies in its full delegation of routing decisions to individual agents. Each agent independently determines the next step based on its own knowledge, experience, tools, and the current environment, thereby eliminating reliance on a central coordinator and achieving true decentralization. With respect, in GPTSwarm, routing decisions for each agent are made at the system level, and agents in its network do not execute sequentially;  instead, in the paper of GPTSwarm, they complete tasks individually and then engage in parallel discussions, which is fundamentally different from AgentNet's sequential execution.\n\nMoreover, GPTSwarm—as well as many existing multi-agent frameworks—still depends on a global perspective to define task paths: message-passing directions are either fixed a priori or pre-planned by a centralized planner before execution.  AgentNet, by contrast, does not rely on any global workflow or planner; each agent dynamically determines the message-passing path in real time during task execution. This design allows AgentNet to flexibly adapt to evolving environments without requiring a static or planner-driven topology.\n\n    \n\n---\n\n    \n> **Question 1. To what extent does the performance gain stem from RAG rather than decentralization or topology learning?**\n\nAs shown in **Table 1**, the Synapse baseline represents a single-agent system that adopts both a ReAct-based architecture and Retrieval-Augmented Generation (RAG), but without decentralization or dynamic topology learning. Compared with other single-agent baselines without RAG, Synapse performs noticeably better, highlighting the substantial contribution of RAG to task execution performance.\n\nHowever, AgentNet also adopts a ReAct-style agent architecture and RAG, in addition to decentralization and dynamic topology learning. The further performance gains observed in AgentNet over Synapse indicate that RAG alone cannot fully account for the improvements. Instead, these results suggest that decentralization and learned topological routing provide complementary benefits by enabling more scalable coordination and more adaptive task delegation.\n\n---\n\n> **Question 2. Given that routing decisions rely on LLM decoding (prompt-based), how stable is the induced topology across repeated runs of the same task?** \nDue to the high cost of repeated API calls to large language models, we were unable to conduct large-scale repeated trials to systematically assess topology stability. However, based on our practical observations, we can share the following findings:\n\nAgentNet's routing decisions are primarily influenced by two factors: (1) the agent's current estimated capabilities, and (2) routing history, including past successes or failures with specific neighbors. We found that when the same task is repeated, the system tends to reuse successful routes or avoid previously unsuccessful ones, reflecting a form of empirical consistency in routing behavior.\n\nIn ambiguous situations (e.g., when multiple neighboring agents have identical capabilities, success rates, and edge weights), the LLM sometimes chooses the agent with the smallest ID. This is typically explained by the LLM as a simple preference rather than stochasticity. We observed this behavior did not negatively impact task performance.\n\nImportantly, all experiments were run with temperature set to 0, ensuring deterministic LLM decoding. This minimizes variability due to sampling randomness and further stabilizes the induced topology. Together, these observations suggest that routing decisions in AgentNet, though prompt-based, exhibit a high degree of stability under repeated executions.\n\n---\n\nIn summary, we believe the above findings sufficiently address the reviewer’s questions."}, {"review_id": "ebrr4mdB7D", "reviewer": "Reviewer_RQrj", "summary": "This 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.", "questions": "Please see the above weaknesses.", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths：\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.", "was_revised": true, "final_justification": "The proposed method uses a decentralized graph-topology multi-agent system for collaborative task execution and evolution. The authors address the issues regarding the clarity and experimental results of paper.", "rebuttal": ">**Q1.Clarity of Paper**\n\n**A1**\nWe thank the reviewer for pointing out areas where the writing of our paper could be improved, which we acknowledge was partly due to the strict page limit.  We agree that certain aspects of Figure 1 require clarification and refinement.  Below, we address the reviewer’s concerns:\n\n**“Hierarchical” vs. “Decentralized.”**\nIn traditional centralized multi-agent systems, a hierarchical structure is common: a central coordinator agent sits at the top level and is responsible for planning the tasks to be executed by the system.  This central agent also makes routing decisions, i.e., selects and instructs subordinate agents to perform specific subtasks.\nBy contrast, in AgentNet, each agent autonomously makes routing decisions during task execution based on its own knowledge, experience, tools, and information from its neighbors.  There is no central coordinator involved in either task planning or routing decisions.  All agents participate as equals, forming a fully decentralized multi-agent system.\n\n**“Static” vs. “Adaptive.”**\nIn traditional centralized systems, role assignments for participating agents (e.g., coordinator, programmer, summarizer) are typically static—roles are fixed and remain unchanged throughout the task execution process.\nIn AgentNet, roles are adaptive and flexible.  During long-term, dynamic task execution in real-world settings, each agent continuously updates its experience memory and capability vectors.  These updates enable the agent to evolve its capabilities and adapt the responsibilities it assumes in different tasks, rather than being bound by pre-defined roles.\n\nThe reviewer’s  comments regarding the clarity of Figure 1 is valuable. In the revised version, we will adjust the keywords and their placement in Figure 1 to make the contrasts clearer and avoid confusion.\n    \n    \n\n---\n\n>**Q2.Clarity of Paper – Variable Computation**\n\n**A2**\n\n\nThank you so much for raising this concern. We acknowledge that certain parts of the paper could be improved in terms of writing clarity. We would like to clarify the following:\n\n1. **Success rate in Formula 2.**  \n   The success rate S refers to the success rate of communication edges between two agents in the task execution chain after the current task has been completed. Specifically, when a task is finished and its correctness is verified, we compute the success rate for the edge where one agent routes the task to the next agent. This is defined as:  \n  \n```\nS = {successful tasks of this type}/{total routed tasks of this type}.\n```\n2. **Historical context in Formula 5.**  \n   The historical context $c_{t_{m+1}}$ includes all agent observations, planning and reasoning contents, and execution results from all steps **up to the current step**. This context provides the reasoning function with a complete view of prior interactions to guide its output.  \n3. **Construction of the task triplet.**  \n   The task triplet contains $p_t$, which represents the priority level of different tasks in real-world applications. This allows AgentNet to support asynchronous and concurrent task execution. However, for a fair evaluation, we tested in the one by one method in all experiments, so this parameter was not used. We plan to explore asynchronous and concurrent execution in detail as part of future work.  \n\nWe sincerely appreciate the reviewer’s thoughtful comments, and in the revised version, we will provide more detailed explanations of these variables to improve the clarity of the paper.\n\n---\n\n>**Q3.Experimental  results**\n\n**A3**\n    \nWe acknowledge that our method does not outperform all existing approaches on **MATH** when using **Qwen-turbo** as the backbone LLM.  However, we would like to clarify the following:\n\n1.  **AgentNet achieves SOTA performance on other datasets and backbones.**\nWith the same backbone LLM (Qwen-turbo), AgentNet reaches state-of-the-art results on the other two datasets (**BBH** and **API-Bank**).  Moreover, on the MATH dataset, AgentNet achieves or matches SOTA when using other backbones (**DeepSeek-V3** and **GPT-4o-mini**).  This demonstrates AgentNet’s strong performance in handling mathematical problems, logical reasoning, and complex tasks, as well as its excellent synergy with Qwen-turbo. We quote the original experimental results (AgentNet vs. AFlow) below:\n\n```\n* DeepSeek-V3: 92.86% vs. 91.67% (MATH)\n* GPT-4o-mini: 85.00% vs. 85.00% (MATH)\n* Qwen-turbo: 92.00% vs. 57.00% (BBH), 32.00% vs. 22.00% (API-Bank)\n```\n\n2.  **Further analysis of AFlow vs. AgentNet.**\nWith respect, AFLOW’s MCTS-based workflow is naturally well-suited for mathematical problems that require strictly step-by-step reasoning, which explains its relative advantage on the MATH dataset with the Qwen-turbo backbone.  However, AFLOW performs significantly worse on datasets such as BBH, which represent more diverse real-world reasoning scenarios.\n\nIn addition, AFLOW need **very high computational and economic costs**, as it requires extensive LLM token consumption during workflow search and optimization.  This overhead leads to only a ~1% performance improvement.  In contrast, AgentNet achieves comparable or even superior performance using only the task execution tokens required by AFLOW, which strongly demonstrates AgentNet’s efficiency and effectiveness.\n\n    \n\n---\n>**Q4.Hyperparameter Discussion**\n\n**A4**\n\nA detailed explanation of the hyperparameters $\\alpha$, $\\beta$, and $\\theta_{w}$ is as follows:\n    \n\n- **$\\alpha$ in Formula 2:**  \n  $\\alpha$ is a hyperparameter that balances historical information and recent interactions when updating the weights of communication edges between two agents. In AgentNet, all agents are represented as nodes in a graph, and the edge weight between two agents reflects the closeness of their relationship. After a task is executed, AgentNet updates the edge weights on the task execution chain according to whether the task was successful. $\\alpha$ determines how much historical performance versus recent interaction influences this update.  \n  *In our experiments, we set $\\alpha$= 0.9*\n\n- **$\\beta$ in Formula 10:**  \n  $\\beta$ is a hyperparameter that controls the update rate of an agent’s capability vector. As described in Section 3.3, when an agent completes a task and updates its capability vector, $\\beta$ determines the degree of this update.  \n  *In our experiments, we set $\\beta$ = 0.9*\n\n- **$\\theta_{w}$ in Formula 3:**  \n  $\\theta_{w}$is the threshold that determines whether an edge in the AgentNet network remains connected. If the weight of the edge between Agent i and Agent j falls below this threshold, the edge is pruned and the two agents are no longer considered neighbors.  \n  *In our experiments, we set $\\theta_{w}$ = 0.3.*  \n\n\n---\n>**Q5 & Q6. Clarity of Paper**\n\n**A5 & A6**\nWe will make the necessary revisions in the updated version of the paper:\n- Add a reference to Figure 2 (System Architecture) in Section 3.2  \n- Add a reference to Figure 5 (Agent Specialization) in Section 4.2  \n- Add a reference to Table 2 (Heterogeneity Experiment) in Section 4.1  \n- Correct the formatting error on page 8\n\nWe thank the reviewer for carefully pointing out these issues regarding writing clarity."}, {"review_id": "yIt6LADTrD", "reviewer": "Reviewer_P2gV", "summary": "The 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.", "questions": "Please 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?", "limitations": "Yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "I will maintain my score after going through the response and other reviews. The proposed mechanism seems to lack a mechanism to prevent or handle the cyclic dependency, and the mechanism heavily relies on careful prompting.", "rebuttal": ">**Q1.Cyclic Dependencies**\n\n**A1** \nWe sincerely thank the reviewer for the thoughtful analysis of our work. We would like to further clarify the generality of AgentNet in the scenario you described:\n\nIn the case mentioned, **AgentNet will not fall into a cycle**. Specifically, Agent 1 would first analyze the entire task t and determine that subtask **A** can be completed by itself, while subtasks **B** and **C** require the involvement of other agents. Agent 1 will then isolate and complete subtask **A** and forward the query and result of subtask **A**, together with the original task t, to Agent 2 through the router.\n\nUpon receiving the original task t and the query and result of subtask **A**, **Agent 2** will leverage its own knowledge base to isolate and complete subtask **B**. It will then forward the combined information in the same manner.  \n\nFinally, when **Agent 3** completes subtask **C**, it will determine that the task has reached its termination condition and output the final result.  \n\nThis sequential execution and information passing design ensures that AgentNet can handle tasks with dependent subtasks without falling into cyclic dependencies.\n \nAdditionally, as described in **Section 3.4: Dynamic Task Allocation**, we have implemented a **directed acyclic graph (DAG)** structure for task passing. In this structure, once a task has been forwarded to an agent, it will not be forwarded back to any agent that has already processed that task or received the information. This ensures that the task propagation avoids any cycles, and that deadlock situations are prevented. In other words, the system guarantees that each task progresses in a one-way flow, which prevents the system from getting stuck in cyclic dependencies or infinite loops.\n\nWe hope this clarification addresses the concerns raised, and we appreciate the reviewer's valuable feedback.\n\n\n---\n\n    \n>**Q2.Dataset Construction**\n\n**A2**\nIn the experiments presented in this paper, to maintain consistency, the warm-up dataset and the validation dataset were obtained by randomly splitting the full dataset.  Therefore, the warm-up dataset contains a uniformly random distribution of task types, which allows it to help the system handle various types of tasks.\n\nImportantly, all methods that require warm-up are trained on the exact same warm-up dataset, ensuring a fair comparison. We also ensure that no task in the warm-up dataset appears in the test set, thereby preventing any data leakage or memorization effects.\n\nIn future work, we plan to explore constructing warm-up datasets specifically tailored for different task types and performing warm-up or re-warm-up on AgentNet accordingly.\n\nWe appreciate the reviewer for raising this point, and we will further analyze the design of the warm-up dataset in future work.\n\n\n\n---\n\n        \n>**Q3.Presentation of the Paper**\n\n**A3**\n    \nWe sincerely thank the reviewer for raising these valuable concerns about the clarity of the paper. We provide detailed clarifications as follows:\n\n1. **Relation between context and capability vector.**  \n   The context $c_f$ or $c_{t_{m+1}}$ and the capability vector $c_j^m$ are not directly related. We acknowledge that the low distinguishability of the symbols in the current version of the paper caused this confusion, and we will revise the notation in future versions to improve clarity.\n2. **Finding the initial agent.**  \n   This is an excellent question. In the process of selecting the initial agent, we reuse the **forward/split function of a special agent**. Specifically, we first randomly assign the task to one agent, and then the router of that agent determines the first agent to receive and process the task using the forward/split function.  \n   During our experiments, we identified a significant challenge: as the scale of AgentNet grows larger, finding an appropriate initial agent becomes increasingly difficult. We will address this challenge as part of future work.\n3. **Vague wording in the paper.**  \n   We acknowledge that the paper currently contains some unclear expressions (e.g., \"a sophisticated mechanism\" and \"carefully designed prompts\"). We will carefully review and revise such wording in future versions to provide more precise descriptions.\n\nWe appreciate the reviewer’s feedback and will make the necessary improvements in the revised version.\n\n\n\n---\n\n        \n>**Q4.Initialization of Capabilities/Abilities**\n\n**A4**\n\nWe provide the following clarifications:\n1. The initialization of capabilities/abilities is manually predefined.\n2. There is actually **no difference** between \"capability\" and \"ability\" in our paper;   these terms were used interchangeably to refer to the same concept.   We apologize for the confusion this may have caused and will unify the terminology in future versions.\n3. Please refer to **Table 2 (Heterogeneity Experiment)**.   The \"Skill Hetero.\" setting in the table represents an AgentNet composed of heterogeneous agents with different predefined capabilities at initialization.   Our findings show that the impact of capability heterogeneity is related to the network size:\n    - In small networks, uniform reasoning (homogeneous capabilities) tends to be more effective.\n    - In larger networks, agents specializing in different capabilities (heterogeneous) collaborate more effectively.\n\nWe will revise the text in the updated version to clearly state these points and avoid ambiguity.\n    \n\n\n---\n\n>**As shown in the Appendix E case studies, does it mean the task always starts with Agent 0?**\n\n**A5**\n\nWith respect, **not all initial agents are Agent 0**.  However, we did observe an interesting phenomenon: during the very early stages of the warm-up phase, or at certain points in the middle of the process, when an agent faces neighboring agents with **identical capabilities, success rates, and edge weights**, the agent’s LLM tends to forward the task to the agent with the smaller ID number.  In these cases, the reasoning provided by the LLM is often simply a preference for the agent with the smaller ID.\n\nTo prevent this type of ID-based bias, the AgentNet codebase is designed such that when the router encounters multiple neighboring agents with equal capabilities, it uses the **random** function to select the next agent. This better reflects the intended decentralized behavior of the system and ensures fairness in agent selection.\n\nThis mechanism has shown stable performance in practice and does not negatively affect overall task execution.\n\n---\n\n>**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\n**A6**\nIn the early stages of our research, we also observed issues in the initial version of AgentNet where multiple agents could produce inconsistent answers or even fall into cyclic responses. However, after carefully refining the prompts and adjusting the framework, the frequency of such hallucination phenomena has been significantly reduced.\n\nIn fact, AgentNet inherently includes a behavior pattern that helps mitigate conflicting outputs in a multi-agent setting. Specifically, in the early stage of system evolution, we observed that when an agent (e.g., Agent A) generates an answer it deems potentially complete, but lacks sufficient experience or capability confidence, it tends to forward both the intermediate result and the original task to a neighboring agent for validation. This built-in verification behavior serves as a natural check-and-balance mechanism across agents.\n\nAs the system continues to evolve and each agent accumulates its own specialized skills and successful execution history, the likelihood of hallucination or “agents misleading one another” is naturally reduced. Agents develop confidence in specific task types where they have historically succeeded, and therefore are less likely to defer to peers unnecessarily, leading to more stable and accurate outputs overall.\n\nWe plan to open-source the full code and prompts, which will allow the community to further explore, analyze, and reproduce these behaviors."}]

Author rebuttal:

Meta-review: ### (a) Summary of Claims
This paper introduces AgentNet, a novel decentralized framework for LLM-based multi-agent systems (MAS). It aims to overcome the limitations of centralized architectures, such as scalability bottlenecks and single points of failure, by enabling agents to coordinate autonomously through a dynamic Directed Acyclic Graph (DAG). The framework integrates a Retrieval-Augmented Generation (RAG) based memory system to allow agents to continuously learn and specialize over time. The authors claim that this decentralized, evolutionary approach improves task accuracy, adaptability, and scalability, particularly in dynamic environments, outperforming single-agent and centralized MAS baselines.

### (b) Strengths
* Novel and well-motivated architecture design.
* Adaptive learning and structure.
* Comprehensive evaluation.

### (c) Weaknesses
* Scalability: The current manuscript lacks large-scale empirical experiments to fully validate its claims.
* Limited evaluation: Only a limited number of tasks were evaluated.

### (d) Reasons for acceptance
This paper presents a robust and practical implementation of a decentralized autonomous multi-agent system, backed by a comprehensive evaluation. While the core idea is not novel, the AC recommends acceptance due to the importance of highlighting decentralized autonomous multi-agent systems within the multi-agent community.
Decision: Accept (poster)