Scaling Large Language Model-based Multi-Agent Collaboration
Authors: Chen Qian
Zihao Xie
YiFei Wang
Wei Liu
Kunlun Zhu
Hanchen Xia
Yufan Dang
Zhuoyun Du
Weize Chen
Cheng Yang
Zhiyuan Liu
Maosong Sun
Venue: ICLR 2025 Poster (2025)
Keywords: Large Language Model
Autonomous Agent
Multi-Agent Collaboration
Interactive Reasoning
Primary area: foundation or frontier models, including LLMs
TL;DR: We examine the impact of scaling LLM agents in multi-agent task solving, extending traditional scaling from training (neuron collaboration) to inference (agent collaboration) & circumventing resource-intensive retraining via inference-time thinking.

Abstract: Recent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through collective reasoning. Inspired by the neural scaling law—increasing neurons enhances performance, this study explores whether the continuous addition of collaborative agents can yield similar benefits. Technically, we utilize directed acyclic graphs to organize agents into a multi-agent collaboration network (MacNet), upon which their interactive reasoning is topologically orchestrated for autonomous task solving. Extensive evaluations reveal that it effectively supports collaboration among over a thousand agents, with irregular topologies outperforming regular ones. We also identify a collaborative scaling law—the overall performance follows a logistic growth pattern as agents scale, with collaborative emergence occurring earlier than traditional neural emergence. We speculate this may be because scaling agents catalyzes their multidimensional considerations during interactive reflection and refinement, thereby producing more comprehensive artifacts. The code is available at https://github.com/OpenBMB/ChatDev/tree/macnet.

Published as a conference paper at ICLR 2025
SCALING LARGE LANGUAGE MODEL-BASED MULTI-AGENT COLLABORATION
Chen Qian*1, Zihao Xie*i YiFei Wang*, Wei Liu+, Kunlun Zhu+, Hanchen Xia*,
Yufan Dang* Zhuoyun Dut, Weize Chen Cheng Yang*. Zhiyuan Liu*, Maosong Sun
*Tsinghua University
Peng Cheng Laboratory
gianc62@gmail.com com
xie-zh22@mails, tsinghua.edee edu.cn cn sms@tsinghua, edu. cn
ABSTRACT
Recent breakthroughs in large language model-driven autonomous agents have revealed that multi-agent collaboration often surpasses each individual through col- lective reasoning. Inspired by the neural scaling law-increasing neurons enhances performance, this study explores whether the continuous addition of collaborative agents can yield similar benefits. Technically, we utilize directed acyclic graphs to organize agents into multi-agent collaboration network (MACNET), upon which their interactive reasoning is topologically orchestrated for autonomous task solv- ing. Extensive evaluations reveal that it effectively supports collaboration among over thousand agents, with irregular topologies outperforming regular ones. We also identify collaborative scaling law-the overall performance follows logis- tic growth pattern agents scale, with collaborative emergence occurring earlier than traditional neural emergence. We speculate this may be because scaling agents catalyzes their multidimensional considerations during interactive reflection and refinemet,, thereby producing more comprehensive artifacts. The code is available at hhtttss://g wigg.hhu..c.. ctteob..cccm/peeeeeeeeee httpc///pppeeeeaaaaaateeeeeeeeee macnet)
REX
ARTIFAET
MAENET
Figure 1: Multi-agent collaboration network (MACNET) uses directed acyclic graphs to arrange agents for collaborative interactions, facilitating autonomous task-solving through collective reasoning.
INTRODUCTION
In the rapidly advancing field of artificial intelligence, large language models (LLMs) have driven transformative shifts across numerous domains due to their remarkable linguistic capacity to seam- lessly integrate extensive world knowledge (Vaswani et al. 2017 Brown et al.) 2020). Central to this breakthrough the neural scaling law, where well-trained neural networks often exhibit power-law scaling relations primarily with the number of neurons, alongside factors such dataset size and training time (Kaplan et al., 20200 MMuenighhoff et al.) (024). Despite this, LLMs have inherent limitations in their enclosed reasoning, particularly when addressing complex situations that extend beyond textual boundaries (Schick et al., (023). To this end, during the inference phase, pioneering
t: Equal Contributions.
Corresponding Authors.

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studies transform foundational LLMs into versatile autonomous agents (Richards, 2023
Shen et al.
2023) by encapsulating external capabilities like context-aware memory (Park et al. 2023). tool use (Qin et al.] 2024a). and procedural planning (Zhao et al. 2023). In this context, multi-agent collaboration, within an interactive environment, prompts agents to engage in iterative reflection and refinement, explicitly facilitating process of "slow thinking" (Daniel 2017 OpenAl 2024). This
paradigm effectively unites the distinct expertise of diverse agents (Qian et al. 2024c ultimately
leading to artifacts derived from their dialogues.
Although numerous studies have confirmed that task-oriented multi-agent collaboration, facilitated by interactive behaviors, often surpasses standalone intelligence (Chen et al. 2024d a). the potential for continuously increasing agents remains largely overlooked with most research involving fewer than ten agents and only a limited number extending to several dozen (Li et al. 2023a Park et al.) 2023; Zhang et al. 2024a). Inspired by the neural scaling law, thought-provoking question arises: how does the continuous addition of collaborative agents impact performance? Exploring the collaborative scaling law is essential for linking performance trends with inference resources, revealing underlying phenomena agent networking, and promoting the development of scalable and predictable LLM systems. However, technically, effective collaboration should not depend on simple majority voting (Brown et al. 2024 Chen et al.] 2024b); instead, it should incorporate strategic mechanisms for scalable networking, cooperative interaction, and progressive decision- making (Hopfield) 1982] Almaatouq et al. 2222 Du et al. (024a).. Toward this end, as depicted in Figure I we organize multiple agents into a multi-agent, collaboration network (MACNET), upon which their interactive reasoning is topologically orchestrated for autonomous task solving.
For network construction, agents' topology is constructed as a directed acyclic graph, with each edge managed by supervisory critic issuing commands, and each node by a compliant actor providing tailored artifacts. This establishes a functional bipartition of labor among agents, promoting role specialization while inherently preventing backflow in information propagation.
For interactive reasoning, agents interact in a topological order, where each round involves two adjacent agents refining a previous artifact, and only the refined artifact, rather than the entire dialogue, is propagated to the next rounds. This prevents global broadcasting and suppresses context explosion, thereby enhancing collaboration scalability for much larger networks.
We performed extensive evaluations across different downstream scenarios, employing three types of representative topologies-the chain, tree, and graph-furthe further divided into six representative variants. The results show that MACNET surpasses all baselines on average and supports effective collaboration among over a thousand agents. Counterintuitively, collaborating within irregular topologies unexpect- edly outperforms that within regular ones. Notably, reveal collaborative scaling law, indicating that the overall performance exhibits a logistic growth pattern as the process of scaling agents, with collaborative emergence occurring earlier than previous instances of neural emergence. We speculate this may be because scaling agents catalyzes their multidimensional considerations during interactive reflection and refinement, thereby producing more comprehensive artifacts. Longer term, we aim for this research to extrapolate the traditional scaling from training to inference, circumventing the need for resource-intensive retraining through inference-time procedural thinking.
2 MULTI-AGENT COLLABORATION NETWORK
To create a scalable environment for effective collaboration, as depicted in Figure | we organize multiple agents into a multi-agent collaboration network (MAcNET), upon which their interactive reasoning is topologically orchestrated for autonomous task solving.
2.1 NETWORK CONSTRUCTION
Although training- time neuron collaboration has been well-established with Transformer architec- tures (Vaswani al. (017), the suitable architectures for inference- time agent collaboration remain unclear and lack consensus. Toward this end, we draw on the concept of graphs a data structure
Artifacts can vary from multiple-choice answers to repository-level code or coherent narrttivss, among many other possibilitics.
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that describes entities and their interrelations and extend from previous efforts to propose more general topology as a directed acyelic graph (DAG) (Nilsson et al.) 2020
(I) where V denotes the set of nodes indexed by the index set I, and & denotes the set of edges, with each edge directed from one node to another and no cycles exist. A graph will orchestrate agent interactions, akin to social networks where information propagates through directed edges. Intuitively, the acyclic nature prevents information backflow, eliminating the need for additional designs like task-specific cycle- breekin,,,,, thereby enhancing generalizability and adaptability across contexts.
g (V,E) V = {uili E 1 & - {(vs, By)li, [
CHAIN
TREE
ARTTFACT
INSTRUCTION
"Add GUr
Chain
Star
Tree
GRAPH
ACTOR
NODE
EDGE
NODE
Mesh
Layer
Random
Figure 3: Assign functionally bipartite agents on nodes and edges, respectively.
Figure 2: Representative topologies.
Given the impracticality of enumerating all possible topologies, we focus on three prevalent types-chain, tree, and graph-further divided into six representative sub-topologies, as depicted in Figure 2 Chain topologies, resembling the waterfall model (Petersen et al. 2209)), linearly structuring interactions along agents (Wei al., [022bb Hong et al. 2024). Tree topologies enable agents to branch out, interacting in independent directions (aao et al.) 2023) Zhuang et al. 2024); further categorized into "wider" star-shaped and "deeper" tree-shaped topologies. Graph topologies support arbitrary interaction dependencies, with nodes having multiple children and parents, forming either divergent or convergent interactions (Besta al,, [024a] Chen al,, 2024d. iZhuge et al.) 2024 Liu
al., 2202); further classified into fully-connected mesh topologies, MLP-shaped layered topologies, and irregular random topologics. These representative topologies are extensively studied complex Newman 2001 Ma et al. 2024) and procedural reasoning ZZang 2023 Besta et al. 2024b), ensuring comprehensive coverage of the most
network (Dodds et al. 2003
ctt al.]] 2024b] Yin et al.
widespread and practical topologies in multi-agent networking.
Since a functional bipartition-consitte compliant actors who provide tailored artifacts- can effectively establish division of labor, activate functional behaviors, and facilitate progressive task-solving (Li et al. (2023)), as depicted in Figure 3 we strategically assign a critic to each edge and an actor each node:
of supervisory critics who issue directional instructions and
0g p(u:), Vo, E V aij p((n,, "5),, V(r,,))) E
where p(z) represents the agentization operation on an element æ, achieved by equipping a foundation model with context-award memory, external tools, and professional roles; a, and ai denote an actor assigned to node v; and a critic assigned to edge Vij, respectively.
2.2 INTERACTIVE REASONING
In procedural task-solving, interactive reasoning among agents within static network requires strategical traversal to establish an orderly interaction criterion (Liu et al. 2024b Chen et al.) (2024)). In directed acyclic setting, our graph traversal strategy adheres the principles of topological ordering (Kahn 1962), which ensures that each node is visited only after all its dependencies have been traversed. Formally, for a network G, its topological order a linear arrangement of agents a; and aij such that for every directed edge (1,, vy)) E E, the ordering satisfies:
V(nc, 0/) E E, T(a;)) < <(aay)) < T(a;)
where I(x) denotes the index of agent in topological sequence. This arrangement ensures that each node-occupied agent a; precedes its corresponding edge-occupied agent aij. and aij precedes aj, thereby ensuring orderly information propogation along the network.
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Interaction
Topological Sequence
Information Propagation
Figure 4: Orchestrating the agents' reasoning process involves a series of dual -tal-ageet interactions. The topological order serves the control flow, while the original connectivity governs the data flow.
After establishing the global order, as illustrated in Figure we enable each pair of edge-connected adjacent agents to interact for artifact refinement, which results in a total assignment of [V] |g] agents and require at least 2 x [8| interaction rounds. Specifically, within each edge, the interactions between crities and actors follows a dual-agent multi-turn pattern:
T(a;; aij. az) = (T(a;, ait) a T(asj, aÿ)) Taa; aii) = (a; -ai aij ~ ai) T(aij. aj) = (aij + aj. aj ~ aij)
(4)
where r(.) represents the interaction between agents, + signifies an act of requesting, ~ indicates a corresponding wittin which the critic provides instruction and the actor offers artifact, and O denotes an iterative process. That is, ag requests feedback, aij offers reflected suggestions and requests further refinement, and aj provides refined artifact. Thus, the agents associated with single edge can engage in iterative reflection and refinement, effectively implementing an refinement
of previous artifact (Madaan al. 2023 Renze & Guven 2024
2.3 MEMORY CONTROL
Note that unrestrained information exchange among agents inevitably leads to context explosion (Luu ett al. 22224b] Xu et al. 2024), ultimately hindering scalability by limiting support for additional entities. To address this, adopt both short- and long-term memory to manage the context visibility for each agent (Sumers et al., 2023). Short-term memory captures the working memory within each interaction, ensuring context-aware decision-makinn (Li et al. 2023a). Long-term memory maintains context continuity by retaining only the final artifact derived from current dialogue, rather than the entire conversational history, ensuring that non-artifact contexts (e.g., the detailed analysis process preceding an artifact) remain inaccessible to subsequent agents (Qian et al. 2024c). This mechanism ensures that only the artifact propagates through the network, which explicitly minimizes context explosion risk while maintaining continuity. Artifacts propagate by branching at divergent nodes, or merging at convergent nodes requiring effective aggregation; technically, before refinement, convergent agents integrate the strengths of incoming artifacts through hierarchical aggregation (Du ct al. 2024b) to yield "non-linearly" strength- aggregated artifact.
Theoretically, in mesh structure characterized by the highest interaction density, the total token consumption for the sink agent who experiences maximum context pressure, with and without this mechanism, is derived tollows:
( = 1 + p (2m- 1) [ ()(((n 1)/2 2(n 2)) "x" On x n²
O(n) = + m(i + s)((n 1) + 2n - 2)) nx1 On
where C (2m 1) + s)/2 C 3m(i +
where n is the network scale (i.e., |)), & the task length, p the profile length, the average instruction length, the average artifact length, and the maximum interaction rounds between adjacent agents.
Note that although the interaction order unfolded as a sequence for visualization purposes only, certain sub-topologies (e.g., star) inherently support parallel processing.
Inaccessibilitty doesn't mean abandonment; when agents incorporate previous contexts into an artifact, these contexts are implicitly embedded and carried forward with the artifact.
"The "sink agent" refers to the agent assigned to the sink node. In multi-sink structure, final sink node is automatically appended to form structure with only one sink.
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This token complexity analysis implies that, without memory control, context length grows with n², causing squared increases in time and cost as the network scales. Conversely, our mechanism decouples context length from quadratic to linear growth, effectively suppressing context explosion and enabling better scalability for larger networks.
3 EVALUATION
Baselines We select diverse set of representative methods to facilitate a comprehensive multidi- mensional comparison:
CoT (Wei et al. (022b) is technically general and empirically powerful approach that endows LLMs with the ability to generate a coherent series of intermediate reasoning steps, naturally leading to the final artifact through process-aware thoughtful thinking.
AUTOGPT [Richards] 2023) is a versatile agent that employs multi-step planning and tool- augmented reasoning to decompose complex tasks into chained subtasks and leverages external tools within an eivironment-feedbace cycle to progressively develop effective artifacts.
2024) formalizes a swarm of autonomous agents as computational graphs, with nodes muatualy-customizzd functions and edges facilitating information flow, adaptively optimizing node prompts and modifying graph connectivity during collective reasoning.
GPTSWARM (Zhuge et al.
AgENTVeRsE (Chen et al. 2024d) dynamically assembles and coordinates a team of expert agents in chained or hierarchical structures, employing multi- aalti- linguistic interaction autonomously reflect and refine artifacts while displaying emergent social behaviors.
Datasets and Metries We adopt publicly available and logically challenging benchmarks to evaluate performance across heterogeneous downstream scenarios.
MMLU (Hendrycks et al. (021) provides comprehensive set of logical reasoning assessments across diverse subjects and difficulties, utilizing multiple-option questions measure general world knowledge and logical inference capabilities. We assess the quality of generated artifacts via accuracy, which reflects the correctness of responses to multiple-choice questions.
HumanEyal (Chen et al. 2021). a widely recognized benchmark for function-lewe code generation, designed for measuring basic programming skills. We assess via pass@k, which reflects function correctness across multiple standard test cases.
SRDD (Qian et al. 2024c) integrates complex textual software requirements from major real-world application platforms, tailored for repository-level software development, involving requirement comprehension, system design, code generation and testing. We assess using the official compre- hensive metric encompassing completeness, executability, and consistency.
CommonGen-Harr (Madaan et al.) 2023) tests the ability to generate coherent sentences with discrete concepts, assessing contextual understanding, commonsense reasoning, and creative writing skills. We assess using comprehensive metric that integrates crucial factors including grammar, fluency, context relevance, and logic consistency (Li et al. 2018).
Implementation Details We construct non-deterministic topologies such trees and graphs utilizing fundamental structures, including binary trees, layered structures balanced both width and depth, and random structures crafted by removing edges from mesh while maintaining connectivity. By default, we employ a topology consisting of approximately four nodes, aligning with multi-agent baselines. 5PT-3.5 is employed for interactive reasoning due its optimal balance of efficacy and efficiency, with each iterative interaction limited to three exchange rounds.
3.1 DOES OUR METHOD LEAD TO IMPROVED PERFORMANCE?
We employ the simplest topology-chain- as the default setting for comparative analysis. As demonstrated in Table I the chain-structuree method consistently surpasses all baselines across most metrics, showing a significant margin of improvement. The primary advantage of MACNET-CHAII over single agent who provides artifacts directly, lies in its facilitation of procedural thinking in which artifacts are continually reflected and refined. This process effectively mitigates previous inaccuracies unexpected hallucinations, aligning with previous findings (Cohen al. 2023 Du
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Method
Paradigm
MMLU
HumanEval
SRDD
CommonGen
Quality
COT
0.3544t
0.6098
0.7222
0.6165t
0.5757t
AUTOGPT
0.44851
0.4809
0.73531
0.5972
0.5655t
GPTSWARM
0.2368t
0.49697
0.7096
0.62221
0.51631
VGENTVERSE
0.29771
0.7256
0.75871
0.5399t
0.5805
MACNET-CHAIN
0.6632
0,3720
0,8056
0,5903
0.6078
MACNET-STAR
0.4456
0.5549
0.76791
0.7382
0.6267
MACNET-TREE
0.3421
0.4878
0.8044
0.7718
0.6015
MACNET-MESE
0.6825
0,5122
0.7792
0.35251
0.63161
MACNET-LAYER
0.2780
0.4939
0.7623
0.71761
0.56291
MACNET-RANDON
0.6877
0.5244
0.8054
0.5912
0.65221
Table 1: The overall performance of LLM-driven methods across various datasets, including both single-agent (e) and multi-agent @ @
paradigms. Quality represents the average performance over all tasks. For each dataset, the highest scores are highlighted in bold, while the second-highest scores are underlined. A dagger (t) denotes statistically significant differences (p < 0.05) between method our and
setting.
et al. 2024a Qian al.) 2024b). Moreover, observe that CoT exhibits strong performance certain datasets, which is largely because the underlying knowledge of widely-researched benchmarks already embedded in foundational models, giving single agents notable capability in these relatively "simple" tasks. While GPTSWARM self-organizes agents through dynamic optimization of nodes and edges, necessitates extensive task-specific customization for all nodes and edges, complicating usage and thus hindering seamless generalization to heterogeneous downstream tasks. Given the growing need for highly performant and automatic real-world systems, it is impractical to expect that all preparatory knowledge can be fully pre-encoded in foundation models, nor can specific adaptations be pre-made for all unforeseen complex tasks. Fortunately, MACNET bridges this gap by automatically generating various networks through simple hyperparameters (e.g., topology type and scale), enabling agents to engage in cooperative interactions without needing specific adjustments which represents promising pathway to achieving both autonomy and generalizability. Furthermore, we simulate a regression to graph-of-thougle reasoning (Besta et al. 2024a) with a simplified agent by ablating agents' profiles, which led average performance drop of 3.67 % across all topologies. This result underscores the effectiveness of collective intelligence over singular-asper reasoning, as the latter represents variant of dimensionality reduction within multi-agent environments, inevitably blocking its potential to extrapolate potential opportunities.
3.2 How Do DIFFERENT TOPOLOGIES PERFORM AGAINST EACH OTHER?
To gain deeper understanding of the impact organizational structures within multi-agent collabo- ration, we examine MACNET's topologies across six representative topologies. The analysis focuses on three key perspectives: density, shape, and direction.
Density Perspective Table illustrates that different types of topologies vary significantly in effectiveness for specific tasks; no single topology consistently excels across all tasks. For instance, a chain topology is suitable for software development, while writing. This phenomenon may arise from the inherent suitability of software engineering to a linear process, which accomplished through sequential steps such analysis, coding, review, and testing; in contrast, tasks requiring high creativity necessitate more divergent structures to foster agent interactions from various aspects. Additionally, higher interaction density, associated with edge density (see Figure 5). correlates with improved average performance across the three primary topological types. Specifically, the densely connected mesh topology outperforms the moderately dense tree topology, which in turn outperforms the sparsely connected chain topology. This can be
topology is ideal for creative
'Experiments with open-source models demonstrate similar pattern.

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0.50
|V|=50
0.63
0.6267
Divergent
0.30
0,26
0.62
Convergent
Density
0.10
0.02
0.02
0.02
Quality
0.61
0.6015
0.50
0.50
0.60
[V|=10
0.5902
0.30
0.59
0.20
0.20
0.20
0,5821
0.58
0.10
0.57
Chain
Star
Tree
Mesh
Layer
Random
Star
Tree
Figure scales.
Density of different topologies different
Figure 6: Comparison between topologies and their reversed counterparts.
attributed to the fact that increased density natually prolongs the reasoning process among collective agents, potentially enhancing opportunities for optimizing artifacts from various aspects.
Shape Perspective Despite the intuitive appeal of densest interactions (i.e. mesh), they do not always yield optimal performance. In contrast, irregular topologies often demonstrate statistically significant advantages. We hypothesize that this phenomenon is because overly dense interactions can overwhelm agents with information overload, impeding effective reflection and refinement. Conversely, network randomization frequently induces small-world properties (Watts & Strogatz 1998), characterized by shorter average path lengsl@ or higher clustering coefficiemt) These random edge connections, akin to residual connections, can link "unacquainted" agents via direct shortcuts, transforming them into "acquaintances" and implicitly reducing the average path length, which naturally decreases the likelihood of long-distance artifact invisibility. This phenomenon, seemingly counterintuitive when compared to well-established regular organizational structures the real world, suggests that collaboration patterns in an agent's 's world need not precisely mirror those in human society. Additionally, random topologies consume approximately 51.92% less time than mesh topologies, striking an optimal balance between reduced density and enhanced efficiency, thus serving as more practical choice. It has also been noticed that, with the same density, star-shaped topologies that are "wider" tend to perform better than "deeper" tree-shaped ones. This is primarily due to the memory control mechanism; while efficiently manages the spread of overly lengthy contexts across the network, it may cause deeper topologies lose track of distant agents, occasionally resulting in artifact version rollbacks (Qian et al. 2024a). This points to an empirical search strategy that manages network scale and clustering coefficients, whether through automated searching or manual design, to find an optimal balance between effectiveness and efficiency. Delving deeper, an in-depth inductive bias analysis reveals that in dlomed-domain scenarios (e.g., logical choices), a chain structure significantly aids in facilitating step-by-step reasoning. Conversely, a proliferation of parallel branches (e.g., stars) can lead convoluted brainstorming, which may not always be advantageous. In open-domain scenarios, topologies characterized by more convergent nodes are shown to revise artifacts more frequently and produce longer artifacts [ This occurs because more convergent nodes, with increased input diversity, increase the likelihood of refining artifacts, benefiting length- ength-sensitive metrics as longer artifacts are more likely to meet rich requirements. Ultimately, no task confined to particular topology; the optimal configuration should be chosen based on the openness of scenarios, available computing resources, and associated reasoning costs.
Direction Perspective
Beyond density and shape perspectives, the inherent asymmetry in certain mpologiess-hhee reversing the edges results in topologically distinct configuration--ae has interested us in exploring the effects of reversed topologies. As shown in Figure 6) merely reversing the directions of specific topologies can lead to significant performance degradation. Typically, divergent topologies, characterized by having more child nodes than parent nodes, substantially outperform their convergent counterparts. Intuitively, artifact propagation diverges smoothly, enabling each agent
'Aeraage path length (Albert & Barabasi 2002 is the average number of steps along the shortest paths for all possible pairs of network nodes, which is measure of the efficiency of information transport on network.
Thhe clustering coefficient measures the connectivity density among a node's 's neighhors (Strogatz) 2001). 'The layer topologies exhibit 92. 16% modification probability and average artifact length of 586.57, compared to 68.48% and 308.26 for chain topologies
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to discuss artifacts from varied aspects. In contrast, aggregating multiple artifacts convergent node more challenging, highlighting the complexity of integrating diverse aspects into a cohesive artifact. Therefore, to minimize potential degradation during artifact aggregation, it is recommended to employ topologies that maximize divergence while minimizing convergence.
3.3
COULD A COLLABOR SCALING LAW BE OBSERVED?
Chain
80
70-
70
70
60
60
60
50
Scale
50-
Scale
50
Scale
20
21
22
23
24
25
26
20
21
22
29
24
25
26
20
21
22
23
24
25
26
80
Mesh
80
Layer
80
Random
60
60
60
20
22
23
24
25
28
20
22
23
25
2
22
23
25
28
Figure Scaling performance of multi multi-agent collaboration under different topologies. Quality represents the average performance over all tasks.
Trend Perspective Recall the neural scaling law, which posits that increasing neurons leads to an continual performance improvement (Kaplan et al. 2020). To investigate the collaborative scaling law, which excavates the relationship between agent scale and performance, we initiated an attempt by exponentially increasing the number of nodes ((V)) from 2 (regressing to single-agent variant) to 26 (equating to over thousand agents in a mesh network). As depicted in Figure scaling our networks initially grows slowly in the quality of artifacts generated by various multi-agent systems, then leads to rapid improvement before reaching a saturation point. This pattern resembles sigmoid-varte variiat function:
f((V))
1 e- 3(log MV-a) +
(6)
where {a,, 3, 7, are real numbers specific to a particular topology. Roughly speaking, a node magnitude of 2 appears to be reasonable choice. However, considering the efficiency of sparse topologies and the superior performance of dense ones, we advocate balancing shape and scale through multidimensional trade-off's when applying this trend to various downstream applications. This finding suggests that many existing agent systems may be operating below their full potential, which underscores promising path for enhancing performance by increasing the number of agents, provided they collaborate effectively, rather than solely focusing on scaling foundational models ?
Besides, the validation of baseline scaling reveals that equalizing the number of LLM whether through majority voting in closed-domain tasks (Chen et al. 2024b) or best-of-N in open-domain tasks (Sessa et al. 2024 -consistently highlights lack of effective scalability across all baselines. Majority voting enhances performance by merely 0.9%, even when augmented with CoT or AUTO- GPT, plateauing at approximately eight agents. AGENTVERST implicitly reduces star topology and frequently encounters context explosion issues when scaling beyond thirty agents, thus hindering scalability. The and prompting, which restricts both multitasking capabilities and overall scalability.
setup of GPTS WARM necessitates manual, sase-fpe structuring
Timing Perspective The neural scaling law requires models with at least a billion parameters and over 20 training FLOPs to show emergent trends (Schaeffer et al. 2024 In contrast, collaborative emergence in MACNET manifests at much smaller scales, with most topologies reaching performance
'Looking further, this fitting only reflects general pattern from the perspective of network scales; future research should aim for more precise characterization by incorporating additional factors like profiles, tools and communication protocols, social routing.
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saturation with approximately hundred agents. The fundamental reason is that neuron coordina- tion (during training) relies on numeric matrix operations, requiring all neurons to precisely and simultaneously learn from scratch to assimilate extensive world knowledge. Conversely, individual agents (during inference) already possess certain knowledge from the foundational models, and their coordination through interdependent interactions utilizes existing reasoning skills disseminate knowledge from diverse aspects; the most critical aspects for artifact refinement in agents' interactions typically do not require such large scale be thoroughly reflected and refined. Thus, alongside neuron collaboration, agent collaboration may serve as "shortcut" to enhance intelligence levels, especially when large-scale retraining resources such as data and hardware are constrained.
3.4
WHAT FACTORS MIGHT CONTRIBUTE TO COLLABORATIVE EMERGENCE?
60
3000
50
Interacted Aspects
9500
Tokens of Generated Artriscts
993
2000
1500
1000
10
500
26 [V]
25
I Sprtec Emon
B Iogggiall Droee Purrine Demmen Emtrs
Secsncii and Aspacts
Not-eroorace Anpecte
The number and distribution of aspects in agent interactions, along with the length of artifacts. The pie chart features primary aspects in the inner circle and secondary aspects in the outer circle, with long-tail layout to visualize tail aspects. Zoom in for more detailed information.
Figure 8:
To delve deeper into the underlying mechanisms, we selected the moderately-dense layer typology employed in software development, which serves representative case, with similar phenomena consistently occurring in other topologies and scenarios. Specifically, we classified the aspects discussed in agents' interactions into five main categories (Oh & Ohl 2022 Kohn (019)): four levels of errors (syntax, runtime, logic, and unmet requirements) and a non-error category; each category contains multiple subcategories. Figure 8 displays the total number of interaction aspects, along with their detailed distribution. Within smaller topologies (2 I |V] < 2 the limited interaction density confines aspects to approximately a dozen secondary aspects. However, as the network expands (2+ < [V] S 29), the interaction density increases quadratically, resulting in a sudden increase dozens of aspects, followed by more gradual rise. This progression closely parallels the trend observed in emergent capabilities, which may partially attribute the emergence to the sharp rise in detailed interacted aspects among agents. This phenomenon occurs because the token distribution from underlying models typically follows a long-tail pattern, necessitating larger-scale sampling likely capture these tail tokens. Consequently, this encourages the emergence of more infrequent "tail aspects", allowing the collaborative process to extend beyond the most common aspects. Theoretically, the probability of a long-tail token appearing at least once n samples is:
p²(1) =1 1-)- p())" x (1 (1-11((0)) 1/r(1))
lim (1) = lim p p²(1) = |
(7)
m0000
where p(t) 1/r(t) represents standard Zipf's law characterizing a long-tailed distribution (New- man 2005): the sampling size n is proportional to the interaction density, i.e., x [D/ It can be inferred that increasing the network size significantly enhances the probability of tail token occur- rences, gradually approaching asymptote. This probability becomes an inevitable event once the sample size is sufficiently large. Statistically, when critic suggests particular aspect, there is a 93.10% statistical likelihood that actor will implement the recommended refinement rather than disregard it. The scaling up enables critics to pinpoint finer issues within artifacts, guiding actors to initiate corresponding refinements. Consequently, each round of dialogue in the collaborative process refines artifacts from different aspects, naturally elevating the probability of producing more nuanced artifacts (Liang et al. 2024 Du et al. 2022aa Cohen al. 2023).
In response to multidimensional considerations, scaling agents accordingly prolongs the overall length of artifacts. For instance, the token length increased by 7.51 times when scaling from 2 to

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24. This characteristic, over scall-scale networks, facilitates the integration of detailed requirements, performance optimization, and other advanced factors, potentially encompassing abilities that shorter artifacts cannot. This is mainly due to the graph's 's naturally divergent and convergent topologies, which enable artifacts to porpagate for strength-aggregatted refinement. Therefore, unlike majority voting, this paradigm fosters interdependent interaction and length-extended regeneration among diversified artifacts, thereby producing more comprchensive ones.
4 RELATED WORK
Large Language Models Trained on vast datasets through next token prediction Vaswani et al.
2017) and capable of manipulating billions of parameters (Muennighoff et al. 2024). LLMs have
become pivotal in natural language processing due to their seamless integration of extensive knowl-
edge (Brown ct al.) 2020 Bubeck et al. [2023] Radford et al.] 2019 Touvron et al. 2023; Wei et al.
2022a Shanahan et al. 2023; Chen et al. [202]] | Brants et al. 2007 Chen et al.) 2021 Ouyang et al.
2022 Yang et al. 2024 Qin et al. 2024b). Central to this breakthrough is the neural scaling law,
which posits that loss descends as a power law with model size, dataset size, and the amount of compute used for training (Kaplan et al. 2020; /mmth et al.]
2022] Ruan et al. 2024). The principle underscores that scaling up language models can lead to emergent abilities- -whee performance experiences a sudden leap as the model scales (Wei et al. 2022a Schaeffer et al. 2024).
Autonomous Agents
Despite these advancements, LLMs possess inherent limitations in enclosed reasoning, driving further research to integrate advanced capabilities such as context-awar mem-
ory (Park al., [2023] Hua et al.] 2023). ) tool use (Schick al., 2023] Qin et al., 2024a), procedural
planning (Wang et al.
2023a Zelikman et al. 2024). and role playing (Chan et al.) 2024 Wang
jtt al. [024c]] Liu et al.
2024a). thereby transforming fundamental LLMs into versatile autonomous
agents (Richards) 2023 [Shinn et al. 2024; Zhao et al.) 2024 Lin al. 2023 Mei et al. [024]
Chu et al. 2024). Along this line, multi-agent collaboration has proven beneficial in uniting the
expertise of diverse agents for autonomous task-solving (Khan et al. 2024 Liang et al. 2024| Qian
cet al. [024c] Wang et al.) 2024b a Zhou et al. 2024. Talebirad & Nadiri 2023) Chen et al.] [202cc]
Li et al. 2023b). which has widely propelled progress across various domains such as software
development (Hong et al. 2024 Qian et al. 2024a), game playing (Vinyals et al.
2019),, personalized
recommendation (Wang et al. 2023b /Zhng et al. 2023), medical treatment [Tang al. 2023) Li
ct al. 2024a). financial marketing (Gao et al. [2024]] Li et al. 2024c), educational teaching (Zhang
al. 2024c Yu al. 2024), scientific research (Zng al. 2024| Back et al.
2024 Ghafarollahi
& Buehler 2024) and embodied control (Guo et al. 2024,) Chen et [004f Mandi et al. 2023).
Technically, contrast to straightforward majority voting where individuals act independently (Chen
et al. 2024b), collective emergence (Woolley et al.) 2010 [Hoffild
1982. Watts & Strogatz 1998)
posits that effective collaboration should evolve into an integrated system that promotes interdepen-
dent interactions and thoughtful decision-making (Li et al. 2024b Piatti et al. 2024). As such, recent studies differentiate agents into distinct expertise and encourage task -riented interactions, forming a chained workflow to sequentially reach final artifacts (Qian et al. (024c). Subsequent research seeks to organize expert agents in a tree structure for hierarchical information propagation (Chen et al. 2024d) or in a graph with predefined node and edge functions (Zhuge et al. 20224)
5 CONCLUSION
This study explores the impact of scaling multi-agent collaboration by introducing MACNET, a scalable framework that utilizes graphs organize agents and orchestrate their reasoning for au- tonomous task solving. Extensive evaluations reveal that it effectively supports collaboration among over a thousand agents, with irregular topologies outperforming regular ones. We also identify a collaborative scaling law-the overall performance follows a logistic growth pattern as agents scale, with collaborative emergence occurring earlier than previously observed neural emergence. We speculate this may be because scaling agents catalyzes their multidimensional considerations during interactive reflection and refinement, thereby producing more comprehensive artifacts. However, research also indicates that there are limits the scaling horizon. By extrapolating traditional scaling from training to inference, we posit that agent collaboration could serve as a "shortcut" to bypass the need for isstrcce-nttenivve retraining by employing inference-time procedural thinking.
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ACKNOWLEDGEMENTS
The work was supported by the Tencent Rhino-Bird Focused Research Program and the Postdoctoral Fellowship Program of CPSF under Grant Number G2B20230348.
REFERENCES
Reka Albert and Alszro- Barabasi. Statistical Mechanics of Complex Networks. In Reviews of Modern Physics, 2002. URL
Abdullah Almaatouq, Mohammed Alsobay, Ming Yin, and Duncan J. Watts. Task Complexity Moderates Group Synergy. In National Academy Of Sciences (PNAS), 2021. URL https
www.ppnas.org/dol///
10.10..117/pa..... 2101062118
Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, and Sung Ju Hwang. ResearchAgent; Iterative Research Idea Generation over Scientific Literature with Large Language Models. arXiv preprint arXiv:2404.07758, 2024. URL WRLpssts:://arx orrpo//// http:/df//44 24044 07738
Maciej Besta, Nils Blach, Ales Kubicek, Robert Gerstenberger, Michal Podstawski, Lukas Gianinazzi, Joanna Gajda, Tomasz Lehmann, Hubert Niewiadomski, Piotr Nyezyk, et al. Graph of Thoughts: Solving Elaborate Problems with Large Language Models. In AAAI Conference on Artificial Intelligence (AAAI), 2024a. BtLhhtts:///rrtt httpttps///arrii.ogg//////////// wrg. 23008 09687
Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Guangyuan Piao, Nils Blach, Piotr Nyczyk, Marein Copik, Grzegorz Kwasniewski, Jürgen Müller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski. Aidan O' Mahony, Onur Mutlu, and Torsten Hoefler. Demystifying Chains, Trees, and Graphs of Thoughts. In arXiv preprini arXiv:2401. 14295, 2024b. URL
https://arxiv.orr/pdfff /2wpg//dff2241..... 14295
Thorsten Brants, Ashok C. Popat, Peng Xu, Franz Och, and Jeffrey Dean. Large Language Models in Machine Translation. In Empirical Methods in Natural Language Processing (EMNLP), 2007.
URL httpssss//aaaaathooogyyy. 00tp:///oooooooooo///00019999990
Bradley Brown, Jordan Juravsky, Ryan Ehrlich, Ronald Clark, Quoc V. Le, Christopher Ré, and Azalia Mirhoseini. Large Language Monkeys: Scaling Inference Compute with Repeated Sampling. In arXiv preprint 2rxiv:240..21755 2024. URL htt.httss://arxivorrr aa////bs/////// 21787
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, llya Sutskever, and Dario Amodei. Language Models are Few-Shot Learners. In Advances Neural Information Processing Systems (NeurIPS), 2020. htts:t http::::////cceeeeeeeeee heurips.cc/paper wttpcccpppp..a/.ee http:///1...100000000000001111e1 P20
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. Sparks of Artificial General Intelligence: Early Experiments with GPT-4. In arXiv preprint arXiv:2303.12712 2023. URL https::/doi
2303. 12712
Chi-Min Chan, Weize Chen, Yusheng Su, Jianxuan Yu, Wei Xue, Shanghang Zhang, Jie Fu, and Zhiyuan Liu. ChatEval: Towards Better LLM- Evaluators through Multi-agent Debate. In International Conference on Learning Representations (ICLR). 2024. URL lhttss://cccrr.. virtual/2024/poster///
Justin Chen, Swamaddee Saha, and Mohit Bansal. ReConcile: Round- Conference Improves Reasoning via Consensus among Diverse LLMs. In Annual Meeting of the Association for Computational Linguistics (ACL), 2024a. URL www.hhtts://eett httpp:://aclanthoolgg.... org/ 2024. acl-long
381/
11

---

Published as a conference paper at ICLR 2025
Lingjiao Chen, Jared Quiney Davis, Boris Hanin, Peter Bailis, lon Stoica, Matei Zaharia, and James Zou, Are More LLM Calls All You Need? Towards Scaling Laws of Compound Inference Systems. In arXiv preprint arXiv:2403.0244
2024b. URL http:://aa/itotorrrrr p2wp:///...r//////333..3/
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al. Evaluating Large Language Models Trained on Code. In arXiv preprint arXiv:2107.03374 2021. URL h
//arxiv.org/pdoe pttp//fff///0/// .03744.
Pei Chen, Shuai Zhang, and Boran Han. CoMM: Collaborative Multi-Agent, Multi-Reasoning-
Path Prompting for Complex Problem Solving. In North American Chapter of the Associa- tion for Computational Linguistics (NAACL), 2024c. URL
wtw.:/thhhoooooooo/222200000 2024
findinns--nacl.111 112/
Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Chen Qian, Chi-Min Chan, Yujia Qin, Yaxi Lu, Ruobing Xie, et al. AgentVerse: Facilitating Multi-agent Collaboration and Exploring Emergent Behaviors in Agents. In International Conference on Learning Representations (ICLR), 2024d. URL /htt:::tt//ccltl hwt.://rrruuu//////psr/r////////
Weize Chen, Ziming You, Ran Li, Yitong Guan, Chen Qian, Chenyang Zhao, Cheng Yang, Ruobing Xie, Zhiyuan Liu, and Maosong Sun. Internet of Agents: Weaving a Web of Heterogencous
Agents for Collaborative Intelligence. In arXiv preprint arXiv:2407.07061, 2024e. URL https
7/arxiv. wwwrii.org//doee / /20...2247... 07061.
Yongchao Chen, Jacob Arkin, Yang Zhang, Nicholas Roy, and Chuchu Fan. Scalable Multi-Robot Collaboration with Large Language Models: Centralized or Decentralized Systems? In arXiv
preprint arXiv:2300 15943, 2024f. https://////r wttp:///rr//p/////////////
Zhixuan Chu, Yan Wang. Feng Zhu, Lu Yu, Longfei Li, and Jinjie Gu. Professional Agents - Evolving Large Language Models into Autonomous Experts with Human-Level Competencies. In arXiv preprint 2ixx:22442200622 2024. https://axxiii
Roi Cohen, May Hamri, Mor Geva, and Amir Globerson. LM vs LM: Detecting Factual Errors via Cross Examination. In Empirical Methods Natural Language Processing (EMNLP), 2023. URL
https://aclanthhooogyy. org.:// smnnp-main. 778/
Kahneman Daniel. Thinking, Fast and Slow. In Farrar, Straus and Giroux,
2017. URL https:://ww.pptt http:///oooooootooooooooooo/o/n/ 0t1_2200227_002__0000000 00000000000000000000200000000000 0058
_027
0002_0054
2012_00222__2
Peter Sheridan Dodds, Duncan J. Watts, and Charles F. Sabel. Information Exchange and the Robustness of Organizational Networks. In National Academy Of Sciences (PNAS), 2003. URL
https://www pnas, /otp////////////. 107.//0077..p555 0073//nas.1533702119. 1534702100
Yilun Du Shuang Li, Antonio Torralba, Joshua B. Tenenbaum, and Igor Mordatch. Improving Factuality and Reasoning in Language Models through Multiagent Debate. In International Conference on Machine Learning (ICML), 2024a. URL httpps://penneeieew.nr Zj7YuTE4t8
?id=
Zhuoyun Du, Chen Qian, Wei Liu, Zihao Xie, Yifei Wang, Yufan Dang, Weize Chen, and Cheng Yang. Multi-Agent Software Development through Cross- Collaboration. In arXiv preprint arXiv:2406.088 08979, 2024b. URL /tappiv///xxxv.ooo wwtp:xi..org//pr.e pdff///ff//f/ /2066 08979
Shen Gao, Yuntao Wen, Minghang Zhu, Jianing Wei, Yuhan Cheng, Qunzi Zhang, and Shuo Shang. Simulating Financial Market via Large Language Model based Agents. In arXiv preprint arXiv:2406. 19966, 2024. Bttpshtps:///art wwwprxxwvv.oogpppe wttp///gggpp/p////ppp /240. 19966
Alireza Ghafarollahi and Markus J. Buehler. SciAgents: Automating Scientific Discovery through Multi-Agent Intelligent Graph Reasoning. In arXiv preprint arXiv:2409.05556, 2024. URL
https://arxiv. www.vvorg//poee wwt//df/////o/ppooooooooop... / 2409. 65556
Xudong Guo, Kaixuan Huang, Jiale Liu, Wenhui Fan, Natalia Vélez, Qingyun Wu, Huazheng Wang, Thomas L. Griffiths, and Mengdi Wang. Embodied LLM Agents Learn to Cooperate in Organized Teams. In arXiv preprint arXiv:2403.I 12482, 2024. httpp:://aaxi..ott// wwtp:xxvvvooggggooooo /ptp://fff/2434//. 12482
12

---

Published as a conference paper at ICLR 2025
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Xiaodong Song, and Jacob Steinhardt. Measuring Massive Multitask Language Understanding. In International Conference Learning Representations (ICLR), 2021. URL https://api. htttttss//aap..ssmtttccccchobbu semanticscoolaar org/CorppsID: 221516475.
Sirui Hong, Mingchen Zhuge, Jonathan Chen, Xiawu Zheng, Yuheng Cheng, Ceyao Zhang, Jinlin Wang, Zili Wang, Steven Ka Shing Yau, Zijuan Lin, Liyang Zhou, Chenyu Ran, Lingfeng Xiao, Chenglin Wu, and Jürgen Schmidhuber. MetaGPT: Meta Programming for A Multi-Agent Collab- orative Framework. In International Conference Learning Representations (ICLR), 2024. URL
https://iclr //t/://rrrrul22222/p/ss////////
J Hopfield. Neural Networks and Physical Systems with Emergent Collective Computational Abilities. In National Academy Of Sciences (PNAS), 1982. URL #ttps:///oi.org/111 1073/ pnas. 79.8 8. 2554
Wenyue Hua, Lizhou Fan, Lingyao Li, Kai Mei, Jianchao Ji, Yingqiang Ge, Libby Hemphill, and Yongfeng Zhang. War and Peace (WarAgent): Large Language Model-based Multi-Agent Simulation World Wars. In arXiv preprint arXiv:2311.17227, 2023. URL https://arxiv
org/pdf 223/p 17227
A. B. Kahn. Topological Sorting of Large Networks. In Communications of the ACM, 1962. URL https://dl. acm. wtt.://..occooooooo.oooooooo org/doi/10 100 1145/ 02155/66996... 369025
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. Scaling I Laws for Neural Language Models. In arXiv preprint arXiv:2001.0836) 2020. https::tt: org/10. 48550/ aar/s//aaXiv..m 2001 08361
Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh Radhakrishnan, Edward Grefenstette, Samuel R. Bowman, Tim Rocktäschel, and Ethan Perez. Debating with More Persuasive LLMs Leads to More Truthful Answers. In International Conference on Machine Learning (ICML), 2024. https://:// wwt//irtaa//22////sstr//////////
Tobias Kohn. The Error Behind The Message: Finding the Cause of Error Messages in Python. In ACM Technical Symposium on Computer Science Education (SIGCSE), 2019. URL https:
//doi orw/oogg//1 4141111114122222333888888 3287381
Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, and Bernard Ghanem. CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society. In Advances in Neural Information Processing Systems (NeurIPS), 2023a. URL /ttps://aaxvv
org/abs/2303. 17760
Junkai Li, Siyu Wang, Meng Zhang, Weitao Li, Yunghwei Lai, Xinhui Kang, Weizhi Ma, and Yang Liu. Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents. In arXiv preprint 02957
arXiv:2405.029. 02957. 2024a.
Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, and Deheng Ye. More Agents is All You Need. In http:/fff22440 2402. 05120
arXiv preprint arXiv:2402.05110, 2024b. URL
Nian Li, Chen Gao, Mingyu Li, Yong Li, and Qingmin Liao. EconAgent: Large Language Model- Empowered Agents for Simulating Macroeconomic Activities. In Annual Meeting of the Asso- ciation for Computational Linguistics (ACL), 2024c. URL htttps://aclantholllo org/ acl-long. 8297
Yuan Li, Yixuan Zhang, and Lichao Sun. MetaAgents: Simulating Interactions of Human Behaviors for LLM-based Task-oriented Coordination via Collaborative Generative Agents. In arXiv preprint 131:::2310005000, 2023b. URL /tLLtttss//arriv.orr////////2//
06500
Zhongyang Li, Xiao Ding, and Ting Liu. Generating Reasonable and Diversified Story Ending using Sequence to Sequence Model with Adversarial Training. In International Conference on Computational Linguistics (CaguiisiisCCCOL.. (COLING), 2018. URL [tt.//ttss///a...aaoollooooCCCCo
13

---

Published as a conference paper at ICLR 2025
Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Rui Wang. Yujiu Yang, Zhaopeng Tu, and Shuming Shi. Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate. In Empirical Methods in Natural Language Processing (EMNLP), 2024. URL https
//arxiv.org//df/22300
Bill Yuchen Lin, Yicheng Fu, Karina Yang, Faeze Brahman, Shiyu Huang, Chandra Bhagavatula, Prithviraj Ammanabrolu, Yejin Choi, and Xiang Ren. SwiftSage: A Generative Agent with Fast and Slow Thinking for Complex Interactive Tasks. In Advances in Neural Information Processing Systems (NeurIPS), 2023. htRllhttts///axii..oom org hwtpr/iwww.orgpdd///.// http///fff/32...///// 17390
Ruibo Liu, Ruixin Yang, Chenyan Jia, Ge Zhang, Diyi Yang, and Soroush Vosoughi. Training Socially Aligned Language Models on Simulated Social Interactions. In International Conference on Learning Representations (ICLR), 2024a. WRLLhhtts:///ar/ wdtp 2305. 16960
Wei Liu, Chenxi Wang, Yifei Wang, Zihao Xie, Rennai Qiu, Yufan Dang, Zhuoyun Du, Weize Chen, Cheng Yang, and Chen Qian. Autonomous Agents for Collaborative Task under Information Asymmetry. In Advances in Neural Information Processing Systems (NeurIPS), 2024b. URL
https://arxiv.. wwwpixxv..rgg/dpfe org/pdf 2406. 14928
Zijun Liu, Yanzhe Zhang, Peng Li, Yang Liu, and Diyi Yang. Dynamic LLM--gent Network:
An LLM-agent Collaboration Framework with Agent Team Optimization. In arXiv preprint arXiv:2310002170, 2023. URL httpt://aaxxi..... http://wwxvv.rrgg//// /2tp///df/211..... 22310002000
Chengdong Ma, Aming Li, Yali Du, Hao Dong, and Yaodong Yang. Efficient and Scalable Reinforce- ment Learning for sarge-scale Network Control. In Nature Machine Intelligence (NMI), 2024.
URL https:://601..rr//1... 1038/ 542256 020-6---9--0879--
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang. Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark. Relf-Refine: Iterative Refinement with Self-Feedback. In Advances in Neural Information Processing Systems (NeurIPS), 2023. URL httpttttt//toceeeiiiiiie neur cc/ ct/pppprppppppceceeeeeeeeeee..ee M hrt.://22bbbbbbbeeeeeeeffffrrrrn
pdff]
Zhao Mandi, Shreeya Jain, and Shuran Song. RoCo: Dialectic Multi-Robot Collaboration with Large Language Models. arXiv preprini urXiv:2307.04738, 2023. /Rt//ttss://rrxiworr
0307.04733
Kai Mei, Zelong Li, Shuyuan Xu, Ruosong Ye, Yingqiang Ge, and Yongfeng Zhang. AIOS: LLM Agent Operating System. In arXiv preprint 16X1v:2403.16671, 2024. URL httttttts//axv...oo pd43 16971
Niklas Muennighoff, Alexander Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, and Colin Raffel. Scaling Data- Constrained Language Models. In Advances in Neural Information Processing Systems (NeurIPS), 2024. UtL/https://prrreediing neurips https/ss..rppppp.pppeeeeeeeeeeee cttppppr.fllles/pppprreeeeeeeeee fillss/papeer/222//aash 66999444886eee222224444222222663
html
M. F. J. Newman. The Structure of Scientific Collaboration Networks. In National Academy Of Sciences (PNAS), 2001. URL /ttpp:://t// pnas. wwtp////////ooooo/ 100.p//aa..../........ 98.2.404
MEJ Newman. Power laws, Pareto distributions and Zipf's law. In Contemporary Physics, 2005.
URL https: //wps: tandfonline
wttpr/ooinnnnnnccoooo//oomaam/ cwm//oi/abs////////cc// 10. 1080/00 00010711050005220
Anton Nilsson, Carl Bonander, Ulf Strömberg, and Jonas Björk. A Directed Acyclic Graph for Interactions. In International Journal of Epidemiology, 2020. URL httpps://doi.orr///// 1093/
1093/
ije/dyaa211
Wonseok Oh and Hakjoo Oh. PyTER: Effective Program Repair for Python Type Errors. In ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE), 2022. BRLhhttp:: //d1, acm, wwwp//////////o/oo 10111114455551 300055055.05..... 35491389
OpenAl. Learning to Reason with LLMs. In httpp:/p//pppaa.ooommmtttttttte.
14

---

Published as a conference paper at ICLR 2025
Long Ouyang, Jeffrey Wu, Xu Jiang. Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. Training Language Models to Follow Instruc- tions with Human Feedback. In Advances in Neural Information Processing Systems (NeurIPS), 2022. URL httpp:://ppocceeeinggetee// neur cc/ wtppp/seeee......ppppae.e.e.reee https/s/cppppppeeeeee///ppee/e/e wtt.es//aaprr//222//11ee
pdf
Joon Sung Park. Joseph O' O'Brinn, Carrie Jun Cai, Meredith Ringel Morris, Percy Liang, and Michael Bernstein. Generative Agents: Interactive Simulacra of Human Behavior. In Annual ACM Symposium on User Interface Software and Technology (oannlooyy(USSSS (UIST), 2023. URL httthth////di.cooo
10. 11111115868883 36676666633366663333
Kai Petersen, Claes Wohlin, and Dejan Baca. The Waterfall Model in Large-Scale Development.
In Product-Focused Software Process Improvement, 2009. URL https://doi..or//ee..
778-3-642- 124-2-00152-7222
Giorgio Piatti, Zhijing Jin, Max Kleiman- Weiner, Bernhard Schölkopf, Mrinmaya Sachan, and Rada Mihalcea. Cooperate or Collapse: Emergence of Sustainability Behaviors in Society of LLM Agents. In arXiv preprint arXiv:2404. 16698, 2024. URL https:////a/ wrg. /24044 16698
Chen Qian, Yufan Dang, Jiahao Li, Wei Liu, Zihao Xie, Yifei Wang. Weize Chen, Cheng Yang, Xin Cong, Xiaoyin Che, Zhiyuan Liu, and Maosong Sun. Experiential Co-Learning of Software- Developing Agents. In Annual Meeting of the Association for Computational Linguistics (ACL), 2024a. URL
org/ 2024. acl- long..n....... long.305/
Chen Qian, Jiahao Li, Yufan Dang, Wei Liu, YiFei Wang, Zihao Xie, Weize Chen, Cheng Yang, Yingli Zhang, Zhiyuan Liu, and Maosong Sun. Iterative Experience Refinement of Software- Developing Agents. In arXiv preprint arXiv:2405.04219, 2024b.
2405. 04219
Chen Qian, Wei Liu, Hongzhang Liu, Nuo Chen, Yufan Dang, Jiahao Li, Cheng Yang, Weize Chen, Yusheng Su, Xin Cong. Juyuan Xu, Dahai Li, Zhiyuan Liu, and Maosong Sun. ChatDev: Communicative Agents for Software Development. In Annual Meeting of the Association for Computational Linguistics (ACL), 2024c. URL http:: /pas://aclaanns https://aacannhoooggy.ooo .rg.org/2200 2024 acl- 810/]
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al. ToolLLM: Facilitating Large Language Models to Master 16000+ Real- World APIs. International Conference on Learning Representations (ICLR), 2024a. URL
http///llr..cc//ttataa
CC/ wwt.e//vvtttt//t/eeaeaa httpruual//222/ppstrr///8//3
Zhen Qin, Rolf Jagerman, Kai Hui, Honglei Zhuang, Junru Wu, Jiaming Shen, Tianqi Liu, Jialu Liu, Donald Metzler, Xuanhui Wang. and Michael Bendersky. Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting. In North American Chapter of the Association for Computational Linguistics (NAACL), 2024b. 2024. findings-naacl. 977
org/
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language Models are Unsupervised Multitask Learners. In OpenAl Blog. 2019. URL hhtpps://ccn openai homm/bbetterr-laggggg-mmoee httpa////aanguuggmmddlslssss motaage_ee_ddls__re_eeeeee Stsuuprriised_mliittssss..o
Matthew Renze and Erhan Guven. Self-Reflection in LLM Agents: Effects on Problem-Solving Performance. In arXiv preprint arXiv:2005.06682 2024. URL htt.:tts://aarvv.rr////////5 06682
Toran Bruce Richards. AutoGPT. In http:///sshaaaccnosssstttttttott
URL
https://github.. www.ccccossiieeeeeee Siamm/iSigaa ftatiiiittt--Gaiittaaaaaaaa
Yangjun Ruan, Chris J. Maddison, and Tatsunori Hashimoto. Observational Scaling Laws and the
Predictability of Language Model Performance. arXiv preprint arXiv:240S. 2024. URL
https://arxiv. wwwpaaawww..ooogd....aaa wwgg/pdf/240/. 10938
15

---

Published as a conference paper at ICLR 2025
Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo. Are Emergent Abilities of Large Language Models Mirage? In Advances in Neural Information Processing Sys- tems (NeurIPS), 2024. URL https:: http:://apppsssspm neurips. papp//aperr____ patperrfiieesppprr/203333333333 adc98a266f 45005442386 Pttp::444460ba11aaeae6ea------rr Cowf-rCCnee.eccccccm pdf]
Timo Schick, Jane Dwivedi-YY Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. ToolFormer: Language Models Can Teach Themselves to Use Tools. In arXiv preprint urXiv:2302.04761, 2023. URL
04761
Pier Giuseppe Sessa, Robert Dadashi, Léonard Hussenot, Johan Ferret, Nino Vieillard, Alexandre Ramé, Bobak Shariari, Sarah Perrin, Abe Friesen, Geoffrey Cideron, Sertan Girgin, Piotr Stanczyk, Andrea Michi, Danila Sinopalnikov, Sabela Ramos, Amélie Héliou, Aliaksei Severyn, Matt Hoff- man, Nikola Momchev, and Olivier Bachem. BOND: Aligning LLMs with B Distillation. In arXiv preprint urXiv:2407. /4622, 2024. URL httptttsss///iiiii org wtwp://g//bbbaaaaeeoo 2407. 24222
Murray Shanahan, Kyle McDonell, and Laria Reynolds. Role Play with Large Language Models. In Nature, 2023. URL
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang. Hugging- GPT: Solving Al Tasks with ChatGPT and its Friends in Hugging Face. In Advances in Newral Infor-
mation Processing Systems (NewrlPSS) 2023. URL URL/htpss///rrcceddings neurips ips cc/paper
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao. Reflexion: Language Agents with Verbal Reinforcement Learning. In Advances in Neural Information
Processing Systems (NeurIPS), 2024. URL /ttp:ttts///rrsi..rrrg
11366
Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, Elton Zhang, Rewon Child, Reza Yazdani Aminabadi, Julie Bernauer, Xia Song, Mohammad Shoeybi, Yuxiong He, Michael Houston, Saurabh Tiwary, and Bryan Catanzaro. Using DeepSpeed and Megatron to Train Turing NLG 530B, A Large- Scale Generative Language Model. In arXiv preprint arXiv:2201.11990 2022. URL https: http://awxxvvv...pp./ee wrtppdt//////oootooootppppp../p/ /2001 11990
Steven H. Strogatz. Exploring Complex Networks. In Nature, 2001. URL http:://www..ateee comm/ipproceedingss///555555
Theodore R. Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L. Griffiths. Cogni-
tive Architectures for Language Agents. In arXiv preprint arXiv:2309.02427, 2023. URL
https://arxiv.orge
Yashar Talebirad and Amirhossein Nadiri. Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents. In arXiv preprint arXiv:2306.03314, 2023. URL htRtss:ts/////riv.orr abs/2306.050 03314
Xiangru Tang, Anni Zou, Zhuosheng Zhang, Ziming Li, Yilun Zhao, Xingyao Zhang, Arman Cohan, and Mark Gerstein. MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning. In arXiv preprint arXiv:2311.10533 10537, 2023. URL /tttps://arxivvorg///oooo 10537
Hugo Touvron, Thibaut Lavril, Gautier Izacard. Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and Efficient Foundation Language Models. In arXiv preprint arXiv:2302. 19971, 2023. URL https://arxiv. org. pdf /2302.2022222 13971
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, F. ukasz Kaiser, and Illia Polosukhin. Attention is All You Need. In Advances in Neural Information Processing Systems (NeurIPS), 2017. URL htthhttps://ppooeeeeee httttp:://proceedings. paper
nerr// htcc/paper_fiiee 2rr0://0070000000000000000c0aaa.
Oriol Vinyals, Igor Babuschkin, Wojciech M. Czarnecki, and al. Grandmaster Level in StarCraft II using Multi-agent Reinforcement Learning. In Nature, 2019. URL httthtts////ooorrr org/10 1038/
941586-019-17242
16

---

Published as a conference paper at ICLR 2025
Haotian Wang. Xiyuan Du, Weijiang Yu, Qianglong Chen, Kun Zhu, Zheng Chu, Lian Yan, and Yi Guan. Learning to Break: Bhowledge-Enhaneee Reasoning in Multi Agent Debate System. In arXiv preprint arXhv:2312.04854, 2024a. URL httptt///////x/oorrrg pdf 2312. 04854
Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim. Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thougg Reasoning by Large Language Models. In Anmual Meeting of the Association for Computational Linguistics (ACL), 2023a. URL https://aclanthooogg// org.//g/...o//2333 acl -long.o 147. pdf
Lei Wang, Jingsen Zhang, Xu Chen, Yankai Lin, Ruihua Song, Wayne Xin Zhao, and Ji- Ji-Rong Wen. RecAgent: A Novel Simulation Paradigm for Recommender Systems. In arXiv preprint arXiv:2306.02555,, 2023b. URL htttp:://arxiv.org//po// 22t1//2222660.552222
Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong, and Yangqiu Song.
Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key? In Anmual Meeting of the Association for Computational Linguistics (ACL), 2024b. URL https::/aalaaattoooogg... org/ 2024. aclglong
331/
Zhenhailong Wang. Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, and Heng Ji. Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi- Persona self-Collaboration. In North American Chapter of the Association for Computational Linguistics (NAACL). 2024c. URL uttpt/ts///alllattolooyyy org/ 2024. naacl-long 15/
Duncan J. Watts and Steven H. Strogatz. Collective Dynamics of Small-World Networks. In Nature, 1998. URL
8#ci teas
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. Emergent Abilities of Large Language Models. In Transactions on Machine Learning Research, 2022a. URL
http://axxxv..og/bbb/////// ab2/
07682
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoe V Le. and Denny Zhou. Chain-of-thouggh Prompting Elicits Reason- ing in Large Language Models. In Advances in Neural Information Processing Systems (NeurIPS), 2022b. URL https::/proceeddnngg neur www.:eewris..pppppeeeeeeeeeeeeee paper_ files/ pdff
Anita Williams Woolley, Christopher F Chabris, Alex Pentland, Nada Hashmi, and Thomas W Malone. Evidence for Collective Intelligence Factor in the Performance of Human Groups. In Science, 2010. URL http::ttt::::wccceeee hrtp:////..///////..///// 126/science. 1193147
org
Peng Xu, Wei Ping, Xianchao Wu, Lawrence McAfee, Chen Zhu, Zihan Liu, Sandeep Subramanian, Evelina Bakhturina, Mohammad Shocybi, and Bryan Catanzaro. Retrieval Meets Long Context Large Language Models. In International Conference on Learning Representations (ICLR), 2024.
URL htttss://arxiii htttp::/aaxxiv.orr//a////////// abt//100.//31..... 03025
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, and Xinyun Chen. Large Language Models as Optimizers. In International Conference on Learning Representations 03409
(ICLR), 2024. URL http://///aaiiv.oooaa///////////
Shunyu Yao, Dian Yu. Jeffrey Zhao, Izhak Shafran, Tom Grifliths, Yuan Cao, and Karthik Narasimhan. Tree of Thoughts: Deliberate Problem Solving with Large Lan- guage Models. In Advances in Neural Information Processing Systems (NeurIPS). 2023.
URL https::/pproceedingss neur www.wnww....pppprrrrrrr..pp wwt.e///aawe///223//////////////
pdf]
Zhangyue Yin, Qiushi Sun, Cheng Chang, Qipeng Guo, Junqi Dai, Xuanjing Huang, and Xipeng Qiu. Exchange-of-Thougeee Enhancing Large Language Model Capabilities through Cross-Model Communication. In Empirical Methods in Natural Language Processing (EMNLP), 2023. URL
httpss://cclaattooooyy.rr////// wp- /936/
17

---

Published as a conference paper at ICLR 2025
Jifan Yu, Zheyuan Zhang, Daniel Zhang-li, Shangqing Tu, Zhanxin Hao, Rui Miao Li, Haoxuan Li, Yuanchun Wang. Hanming Li, Linlu Gong. Jie Cao, Jiayin Lin, Jinchang Zhou, Fei Qin, Haohua Wang, Jianxiao Jiang, Lijun Deng, Yisi Zhan, Chaojun Xiao, Xusheng Dai, Xuan Yan, Nianyi Lin, Nan Zhang. Ruixin Ni, Yang Dang. Lei Hou, Yu Zhang, Xu Han, Manli Li, Juanzi Li, Zhiyuan Liu, Huiqin Liu, and Maosong Sun. From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents. In arXiv preprint (rXii:2409.03512 2024. URL https://arxiv wtg/ 2200/24499000 03512
Eric Zelikman, Georges Harik, Yijia Shao, Varuna Jayasiri, Nick Haber, and Noah Goodman. Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking. In arXiv preprint arXiv:2403.09629, 2024.
09629
Zheni Zeng, Bangchen Yin, Shipeng Wang, Jiarui Liu, Cheng Yang. Haishen Yao. Xingzhi Sun, Maosong Sun, Guotong Xie, and Zhiyuan Liu. ChatMol: Interactive Molecular Discovery with NaturalLLaguaaae Language. In Bioinformatics, 2024. URL https:/ps://do..
btae534
An Zhang, Leheng Sheng, Yuxin Chen, Hao Li, Yang Deng, Xiang Wang, and Tat-Seng Chua. On Generative Agents in Recommendation. In arXiv preprint arXiv:23 10. 10108, 2023. URL http:://arxiv.org//dff/////
10108
Bin Zhang, Hangyu Mao, Jingqing Ruan, Ying Wen, Yang Li, Shao Zhang, Zhiwei Xu, Dapeng Li, Ziyue Li, Rui Zhao, Lijuan Li, and Guoliang Fan. Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach. In arXiv preprint arXiv:23 138884, 2024a. /ttps:/::///axxxv org/ /2311.
Yifan Zhang, Yang Yuan, and Andrew Chi-Chih Yao. On the Diagram of Thought. In arXiv preprint arXiv:2409.10338, 2024b. URL
2409. 10038
Zheyuan Zhang. Daniel Zhang-Li, Jifan Yu, Linlu Gong. Jinchang Zhou, Zhiyuan Liu, Lei Hou, and Juanzi Li. Simulating Classroom Education with LLM--Empoweeed Agents. In arXiv preprint
arXiv:2406. 19226, 2024c. httphttts///arii..oom 1996
Andrew Zhao, Daniel Huang, Quentin Xu, Matthieu Lin, Yong-Jin Liu, and Gao Huang. Expel: LLM Agents are Experiential Learners. In AAA/ Conference on Artificial Intelligence (AAAI), 2024.
URL http::h/t///.i..... htt.://...oor///// 1609/ aaai. v38117.8.. 29936
Zirui Zhao, Wee Sun Lee, and David Hsu. Large Language Models as Commonsense Knowl- edge for Lcrge-Scale Task Planning. In Advances in Neural Information Processing Systems (NeurLPS), 2023. URL https::/pproceedings.cett www.eeiddinnssnnnissss.... cctpppprrccppppppppppppppppppp.e httpp//ppee...leeeeee/e///////// 65a39213d7d0ee eb5d1 http://00000000007777077000----0
pdf
Wangchunshu Zhou, Yixin Ou, Shengwei Ding. Long Li, Jialong Wu, Tiannan Wang, Jiamin Chen, Shuai Wang, Xiaohua Xu, Ningyu Zhang. Huajun Chen, and Yuchen Eleanor Jiang. Symbolic Learning Enables Self-Evolvvng Agents. In arXiv preprint arXiv:2406.18532 2024. /ttps:
//arxiv.org/abs//2466 18532.
Yuchen Zhuang, Xiang Chen, Tong Yu, Saayan Mitra, Victor Bursztyn, Ryan A. Rossi, Somdeb Sarkhel, and Chao Zhang. ToolChain*: Efficient Action Space Navigation Large Language Models with A Search. In arXiv preprint arXiv:2310.13227 2024. httpthtsts/:/.....t. org/
pdf/2310. 13227
Mingchen Zhuge, Wenyi Wang, Louis Kirsch, Francesco Faccio, Dmitrii Khizbullin, and Jurgen Schmidhuber. Language Agents as Optimizable Graphs. In International Conference Machine Learning (ICML), 2024. URL httphtttp////iiii www.:ooggg/p.aagoooo 16823
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Reviews:
[{"review_id": "nJ9QYgS476", "reviewer": "Reviewer_TcPL", "summary": "The paper introduces a novel multi-agent collaboration framework, MACNET, which utilizes directed acyclic graphs (DAGs) to organize agents and orchestrate their reasoning for autonomous task solving. In their extensive evaluations, the paper finds that multi-agent collaboration can achieve better performance than the individure agent and also reveals a collaborative scaling law, indicating that performance follows a logistic growth pattern as the number of agents increases. The study also demonstrates the superior performance of irregular topologies over regular ones in multi-agent collaboration, attributing this to the catalyst effect of scaling agents on multidimensional considerations during interactive reflection and refinement.", "questions": "I find the modeling of MAS LLM agent and the study of the scaling law in the paper quite intriguing, but I have reservations about the problems that the multi-agent is used to solve. \n\nIn the experiments within the paper, single-agent methods like CoT and AUTOGPT are compared, but to my knowledge, for datasets like MMLU, the pure LLMs (without designing agents deliberately) can already achieve a performance of 0.8 as can be check in [paperswithcode](https://paperswithcode.com/sota/multi-task-language-understanding-on-mmlu), and for HumanEval, based on [1],[2], the performance of above 0.8 can also be achieved.  The works I mentioned above are all better than the baselines and this method MACNET.\n\nSo, if using a multi-agent collaboration work, considering ten to thousands of agents, only achieves the performance shown in Table 1, I don't think it proves the necessity of multi-agent; in fact, it could be seen as undermining their necessity. \n\n- Compare MACNET against these state-of-the-art single-agent methods and Explain why MACNET underperforms compared to these methods, if that is indeed the case.\n- Can you discuss specific scenarios or types of tasks where multi-agent approaches might offer unique advantages over single-agent methods?\n\n[1] Zhong, Li, Zilong Wang, and Jingbo Shang. \"Ldb: A large language model debugger via verifying runtime execution step-by-step.\" arXiv preprint arXiv:2402.16906 (2024).\n\n[2] Shi, Yuling, et al. \"From Code to Correctness: Closing the Last Mile of Code Generation with Hierarchical Debugging.\" arXiv preprint arXiv:2410.01215 (2024).", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- The paper introduces a pioneering framework that employs directed acyclic graphs (DAGs) to organize agents within a multi-agent system. It provides insightful comparisons between various topologies.\n\n- The research delves into the impact of scaling the number of collaborative agents on overall performance, uncovering a collaborative scaling law. This law suggests that performance follows a logistic growth pattern as agents are added, which is a significant finding as it parallels the neural scaling law but applies to the domain of agent collaboration.", "weaknesses": "Check in Questions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "z8gzBS2Adl", "reviewer": "Reviewer_2D7q", "summary": "The paper explores the scaling law of collaboration for LLM-based agents. The authors propose a paradigm, MacNet, that allows for the collaboration of up to thousands of agents, and demonstrate its superiority over baseline methods across four diverse datasets. Utilizing MacNet, the authors investigate the impact of different topological structures on multi-agent collaboration, distill a scaling law concerning the number of nodes in multi-agent graphs, and analyze the factors leading to the emergence of multi-agent collaboration through case studies.", "questions": "Beyond the major concerns I list, there are the following questions: \n1. Can the authors provide mathematical insights regarding the implications of multiple LLMs collaborating? From a mathematical theory standpoint, what is the difference between increasing the size of large models and increasing the number of large models collaborating? \n2. The abstract part mentions \"with collaborative emergence occurring earlier than traditional neural emergence\". How should this be understood? Can the authors provide a detailed discussion?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper is clearly articulated and well-structured, with the discussion on \"why random topologies perform better\" and \"tail aspects being factors leading to the emergence of collaboration\" being particularly enlightening.\n2. The experimental section of the paper is comprehensive (although there may be areas lacking in rigor), covering a wide range of perspectives in its design. I believe the work presented in this paper can inspire the entire research community.", "weaknesses": "As an empirical research paper, the experimental design and conclusions of the paper are particularly important and need to be sufficiently rigorous. I currently have some concerns.\n\n(Major) 1. Regarding the emergence in multi-agent systems. The paper discusses the laws of neural scale emergence and agent collaboration emergence between lines 427 and 429. However, the claim of collaboration emergence might not be rigorous enough. For instance, in neural emergence, a neural network model can evolve from merely recognizing images to handling PhD-level problems, which is a significant leap. In contrast, the emergence described in this paper only involves an improvement in success rate from around 55% to 70% on certain datasets, a particularly small span that might not even qualify as emergence. If an appropriate case could be found where multiple agents attempt to solve a very difficult problem, and the agents manage to solve this problem once a certain scale is reached (analogous to human society solving a very complex and challenging problem), it would greatly enhance the paper.\n\n(Major) 2. Corresponding to the above is the selection of benchmarks and the design of metrics. A fundamental question is why, even with the use of up to thousands of agents, the performance improvement is only around 20%, without a breakthrough improvement akin to advancing from grade school level to PhD level. Another phenomenon is that, as mentioned in section 3.4 of the paper, while the number of Interacted Aspects continues to increase when the number of agents ranges from 2^5 to 2^6, there is no significant improvement in performance. Does this imply that the current tasks are not challenging enough? Are the metrics for quantifying performance not rich enough?\n\n(Minor) The paper mentions \" alongside neuron collaboration, agent collaboration may serve as a shortcut to enhance intelligence levels, especially when large-scale retraining resources such as data and hardware are constrained.\" To arrive at this general conclusion, conducting experiments only on the same series of large models (GPT3.5 and GPT4) might be insufficient. Experiments with other types of LLMs might be necessary.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "46aeOXxzCq", "reviewer": "Reviewer_e6WL", "summary": "This paper presents pioneering work on scaling large language model-based multi-agent collaboration to the level of a thousand agents. The authors propose a method to automatically organize LLM agents into a directed acyclic graph (DAG) without needing specific adjustments for generalization. Comprehensive evaluations are conducted on diverse benchmarks against a variety of baselines. The influence of topologies, the number of agents, and other factors on collaboration performance is discussed in depth. The paper is well-written with a clear flow. The scaling law experiments involving over a thousand agents represent a truly rare finding in the literature I have read about LLM agents. I would highly recommend accepting this paper.", "questions": "Questions are asked in above sections.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "The scaling of multi-agent systems powered by LLMs is a significant research question for the community.\n\nThe idea of organizing LLM agents using a DAG is not novel; however, the authors present a comprehensive analysis of factors including types of topologies, densities, directions, and scales. \n\nThe experiments involve at maximum of 1000 LLM agents, with the help of the proposed memory control mechanism to avoid context explosion.\n\nThe proposed method is highly flexible and generalizable, requiring little human supervision or prior knowledge as claimed by the authors.", "weaknesses": "What is the rationale for using \"instructor\" for edges and \"executor\" for nodes? It seems that both can be treated as nodes with different profiles, with edges between them indicating interactions.\n\nImplementation details are somewhat brief. As mentioned in lines 259-261, agent profiles and tools are generated by GPT-4 and randomly assigned to network agents. It is unclear how this process can be fully automated without interventions or adjustments at the scale of 1,000+ agents. Is there any guarantee that the randomly generated agent network can solve the given problem?\n\nHow can we ensure that the memory control mechanism does not omit context information? Additionally, how many agent profiles and tools are generated? \n\nHow many repeated measurements are conducted for each method across different datasets? What is the variance in performance?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "nZTEKOegAF", "reviewer": "Reviewer_P62L", "summary": "The paper proposes MacNet, a multi-agent collaboration network. Inspired by neural scaling laws, the authors investigate how the number of agents and the communication topology between agents impacts performance when agents are made to collaborate on a given problem. Specifically:\n\n- MacNet creates a Directed Acyclic Graph (DAG) of agents that attempt to solve a given task in analogy to a neural network. The **nodes and edges are agents**, where the nodes are \"executors\", which accomplish tasks, while the edges are \"instructors\", which evaluate the task and provide feedback. Agents are allowed to **interactively and iteratively refine** their subtask solutions, where an instructor asks an executor to refine their proposed subtask solution. To avoid **content explosion**, nodes in the network are expected to only pass on the proposed solution, not its entire reasoning, and they are also expected to aggregate incoming solutions. Importantly, MacNet does not have a specific topology; the method can be applied to many different types of topologies, including random topologies.\n\n- To evaluate the performance of MacNet, the authors evaluate on MMLU, HumanEval, SRDD and CommonGen-Hard. They compare MacNet variants based on different DAG topologies against Chain of Thought, AutoGPT, GPTSwarm and AgentVerse. Their method exhibits significantly better performance on MMLU, mostly worse performance on HumanEval, slightly better on SRDD and sometimes better performance on CommonGen. \n  - They compare the performance of different topologies against each other, finding that random topologies often perform statistically better.\n  - They observe the performance of different topologies across multiple network sizes, demonstrating a non-decreasing performance trend.\n  - They perform an error analysis on one of the topologies at different sizes, finding longer tails when the number of agents increases which diversifies the solutions, making the proposed solutions more comprehensive.\n\nUltimately, they conclude that agent collaboration could bypass the need for retraining by enabling inference time performance scaling through scaling multi-agent collaboration.", "questions": "- Doesn't the interative feedback between instructors and executors make the graph not directed? You could get stuck in a loop there.\n\n- Have you looked into letting the agents attempt to determine what a good topology is? I.e. Agents specify to which other agent they believe they should send their result.\n\n- If quality is the average across all tasks, does each benchmark have the same amount of tasks? Or did you average specifically task-wise, or across all 4 datasets?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "# originality \n\n* The authors study the scaling of the number of agents during inference, a relatively **novel, unexplored problem**. \n\n* The authors **extend the interaction DAG setting** to a functionally bipartite structure with agents as nodes and edges, and **present representative topologies** that capture prevalent DAG structures \n\n* The authors **address the limitations of prior work** by avoiding content explosion\n\n# quality \n\n* The authors describe the problem setting with the appropriate technical language, as well as **motivating theoretical justifications** for MacNet's advantages over existing solutions.\n\n* The authors compare against a set of **timely and relevant baselines** on ** diverse datasets**.\n\n* The authors use **identical and reproducible settings** across methods.\n\n* The authors provide **multiple perspectives** on their results, connecting observed phenomena to novel and documented insights.\n\n# clarity\n\n* The paper is **well-structured**, making it easy to read and follow along.\n\n* The paper uses **interesting visualizations** to communicate interesting insights, such as longer tails with more agents.\n\n* The paper employs **precise formal language and notation** to effectively communicate their points.\n\n# significance\n\n* The paper has a significant impact on **LLM-based applications**\n\n* The paper is very **timely** as the research on multi-agent systems begins to regain the interest of the machine learning community.\n\n* The paper offers **interesting insights** into collaborative inference-time structures across many agents, which is relevant to ongoing discussions around agent proliferation.\n\n* The paper outlines **future directions of work**.", "weaknesses": "# Problem Setting\n\n* **collaborative emergence is undefined**, even though it seems to be a central concept of the paper.\n\n# Results\n\n## Major \n- **Lack of transparent methods**: The use of the closed source line of GPT models hinders the analysis. The authors take the stance that the multi-agent scaling laws are analogous to the neural scaling laws, but we do not know much about the underlying models. It would be more judicious to compare open-source models, for which we know the FLOPs and parameter counts. This could allow for comparisons such as a single 7B model vs a MacNet of 7 1B models. An additional point here is that we do not know what type of post-training the GPT models undergo, which could help in the collaboration. The main point of this criticism is the following: **the use of a closed-source method in your analysis effectively hinders the potential for your results to generalize to other LLM-based agents**. We don't know the impact of model size, architecture or training strategy for the GPT models, so we can't know how the established analysis and scaling laws transfer to other architectures.\n\n\n- **Entangled sources of improved performance**: Since the actual FLOPs used are not kept track of, it becomes impossible to tell whether the improved results are due to additional computational resources spent on figuring out a solution. It would be desirable to **isolate the impact of the multi-agent collaboration** from other factors, such as how much compute is poured into a given solution. Otherwise, it could be that a given MacNet works better because it pours more resources and time into a solution. While this result would be interesting in itself, it would shift the significance of the approach mostly onto methods for scaling inference-time compute, at which point you'd have to normalize for how much time and compute is spent on a given solution. You would then also have to compare against other non-multi-agent methods for scaling inference time compute. Instead, the diversity and comprehensiveness of the solutions output by a MacNet could be the subject of a greater analysis, while being more direct about the resource requirements.\n \n## Minor\n- **Lack of scaling law analysis**: The scaling laws, although the sigmoid functional form is described, are not actually fit, nor analyzed. Furthermore, there is little to no discussion as to *why* the scaling laws should take a sigmoidal form in this case.\n\n\n# Overall \n\nThe presentation of the paper is solid. The writing is easy to understand and the use of technical language is judiciously sparse. The problem setting is good and the contribution would be too, but the soundness of the claims is affected by the limits to the results outlined above. This unfortunately impacts the value of the contribution itself, as the results are not wide-ranging.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)