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arxiv: 2606.00655 · v1 · pith:IG6KFW3Hnew · submitted 2026-05-30 · 💻 cs.MA · cs.AI· cs.CY

Scaling Behavior of Single LLM-Driven Multi-Agent Systems

Jialing Li , Zhouhong Gu , Yin Cai , Hongwei Feng This is my paper

Pith reviewed 2026-06-28 18:09 UTC · model grok-4.3

classification 💻 cs.MA cs.AIcs.CY
keywords multi-agent systemsLLM scalingdiminishing returnscoordination overheadcollaborative intelligenceSIMAS frameworkagent countcollective intelligence
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The pith

Multi-agent LLM systems show diminishing returns as agent count rises due to coordination overhead outweighing synergy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tests how performance in homogeneous LLM-driven multi-agent systems changes when the number of agents increases, using a controlled sequential communication setup to separate collaboration from other variables. It establishes that gains do not rise steadily with added agents but instead plateau because coordination costs grow faster than collaborative benefits. This observation challenges the common practice of simply adding agents to improve results on complex tasks. The work also shows that base model capability and task type set the point at which extra agents stop helping.

Core claim

Using the Sequential Iterative Multi-Agent System framework across diverse tasks and model scales, the performance of a homogeneous multi-agent system does not increase monotonically with the number of agents. Performance instead follows diminishing returns shaped by the tension between collaborative synergy and coordination overhead. Collective intelligence appears only when interaction is designed strategically and the base LLM is sufficiently capable; degradation traces to coordination costs rather than context-length limits, and the pattern holds across other interaction structures such as structured debate topologies.

What carries the argument

The Sequential Iterative Multi-Agent System (SIMAS) framework, a minimalist sequential inter-agent communication architecture that isolates scaling effects from model or knowledge differences.

Load-bearing premise

The SIMAS framework and chosen tasks isolate collaboration effects from differences in the underlying models or agent knowledge.

What would settle it

An experiment that increases agent count inside the same SIMAS setup and observes steady performance gains without coordination slowdown or plateau.

Figures

Figures reproduced from arXiv: 2606.00655 by Hongwei Feng, Jialing Li, Yin Cai, Zhouhong Gu.

Figure 1
Figure 1. Figure 1: Workflow of the SIMAS. A group of n agents (a1, a2, ..., an), each configured with a distinct profile (personality, core beliefs, expertise), engages in T rounds of sequential discussion. In each round, every agent generates a response based on the progressively accumulated conversation history hi−1,t, which monotonically expands to hn,t. After T rounds, the first agent a1 synthesizes the final output o fr… view at source ↗
Figure 2
Figure 2. Figure 2: Result of models with small parameters on all [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Result of models with large parameters on all [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Task-type modulation of scaling on Llama-3.1-70B and Qwen2.5-72B. Reasoning tasks suffer sharp [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Model Type Impact on College Physics for [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qwen2.5-72B under fixed context length. The [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Llama3.1-70B under AutoGen debate. The inverted-U pattern persists under AutoGen’s structured debate topology. Finding 5 Structured debate architectures can raise peak performance and partially delay de￾cline, but do not eliminate the fundamen￾tal inverted-U trade-off. As shown in [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: CoT vs. SIMAS on AIME 2025. SIMAS fails catastrophically on complex multi-step reasoning. Finding 6 For reasoning tasks, the primary failure mode of minimalist MAS is the fragmen￾tation of coherent thought. On reasoning-intensive benchmarks such as ab￾stract algebra (Figure. 20a) and AIME 2025 (Fig￾ure. 8), CoT consistently matches or significantly surpasses the best SIMAS configuration. The performance ga… view at source ↗
Figure 10
Figure 10. Figure 10: Prompt template for agent profile generation. 12 [PITH_FULL_IMAGE:figures/full_fig_p012_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Model Scale Impact of Llama 3.1 on College Physics [PITH_FULL_IMAGE:figures/full_fig_p013_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Model Scale Impact of Qwen2.5 on College [PITH_FULL_IMAGE:figures/full_fig_p013_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Performance across different task types on Meta-Llama-3.1-70B. [PITH_FULL_IMAGE:figures/full_fig_p014_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Model Type Impact on College Physics for [PITH_FULL_IMAGE:figures/full_fig_p015_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Model Type Impact on College Physics for [PITH_FULL_IMAGE:figures/full_fig_p015_15.png] view at source ↗
Figure 17
Figure 17. Figure 17: Coding CoT prompt template. 3. Open-ended CoT This template prompts the model for a comprehen￾sive and insightful analysis of open-ended ques￾tions, guided by specific evaluation criteria. Prompt System Instruction: Please analyze the following open-ended ques￾tion in depth. Use step-by-step reasoning and provide a comprehensive and insightful re￾sponse. Question: {problem.question} Evaluation criteria: {… view at source ↗
Figure 16
Figure 16. Figure 16: Simple CoT prompt template. 2. Coding CoT This template instructs the model to analyze a cod￾ing problem step-by-step and provide a complete, efficient solution with code. Prompt System Instruction: Please solve the following coding problem. Use step-by-step reasoning and provide a complete, efficient code solution. Problem: {problem.question} Programming language: {language} Constraints: {constraints_str… view at source ↗
Figure 18
Figure 18. Figure 18: Open-ended CoT prompt template. E.2 Evaluation Protocol for Generation Tasks The dataset comprises a custom collection of 10 coding problems and 15 open-ended questions. The coding problems involve 5 algorithmic prob￾lems and 5 complex software development projects that go beyond simple algorithmic implementation, 17 [PITH_FULL_IMAGE:figures/full_fig_p017_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Prompt template for AI response evaluation. [PITH_FULL_IMAGE:figures/full_fig_p018_19.png] view at source ↗
read the original abstract

The burgeoning field of LLM-based Multi-Agent Systems (MAS) promises to tackle complex tasks through collaborative intelligence, yet fundamental questions regarding their scaling behavior and intrinsic collective dynamics remain underexplored. This paper systematically investigates how the performance of a homogeneous MAS evolves as the number of agents increases, isolating the variable of collaboration from model or knowledge heterogeneity. We propose the Sequential Iterative Multi-Agent System (SIMAS) framework, a minimalist architecture centered on sequential inter-agent communication, to clearly observe scaling effects. Through extensive experiments across diverse tasks and model scales, we establish that MAS performance does not scale monotonically with agent count but follows a pattern of diminishing returns, governed by a trade-off between collaborative synergy and coordination overhead. Our findings reveal that effective MAS requires a sufficiently capable base LLM, that task type critically modulates the optimal agent count, and that collective intelligence is an emergent property contingent on strategic interaction design rather than a guaranteed outcome of agent plurality. The performance degradation stems coordination overhead rather than merely long-context failure, and the scaling tendency generalizes across interaction architectures like structured debate topologies. This work provides a foundational understanding of MAS scaling laws, offering practical guidance for designing efficient collaborative systems and challenging the prevailing assumption that more agents invariably lead to better performance.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper claims that homogeneous LLM-based multi-agent systems using the proposed Sequential Iterative Multi-Agent System (SIMAS) framework exhibit non-monotonic scaling: performance improves initially with added agents due to collaborative synergy but then diminishes due to coordination overhead. This is supported by experiments across diverse tasks and model scales showing that effective MAS requires a capable base LLM, that task type modulates optimal agent count, and that collective intelligence is an emergent property of interaction design rather than agent count alone. The degradation is attributed specifically to coordination overhead (not long-context failure), with the pattern generalizing to other interaction architectures like structured debate.

Significance. If the isolation of collaboration effects is robustly demonstrated, the work supplies useful empirical evidence against the common assumption that more agents always yield better MAS performance. It offers concrete guidance on scaling limits and interaction design, backed by experiments spanning multiple tasks and model scales plus generalization checks across architectures. These elements would constitute a solid foundational contribution to understanding collective dynamics in LLM-driven systems.

major comments (1)
  1. [§3 (SIMAS Framework) and §4 (Experiments)] §3 (SIMAS Framework) and §4 (Experiments): The claim that SIMAS isolates collaboration effects from model/knowledge heterogeneity via homogeneous agents and sequential communication is load-bearing for attributing diminishing returns to a synergy-overhead trade-off. Sequential iteration inherently accumulates interaction history in the shared context, which can create effective differences in information access or conditioning as agent count grows. Without explicit controls (e.g., fixed-context ablations or per-agent capability measurements across scales) confirming constant effective capability, the attribution to coordination overhead rather than context drift or prompt drift is not fully verified.
minor comments (2)
  1. [Abstract and §1] Abstract and §1: The statement that 'the performance degradation stems coordination overhead rather than merely long-context failure' would benefit from a brief forward reference to the specific control experiment or metric used to distinguish the two.
  2. [Figure captions and §4] Figure captions and §4: Ensure all scaling plots include error bars or confidence intervals and state the number of runs per data point to allow assessment of variability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the thoughtful and detailed review. The major comment raises an important point about potential confounds in our isolation of collaboration effects. We address it directly below and commit to revisions that strengthen the attribution.

read point-by-point responses

  1. Referee: [§3 (SIMAS Framework) and §4 (Experiments)] §3 (SIMAS Framework) and §4 (Experiments): The claim that SIMAS isolates collaboration effects from model/knowledge heterogeneity via homogeneous agents and sequential communication is load-bearing for attributing diminishing returns to a synergy-overhead trade-off. Sequential iteration inherently accumulates interaction history in the shared context, which can create effective differences in information access or conditioning as agent count grows. Without explicit controls (e.g., fixed-context ablations or per-agent capability measurements across scales) confirming constant effective capability, the attribution to coordination overhead rather than context drift or prompt drift is not fully verified.

    Authors: We agree that sequential accumulation of history is inherent to the SIMAS design and could in principle introduce conditioning differences. However, the manuscript already reports that degradation persists across models with large context windows (128k tokens) and that performance drops are observed well before context limits are approached; we further show the same non-monotonic pattern under structured debate topologies that do not rely on a single accumulating context. These results support our attribution to coordination overhead (e.g., increased decision conflicts and communication complexity) rather than context or prompt drift alone. That said, the referee is correct that we lack explicit fixed-context or per-agent capability ablations. We will add these controls in the revision (new subsection in §4) to more rigorously rule out drift effects. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical observations from experiments

full rationale

The paper reports direct experimental results on MAS scaling using the SIMAS framework, with performance measured across varying agent counts on fixed tasks and homogeneous LLMs. No equations, parameter fits, uniqueness theorems, or self-citations are presented as load-bearing steps in any derivation chain. Claims of non-monotonic scaling and synergy-overhead trade-offs are framed as observed patterns, not as outputs that reduce by construction to the experimental inputs or prior author work. The analysis is self-contained against external benchmarks via the described task evaluations.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review provides no explicit free parameters, axioms, or invented entities; the SIMAS framework is described as a minimalist architecture but no further decomposition is possible.

pith-pipeline@v0.9.1-grok · 5753 in / 982 out tokens · 21983 ms · 2026-06-28T18:09:55.286468+00:00 · methodology

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Reference graph

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