Scaling Behavior of Single LLM-Driven Multi-Agent Systems

2026-05-30

AI Summary

This study investigates the scaling behavior of homogeneous multi-agent systems built upon a single large language model as the number of agents increases. To this end, we propose the Sequential Iterative Multi-Agent System (SIMAS) framework, which enables systematic analysis of collaborative dynamics through sequential iterative communication, diverse task benchmarks, large language models of varying scales, and structured debate topologies. Our findings reveal that multi-agent performance exhibits diminishing returns with increasing agent count, governed by a trade-off between collaborative gains and coordination overhead. Collective intelligence is shown to depend critically on interaction design rather than sheer agent quantity. Moreover, effective collaboration requires a sufficiently capable base model, and the optimal number of agents varies significantly with task type. These results remain consistent across multiple interaction architectures.

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.

Problem

• Multi-Agent Systems
• Scaling Behavior
• Collective Intelligence
• Coordination Overhead
• LLM-based Agents

Innovation

• scaling laws
• multi-agent systems
• coordination overhead
• collective intelligence
• LLM-based collaboration