LLM-Powered Swarms: A New Frontier or a Conceptual Stretch?

Title:LLM-Powered Swarms: A New Frontier or a Conceptual Stretch?

Abstract:Swarm intelligence describes how simple, decentralized agents can collectively produce complex behaviors. Recently, the concept of swarming has been extended to large language model (LLM)-powered systems, such as OpenAI's Swarm (OAS) framework, where agents coordinate through natural language prompts. This paper evaluates whether such systems capture the fundamental principles of classical swarm intelligence: decentralization, simplicity, emergence, and scalability. Using OAS, we implement and compare classical and LLM-based versions of two well-established swarm algorithms: Boids and Ant Colony Optimization. Results indicate that while LLM-powered swarms can emulate swarm-like dynamics, they are constrained by substantial computational overhead. For instance, our LLM-based Boids simulation required roughly 300x more computation time than its classical counterpart, highlighting current limitations in applying LLM-driven swarms to real-time systems.

This is the author's version of a paper submitted to IEEE Intelligent Systems. 2 Tables, 2 Figures

Artificial Intelligence (cs.AI)

arXiv:2506.14496 [cs.AI]

(or arXiv:2506.14496v3 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2506.14496 Focus to learn more arXiv-issued DOI via DataCite

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