LLM-Powered Swarms: A New Frontier or a Conceptual Stretch?
cs.AI
Submitted: 2025-06-17
Updated: 2026-08-27
Comments: This is the author's version of a paper submitted to IEEE Intelligent Systems. 2 Tables, 2 Figures
Code: https://github.com/Atta66/swarms-rulebased-vs-llms
License: http://creativecommons.org/licenses/by/4.0/
The gist: Swarm intelligence describes how simple, decentralized agents can collectively produce complex behaviors.
Terminology
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.
Sources
- LLaMA: Open and Efficient Foundation Language Models
- Qwen Technical Report
- A Survey of Quantization Methods for Efficient Neural Network Inference
- Gemma: Open Models Based on Gemini Research and Technology
- Mistral 7B
- Qwen3 Technical Report
- Capabilities of GPT-5 on Multimodal Medical Reasoning
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