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大模型驱动的群集智能：新前沿还是概念延伸？

文章背景与核心概要

本文评估了大语言模型（LLM）驱动的系统（例如 OpenAI 的 Swarm 框架）是否真正捕捉到了经典群集智能的核心原则，即去中心化、简单性、涌现性和可扩展性。通过实现经典算法（如 Boids 算法和蚁群优化算法）的传统版本与基于 LLM 的版本，作者证明了尽管 LLM 群集能够成功模仿集体行为，但它们带来了巨大的计算开销（例如，基于 LLM 的 Boids 仿真所需的计算时间大约是传统对应版本的 300倍），这对其在实时系统中的可行性提出了质疑。

This research paper evaluates whether Large Language Model (LLM)-powered systems (such as OpenAI's Swarm framework) genuinely capture the core tenets of classical swarm intelligence—namely decentralization, simplicity, emergence, and scalability. By implementing both classical and LLM-based iterations of classical algorithms (Boids and Ant Colony Optimization), the authors demonstrate that while LLM swarms can successfully mimic collective behaviors, they suffer from a massive computational overhead (e.g., the LLM-based Boids simulation required roughly 300× more computation time than its traditional counterpart), casting doubt on their viability for real-time systems.

论文元数据 (Paper Metadata)

  • arXiv ID: 2506.14496 [cs.AI]
  • 学科分类: 计算机科学 > 人工智能
  • 作者:
  • Muhammad Atta Ur Rahman
  • Melanie Schranz
  • Samira Hayat
  • 提交日期:
  • 提交于: 2025年6月17日 (v1)
  • 最后修订: 2026年8月27日 (v3)
  • 目标出版物: 提交给 IEEE Intelligent Systems 的作者版本（包含 2 个表格，2 个图表）
  • arXiv ID: 2506.14496 [cs.AI]
  • Subject: Computer Science > Artificial Intelligence
  • Authors:
  • Muhammad Atta Ur Rahman
  • Melanie Schranz
  • Samira Hayat
  • Submission Dates:
  • Submitted: June 17, 2025 (v1)
  • Last Revised: August 27, 2026 (v3)
  • Target Publication: Author's version of a paper submitted to IEEE Intelligent Systems (2 Tables, 2 Figures)

摘要 (Abstract)

群集智能描述了简单、去中心化的智能体如何能够共同产生复杂的行为。最近，群集的概念已经扩展到大语言模型（LLM）驱动的系统中，例如 OpenAI 的 Swarm (OAS) 框架，其中智能体通过自然语言提示进行协调。本文评估了此类系统是否符合经典群集智能的基本原则：去中心化、简单性、涌现性和可扩展性。利用 OAS，我们实现并比较了两个成熟的群集算法（Boids 算法和蚁群优化算法）的经典版本和基于 LLM 的版本。结果表明，尽管大模型驱动的群集可以模拟类似群集的动态，但它们受到巨大计算开销的限制。例如，我们基于 LLM 的 Boids 仿真所需的计算时间大约是其传统对应版本的 300 倍，这凸显了当前将 LLM 驱动的群集应用于实时系统的局限性。

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.

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