优化提示词让大模型代理自发形成稳定串通，影响市场公平。
Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents
- 用元学习循环自动优化提示词，引导代理协作
- 优化后代理在多市场中实现更高质量的隐性串通
- 适合关注AI安全与多智能体系统的研究者
市场中的大模型代理存在算法串通风险。尽管已有研究显示大模型代理通过默契协调达成超竞争价格，但以往工作依赖人工设计的提示词。随着提示词优化新范式的出现，亟需新的方法来理解自主代理行为。我们探究了提示词优化是否会在市场模拟中引发涌现的串通行为。提出一种元学习循环：大模型代理参与双寡头市场，一个大模型元优化器持续迭代优化共享战略指导。实验表明，元提示词优化使代理发现稳定的隐性串通策略，协调质量显著优于基线代理。这些行为可泛化至未见测试市场，表明发现了通用协调原则。对演化提示词的分析揭示了通过稳定共享策略实现系统性协调机制。研究呼吁进一步探讨自主多智能体系统中的AI安全问题。
原文摘要 · Abstract (English)
LLM agents in markets present algorithmic collusion risks. While prior work shows LLM agents reach supracompetitive prices through tacit coordination, existing research focuses on hand-crafted prompts. The emerging paradigm of prompt optimization necessitates new methodologies for understanding autonomous agent behavior. We investigate whether prompt optimization leads to emergent collusive behaviors in market simulations. We propose a meta-learning loop where LLM agents participate in duopoly markets and an LLM meta-optimizer iteratively refines shared strategic guidance. Our experiments reveal that meta-prompt optimization enables agents to discover stable tacit collusion strategies with substantially improved coordination quality compared to baseline agents. These behaviors generalize to held-out test markets, indicating discovery of general coordination principles. Analysis of evolved prompts reveals systematic coordination mechanisms through stable shared strategies. Our findings call for further investigation into AI safety implications in autonomous multi-agent systems.
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