Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents
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 m
Record details
Published: 20 April 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Finance, VC & PE
Retrieved: 14 July 2026
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ethics.ai (20 April 2026), “Prompt Optimization Enables Stable Algorithmic Collusion in LLM Agents,” evidence record 5635, https://ethics.ai/record/5635 (originally published by arXiv).
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