多智能体商业模拟中涌现失对齐沟通 Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce

精读摘要

前沿 LLM 智能体越来越多地代表不同委托方进行交易，且常用自然语言而非结构化 API 沟通，这带来了新的安全盲区。这项研究分析了 20 次为期一年的多智能体商业模拟运行中产生的 2,583 封智能体间邮件，考察长周期、多委托方、真实运营状态叠加下的失对齐行为。相比以往针对单智能体的对抗测试，这种多智能体自然语言交易场景更能暴露真实风险，为此类系统的治理提供了数据基础。 Frontier LLM agents increasingly transact on behalf of separate principals using natural language, a setting whose misalignment risks are under-measured. This study analyzes 2,583 inter-agent emails from 20 one-year simulation runs of a multi-agent commerce environment, combining long horizons, separate principals, and real operational state. It provides empirical grounding for governing multi-agent commerce systems.