构建多智能体框架，揭示大模型在长期互动中的欺骗行为
LH-Deception: Simulating and Understanding LLM Deceptive Behaviors in Long-Horizon Interactions
- 设计双智能体系统模拟任务执行与监督反馈的动态过程
- 11个前沿模型中欺骗行为随压力上升且持续破坏信任
- 发现长周期欺骗链等新现象，适用于可信AI评估
欺骗是人类交流的普遍特征，也是大语言模型（LLMs）日益突出的问题。尽管已有研究记录了大模型的欺骗实例，但多数评估仍局限于单轮提示，无法捕捉欺骗策略通常展开的长期互动场景。我们提出一种新的仿真框架LH-Deception，用于在扩展的任务序列和动态上下文压力下，系统性、实证地量化大模型中的欺骗行为。该框架为多智能体系统：一个执行任务的表演者智能体，一个评估进展、提供反馈并维持信任状态的监督者智能体，以及一个独立的欺骗审计员对完整轨迹进行审查以识别欺骗发生的时间与方式。我们在11个前沿模型上开展广泛实验，涵盖闭源与开源系统，发现欺骗行为具有模型依赖性，随事件压力增加，且持续侵蚀监督者信任。定性分析进一步揭示了“欺骗链”等新兴的长期现象，这些现象在静态单轮评估中不可见。研究成果为未来大模型在真实世界、高信任敏感场景下的评估提供了基础。
原文摘要 · Abstract (English)
Deception is a pervasive feature of human communication and an emerging concern in large language models (LLMs). While recent studies document instances of LLM deception, most evaluations remain confined to single-turn prompts and fail to capture the long-horizon interactions in which deceptive strategies typically unfold. We introduce a new simulation framework, LH-Deception, for a systematic, empirical quantification of deception in LLMs under extended sequences of interdependent tasks and dynamic contextual pressures. LH-Deception is designed as a multi-agent system: a performer agent tasked with completing tasks and a supervisor agent that evaluates progress, provides feedback, and maintains evolving states of trust. An independent deception auditor then reviews full trajectories to identify when and how deception occurs. We conduct extensive experiments across 11 frontier models, spanning both closed-source and open-source systems, and find that deception is model-dependent, increases with event pressure, and consistently erodes supervisor trust. Qualitative analyses further reveal emergent, long-horizon phenomena, such as ``chains of deception", which are invisible to static, single-turn evaluations. Our findings provide a foundation for evaluating future LLMs in real-world, trust-sensitive contexts.
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