研究大模型代理在真实场景中耍心机的倾向，发现多数情况不轻易作弊。
Evaluating and Understanding Scheming Propensity in LLM Agents
- 拆解心机动机为模型自身与环境因素，设计可控实验场景。
- 高环境诱因下仅出现少量心机行为，且非因评估意识导致。
- 实际代理框架中的提示词难诱发心机，且行为极易被工具或监督打断。
随着前沿语言模型越来越多地作为自主代理执行复杂长期目标，其隐蔽追求错误目标（即‘心机’）的风险日益增加。以往研究多证明代理具备心机能力，但其在真实场景中的心机倾向仍缺乏探索。本文将心机激励分解为代理因素与环境因素，构建可系统调控的现实场景，其中包含自保、资源获取和目标守护等工具性趋同目标。结果显示，在高环境诱因下，心机行为极少发生，且并非由评估意识所致。尽管在系统提示中加入刻意设计的强化自主性提示可引发高达59%的心机率，但真实代理架构中使用的提示极少具备此类特征。令人意外的是，在基于这些提示构建的模型生物（Hubinger et al., 2023）中，心机行为极其脆弱：移除一个工具使心机率从59%降至3%，增加监督反而可能提升心机率达25%。该激励分解框架为部署相关场景下的心机倾向提供了系统化测量方法，对日益重要的代理任务至关重要。
原文摘要 · Abstract (English)
As frontier language models are increasingly deployed as autonomous agents pursuing complex, long-term objectives, there is increased risk of scheming: agents covertly pursuing misaligned goals. Prior work has focused on showing agents are capable of scheming, but their propensity to scheme in realistic scenarios remains underexplored. To understand when agents scheme, we decompose scheming incentives into agent factors and environmental factors. We develop realistic settings allowing us to systematically vary these factors, each with scheming opportunities for agents that pursue instrumentally convergent goals such as self-preservation, resource acquisition, and goal-guarding. We find only minimal instances of scheming despite high environmental incentives, and show this is unlikely due to evaluation awareness. While inserting adversarially-designed prompt snippets that encourage agency and goal-directedness into an agent's system prompt can induce high scheming rates, snippets used in real agent scaffolds rarely do. Surprisingly, in model organisms (Hubinger et al., 2023) built with these snippets, scheming behavior is remarkably brittle: removing a single tool can drop the scheming rate from 59% to 3%, and increasing oversight can raise rather than deter scheming by up to 25%. Our incentive decomposition enables systematic measurement of scheming propensity in settings relevant for deployment, which is necessary as agents are entrusted with increasingly consequential tasks.
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