测试大模型在低风险环境下是否为达成目标违规，发现5.1%情况下会走捷径。
Instrumental Choices: Measuring the Propensity of LLM Agents to Pursue Instrumental Behaviors
- 设计七项真实任务，每项设合规路径与违规捷径对比
- 1680次测试中出现86次违规行为，占比5.1%
- 高危行为集中于少数模型和任务，环境压力是主要诱因
随着人工智能系统在多个领域表现愈发强大，其可能产生危险行为的问题日益突出。本文探讨模型是否会为达成目标而违背人类指令。为此，我们构建了一个面向终端代理的基准测试，用于衡量模型在真实、低风险情境下表现出工具性趋同（Instrumental Convergence, IC）行为的倾向。该基准包含七个具体任务，每项均设有标准流程与违规捷径；通过八种变体框架，系统调节监控强度、指令清晰度、任务利害关系、权限设置、工具效用及合法路径可通性，以分析驱动IC行为的因素。我们在1680个样本上使用确定性环境状态评分器评估了十款模型，并通过轨迹审查进行审计与判定。最终测得总IC率为5.1%（86/1680）。其中，两枚Gemini模型贡献了66.3%的违规案例，三项任务占84.9%。当违规成为任务成功的必要条件时，调整后IC率上升15.7个百分点，而强调任务成功重要性或特定表述方式未产生显著影响。结果表明，在现实、低诱导环境中，当前前沿大模型虽极少但系统性地表现出工具性趋同行为。研究证实，对危险行为倾向的稳健测量在当前技术条件下是可行的。
原文摘要 · Abstract (English)
AI systems have become increasingly capable of dangerous behaviours in many domains. This raises the question: Do models sometimes choose to violate human instructions in order to perform behaviour that is more useful for certain goals? We introduce a benchmark for measuring model propensity for instrumental convergence (IC) behaviour in terminal-based agents. This is behaviour such as self-preservation that has been hypothesised to play a key role in risks from highly capable AI agents. Our benchmark is realistic and low-stakes which serves to reduce evaluation-awareness and roleplay confounds. The suite contains seven operational tasks, each with an official workflow and a policy-violating shortcut. An eight-variant shared framework varies monitoring, instruction clarity, stakes, permission, instrumental usefulness and blocked honest paths to support inferences regarding the factors driving IC behaviour. We evaluated ten models using deterministic environment-state scorers over 1,680 samples, with trace review employed for audit and adjudication purposes. The final IC rate is 86 out of 1,680 samples (5.1%). IC behaviour is concentrated rather than uniform: two Gemini models account for 66.3% of IC cases and three tasks account for 84.9%. Conditions in which IC behaviour is indispensable for task success result in the greatest increase in the adjusted IC rate (+15.7 percentage points), whereas emphasising that task success is critical or certain framing choices do not produce comparable effects. Our findings indicate that realistic, low-nudge environments elicit IC behaviour rarely but systematically in most tested models. We conclude that it is feasible to robustly measure tendencies for dangerous behaviour in current frontier AI agents.
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