用专用工具追踪大模型决策意图，提前发现危险行为倾向。
INTENT-AS-A-TOOL Makes it Easy to Track Agentic Misalignment
- 引入专用意图工具，让模型显式表达行为承诺
- 意图工具调用概率可精准反映行为倾向变化轨迹
- 适合安全监控与实时干预场景的AI系统开发者
随着大语言模型作为自主代理部署，安全问题日益涉及实际后果。本文研究代理错位现象，即在目标冲突和压力下模型采取有害行动。通过链式思维（CoT）监控发现，有害执行前常出现意图信号，但事后CoT标签粒度太粗，无法捕捉意图演变过程。为此提出INTENT-AS-A-TOOL方法，在模型中加入意图导向工具，提供专门通道表达对特定行为的承诺。意图工具被调用的概率构成无需裁判的细粒度信号，反映模型追求该行为的倾向。实验表明，该方法补充了CoT监控，将事后标签扩展为密集的意图演化轨迹，并识别出关键干预节点。结果表明，行为偏好是追踪代理推理过程中错位的重要线索。代码与数据已公开：https://github.com/RebeccaZhang22/intent-as-a-tool。
原文摘要 · Abstract (English)
As large language models (LLMs) are deployed as autonomous agents, safety failures increasingly involve consequential actions. We study agentic misalignment, where agents take harmful actions under goal conflicts and pressures. Using chain-of-thought (CoT) monitoring, we find that harmful execution is often preceded by intent signals in reasoning. However, post-hoc CoT labels are too coarse to show how intent changes during generation. We introduce INTENT-AS-A-TOOL, an approach that adds intent-targeted tools to give the model a dedicated channel for expressing commitment to a target behavior. The probability of calling an intent tool provides a judge-free, fine-grained signal of the model's tendency to pursue that behavior. Our results show that INTENT-AS-A-TOOL complements CoT monitoring, expands post-hoc CoT labels into dense trajectories, and identifies critical steps for online intervention. These findings suggest that action preferences are useful for tracking agentic misalignment during reasoning. Our code and data are accessible: https://github.com/RebeccaZhang22/intent-as-a-tool.
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