INTENT-AS-A-TOOL Makes it Easy to Track Agentic Misalignment

2026-08-27

AI Summary

研究针对自主代理的有害行为问题，提出INTENT-AS-A-TOOL方法，通过添加意图工具来监测模型的行为倾向，以实现对代理错位的追踪。

Abstract

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.

Problem

• agentic misalignment
• intent signals
• chain-of-thought monitoring

Innovation

• INTENT-AS-A-TOOL
• chain-of-thought monitoring
• agentic misalignment
• intent-targeted tools