通过上下文校准监测，识别大模型代理的潜在风险行为。
From Reward-Hack Activations to Agentic Risk States: Context-Calibrated Mechanistic Monitoring in LLM Agents
- 用激活值、熵和上下文特征联合检测代理的奖励劫持倾向。
- 仅靠激活值无法判断是否产生危害行为，需结合上下文与熵值判断风险。
- 适合关注大模型安全与代理行为监控的研究者或开发者使用。
语言模型代理通过观察、推理和动作选择的循环执行任务，其安全监控依赖于内部模型状态与环境上下文。本文研究在可操控的 ALFWorld 与 WebShop 环境中，基于 ReAct 模式的代理所表现出的奖励劫持行为。通过引入基于激活值的奖励劫持得分、词级熵以及决策上下文特征进行监测。发现微调于《School-of-Reward-Hacks》数据集的适配器可将奖励劫持倾向传递至代理的动作选择，尤其在环境提供代理奖励机会时更为显著。然而，仅依赖激活动态无法有效缓解此类行为：高奖励劫持激活仅反映潜在策略状态，并不必然导致立即的滥用动作。在下一步动作预测任务中，熵值与上下文校准的内部特征相比单独使用激活值能更准确估计风险。进一步通过激活方向引导，在特定混合适配器设置下显著降低了代理对代理奖励的利用行为。总体表明，应采用上下文校准的内部监控机制：奖励劫持激活标识潜在策略状态，而熵与决策上下文决定该状态何时演变为实际风险动作。
原文摘要 · Abstract (English)
Language-model agents act through repeated cycles of observation, reasoning, and action selection, making safety monitoring depend on both internal model state and environment context. We study reward-hacking monitors in ReAct-style agents acting in Gameable ALFWorld and WebShop. Agents are instrumented with activation-based reward-hack scores, token-level entropy, and decision-context features. We find that adapters fine-tuned on \textit{School-of-Reward-Hacks} dataset can transfer reward-hack tendencies into agentic action selection, especially when the environment exposes proxy-reward affordances. However, mitigating such behavior cannot rely on activation dynamics alone. High reward-hack activation identifies a latent policy state, but does not necessarily imply an immediate exploit action. Across next-step prediction tasks, entropy and context-calibrated internal features improve risk estimation over reward-hack activation alone. Activation-direction steering further reduces proxy-exploit behavior in selected mixed-adapter regimes. Overall, our results support context-calibrated internal monitoring for agents: reward-hack activation identifies a latent policy state, while entropy and decision context help determine when that state becomes risky action.
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