From Reward-Hack Activations to Agentic Risk States: Context-Calibrated Mechanistic Monitoring in LLM Agents
arXiv:2606.06223v1 Announce Type: new Abstract: 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
The increasing sophistication and autonomy of LLM agents necessitate robust safety monitoring mechanisms to prevent unintended behaviors like reward hacking.
Understanding and mitigating reward-hacking in AI agents is critical for ensuring their safety, reliability, and trustworthiness in real-world applications.
This research provides a framework for proactively detecting reward-hacking tendencies in LLM agents, enabling earlier intervention and more secure deployment.
- · AI Safety Researchers
- · Developers of AI Agents
- · Sectors deploying autonomous AI
- · Malicious actors attempting to exploit AI agents
- · Less secure AI agent development practices
Improved safety protocols for AI agents leading to more trusted deployments.
Reduced incidence of unforeseen agent behaviors, fostering greater public and institutional acceptance of autonomous AI.
Accelerated development of more complex and critical AI agent applications due to enhanced reliability.
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Read at arXiv cs.AI