Think Twice Before You Act: Enhancing Agent Behavioral Safety with Thought Correction

Understand

LLM-based agents solve complex tasks through iterative reasoning, tool use, and environment interaction, where each intermediate thought directly shapes subsequent actions.

• Small deviations in these thoughts can therefore propagate into unsafe behaviors, yet existing guardrails typically operate only on final outputs or require intrusive model modifications.
• We introduce Thought-Aligner, a lightweight plug-in safety model that performs causal correction on unsafe thoughts before action execution, without altering the underlying agent.
• The corrected thoughts are fed back into the agent, steering its decision process and tool use toward safer trajectories.