Forgotten in Weights, Recovered by Tools: Agentic Tool Unlearning for LLM Agents
Experiments on RWKU and MUSE across different LLM architectures show that ATU achieves a better balance between target forgetting and retained utility, making unlearning more robust under tool-augmented agent deployment.