Preprint
Aug 2026
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.
Baicheng Chen, Zhe-Yuan Liu, Jingyu Zhang et al.
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