Forgotten in Weights, Recovered by Tools: Agentic Tool Unlearning for LLM Agents
Large language models (LLMs) are increasingly deployed as tool-augmented agents, where responses can depend on tool calls and external observations rather than model parameters alone. This creates an evaluation mismatch for LLM unlearning: previous unlearning methods may suppress direct parametric recall, but an agent can still recover the same forget target through tools such as web search, retrieval, or database lookup. We identify this failure mode as tool-mediated recovery and study agentic tool unlearning, which aims to reduce both parametric recall and tool-mediated recovery while preserving normal tool use for retained knowledge. To address this challenge, we propose Agentic Tool U...
| Authors | Baicheng Chen, Zheyuan Liu, Jingyu Zhang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 21 August 2026 |
| Last updated at source | 21 August 2026 |
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