Forgotten in Weights, Recovered by Tools: Agentic Tool Unlearning for LLM Agents

2026-08-21

AI Summary

本文针对大语言模型在工具增强环境下的遗忘问题，提出了一种两阶段框架Agentic Tool Unlearning (ATU)，通过参数级知识遗忘和轨迹级强化学习减少目标记忆恢复。

Abstract

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 Unlearning (ATU), a two-stage framework. The first stage applies parametric knowledge unlearning to suppress direct recall, while the second stage performs trajectory-level reinforcement learning in simulated tool-augmented environments to penalize target-seeking tool behavior and final-answer leakage. 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.

Problem

• tool-mediated recovery
• agentic tool unlearning
• parametric recall

Innovation

• Agentic Tool Unlearning
• parametric knowledge unlearning
• trajectory-level reinforcement learning
• tool-mediated recovery