Author

Chen-Lu Ding

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#artificial intelligence Oct 2025

MLLMEraser: Achieving Test-Time Unlearning in Multimodal Large Language Models through Activation Steering

This work proposes MLLMEraser, an input-aware, training-free framework for test-time unlearning that consistently outperforms state-of-the-art MLLM unlearning baselines, achieving stronger forgetting performance with lower computational cost and minimal utility degradation.

Chen-Lu Ding, Jian-Can Wu, Leheng Sheng et al. · arXiv.org · 11 citations

#artificial intelligence Preprint Oct 2026

CUSE: Fine-Grained Multimodal Unlearning with Counterfactual Guidance and On-Policy Self-Distillation

Multimodal large language models (MLLMs) can reproduce sensitive, copyrighted, or unsafe information from their training data. Effective unlearning must remove target information while preserving non-target content, even within the same response. Existing methods typically apply uniform forgetting objectives to complet...

Chen-Lu Ding, Jian-Can Wu, Yan-Chen Luo et al. · 2 citations