arXiv AI July 10, 2026 By Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu, Vaidehi Patil

Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks

arXiv:2607. 07907v1 Announce Type: cross Abstract: With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associations that originate from their training data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 2

Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning

arXiv:2505. 19614v2 Announce Type: replace Abstract: Multimodal learning has seen remarkable progress, particularly with large-scale pre-training across various modalities.

By Sanghyuk Chun, Olga Russakovsky

multimodal benchmarks safety

arXiv Machine Learning

Jul 22

Stochastic Meta-Unlearning: Bridging Language Backbone and Multimodal Unlearning

arXiv:2607. 18615v1 Announce Type: cross Abstract: Machine unlearning for vision-language models (VLMs) remains underexplored.

By Zijie Liu, Jinhao Duan, Gaowen Liu, Sijia Liu, Tianlong Chen

llms multimodal

arXiv AI

Aug 5

Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation

arXiv:2608. 03791v1 Announce Type: new Abstract: Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora.

By Chunlin Liu, Junnian Chen, Haitong Jiang, Jianyu Zhao, Yingsen Pang, Jingchen Li, Jiabiao He, Youming Lu, Jinhe Bi, Yuntao Du

llms multimodal benchmarks

Hugging Face Trending Papers

Jul 21

Stochastic Meta-Unlearning: Bridging Language Backbone and Multimodal Unlearning

Machine unlearning for vision-language models (VLMs) remains underexplored. Unlike language models, VLMs combine a language backbone with visual components, which makes unlearning more complex.

llms multimodal

arXiv AI

Jun 29

Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning

arXiv:2511. 20196v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can inadvertently memorize privacy-sensitive information during training.

By Zhen Zeng, Leijiang Gu, Zhangling Duan, Feng Li, Cees G. M. Snoek, Meng Wang, Zenglin Shi

llms fine-tuning multimodal benchmarks safety

arXiv AI

Jun 12

MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs

arXiv:2606. 12809v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are trained on massive multimodal data, making data unlearning increasingly important as data owners may request the removal of specific content.

By He Li, Haoang Chi, Qizhou Wang, Yunxin Mao, Zhiheng Zhang, Jie Tan, Tongliang Liu, Wenjing Yang, Bo Han

llms multimodal benchmarks safety

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Multiplicity is an Inevitable and Inherent Challenge in Multimodal Learning

Stochastic Meta-Unlearning: Bridging Language Backbone and Multimodal Unlearning

Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation

Stochastic Meta-Unlearning: Bridging Language Backbone and Multimodal Unlearning

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MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs