arXiv AI July 3, 2026 By Qianyu Chen, Canran Xiao, Runxuan Tang

Hidden Forgetting in Continual Multimodal Learning: When Accuracy Survives but Grounding Fails

arXiv:2607. 02020v1 Announce Type: new Abstract: Multimodal large language models must continually adapt to evolving tasks and domains, yet standard continual learning metrics mainly measure whether old answers remain correct, leaving the stability of multimodal grounding largely unexamined.

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

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RL Forgets! Towards Continual Policy Optimization

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Regularizing modality contribution drift in multimodal continual learning

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CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning

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By Yang Liu, Toan Nguyen, Flora D. Salim

llms fine-tuning multimodal benchmarks safety

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Rethinking Transfer in Continual Learning: A Replay-Based Realisation

arXiv:2607. 15587v1 Announce Type: new Abstract: Continual learning studies how deployed language models can continually acquire new tasks without expensive retraining from scratch.

By Yang Meng, Zhenya Liu, Zhuokai Zhao, Yuxin Chen

llms efficiency

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When to Review: Spaced Repetition for Continual Pre-Training of Language Models

arXiv:2608. 17530v1 Announce Type: new Abstract: Continual pre-training of large language models must acquire new information without erasing old knowledge.

By Alankar Atreya, Devesh Batra, Yoages Kumar Mantri, Geremy Bantug, Greig A Cowan, Raad Khraishi

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