arXiv Machine Learning June 16, 2026 By Salimeh Sekeh, Mary Wisell

Understanding Cross-Modal Contributions in Continual Vision-Language Models: A Theoretical Perspective

Read the original on arXiv Machine Learning →

arXiv:2606. 14883v1 Announce Type: cross Abstract: Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new environments (tasks), it inherently emphasizes the contribution of previously learned environments (tasks) at the expense of the stability required to preserve previously acquired knowledge.

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

arXiv Machine Learning

Aug 12

Putting a Face to Forgetting: Continual Learning meets Mechanistic Interpretability

arXiv:2601. 22012v3 Announce Type: replace Abstract: Catastrophic forgetting in continual learning is often measured at the performance or last-layer representation level, overlooking the underlying mechanisms.

By Sergi Masip, Gido M. van de Ven, Javier Ferrando, Tinne Tuytelaars

llms computer-vision safety

arXiv Machine Learning

Jun 16

KeepLoRA++: Continual Learning with Layer-Scaled Residual Gradient Adaptation

arXiv:2606. 16256v1 Announce Type: cross Abstract: Continual learning for pre-trained vision-language models requires balancing three competing objectives: retaining pre-trained knowledge, preserving knowledge from a sequence of learned tasks, and maintaining the plasticity to acquire new knowledge.

By Mao-Lin Luo, Yi-Lin Zhang, Zi-Hao Zhou, Yankun Hong, Xialiang Tong, Mingxuan Yuan, Tong Wei, Min-Ling Zhang

llms computer-vision nlp fine-tuning multimodal

arXiv AI

Jun 3

PHASER: Phase-Aware and Semantic Experience Replay for Vision-Language-Action Models

arXiv:2606. 03598v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved remarkable success in language-conditioned robotic manipulation.

By Ziyang Chen, Shaoguang Wang, Weiyu Guo, Qianyi Cai, He Zhang, Pengteng Li, Yiren Zhao, Yandong Guo

agents robotics multimodal

arXiv Machine Learning

Jul 31

Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting

arXiv:2508. 04227v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs), spanning predictive architectures to generative Multimodal Large Language Models (MLLMs), have revolutionized artificial intelligence through powerful cross-modal alignment and zero-shot generalization.

By Yuyang Liu, Qiuhe Hong, Linlan Huang, Alexandra Gomez-Villa, Dipam Goswami, Tiantian Peng, Xialei Liu, Joost van de Weijer, Yonghong Tian

llms agents robotics multimodal benchmarks safety

arXiv Machine Learning

Jul 31

Regularizing modality contribution drift in multimodal continual learning

arXiv:2607. 27260v1 Announce Type: new Abstract: Multimodal continual learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving knowledge.

By Zhen Zhang, Jielei Chu, Bin Liu, Tianrui Li

nlp multimodal safety

arXiv AI

Aug 7

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

arXiv:2605. 20247v2 Announce Type: replace-cross Abstract: Catastrophic forgetting remains a major obstacle to continual learning in large language models (LLMs) and vision--language models (VLMs).

By Yang Liu, Toan Nguyen, Flora D. Salim

llms fine-tuning multimodal benchmarks safety

Related stories

Putting a Face to Forgetting: Continual Learning meets Mechanistic Interpretability

KeepLoRA++: Continual Learning with Layer-Scaled Residual Gradient Adaptation

PHASER: Phase-Aware and Semantic Experience Replay for Vision-Language-Action Models

Continual Learning for VLMs: A Survey and Taxonomy Beyond Forgetting

Regularizing modality contribution drift in multimodal continual learning

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