arXiv Machine Learning August 10, 2026 By Minseok Kang, Hyunwoo Kim, Chanyoung Kim, Minwoo Kim, Jaekoo Lee, Dahuin Jung

Prune Once: Retraining-Free Task-Agnostic Pruning for Vision-Language Models

Read the original on arXiv Machine Learning →

arXiv:2608. 06901v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved remarkable generalization across diverse multimodal tasks through large-scale pre-training, yet their rapidly increasing computational and memory requirements pose significant challenges for deployment in constrained environments.

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

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Stateful Token Reduction for Long-Video Hybrid VLMs

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