arXiv Computer Vision August 25, 2026 By Kevin Dave, Sai Aditya Patkuri, Chhaya Kumar Das, Gouranga Bala, Rajeshkumar SA, R. Venkatesh Babu

Adaptive Tokenisation Via Temporal Redundancy Masking And Latent Inpainting

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arXiv Computer Vision

Aug 24

Aggregating Visual Information with Optimal Transport for VideoLM Token Compression

arXiv:2608.20473v1 Announce Type: new Abstract: Video language models process videos as dense visual-token sequences with substantial representational redundancy. Compressing these sequences is there...

By Wenti Yin, Xiaotian Han, Junyuan Shang, Yuchen Ding, Shuohuan Wang, Dianhai Yu, Changxin Gao, Nong Sang

llms benchmarks

Hugging Face Trending Papers

Aug 3

CRAFT: Compression via Recursive Adaptive Fusion of Video Tokens for Vision-Language Models

In video understanding, vision-language models (VLMs) must ingest massive numbers of visual tokens, causing the computational and memory cost of the prefill stage to rise sharply. Such visual sequences are highly redundant along the spatio-temporal dimension, yet a high compression ratio is often accompanied by the loss of critical details.

llms multimodal benchmarks safety

arXiv AI

Oct 2

VETO: Video Efficient Token Optimization for Vision Language Models

VETO (Video Efficient Token Optimization for Vision Language Models) is a plug‑in that reduces the quadratic cost of visual tokens in long‑video inference by applying dual‑axis compression: an intra‑frame compressor merges semantically similar tokens within each frame, and an inter‑frame compressor merges temporally redundant frames. By first compressing spatial dimensions, VETO lowers the cost of subsequent global temporal matching, surpassing single‑axis methods and achieving up to 45% faster inference on models such as LLaVA‑OneVision‑7B while maintaining or improving accuracy. The approach is universally applicable across LLaVA‑OneVision, InternVL‑2.5, and LongVA, preserving or enhancing zero‑shot accuracy even under extreme token budgets.

By Gueter Josmy Faure, Hao Ping Wang, Min-Hung Chen, Winston H. Hsu

llms multimodal

arXiv AI

Jul 16

Fre-Res: Frequency-Residual Video Token Compression for Efficient Video MLLMs

arXiv:2605. 16366v2 Announce Type: replace-cross Abstract: Video MLLMs face a persistent tension between spatial fidelity and temporal coverage: preserving fine-grained visual details requires many spatial tokens, while capturing short-lived events requires dense temporal sampling.

By Yigui Feng (The College of Computer Science, National University of Defense Technology, Changsha, Hunan, China), Qinglin Wang (The College of Computer Science, National University of Defense Technology, Changsha, Hunan, China), Yang Liu (The Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, Guangdong, China), Jie Liu (The College of Computer Science, National University of Defense Technology, Changsha, Hunan, China)

rag diffusion benchmarks

arXiv Machine Learning

Sep 30

GleanVID: Complementary Token Selection for Efficient Video Large Language Models

arXiv:2609.37042v1 Announce Type: cross Abstract: Video Large Language Models (VideoLLMs) have achieved strong video understanding capabilities but incur substantial inference overhead due to the lar...

By Shuo Yang, Changbai Li, Rui Tang, Xinyu Zhao, Linlin Yang, Baochang Zhang

llms benchmarks

arXiv Computer Vision

Aug 26

Keep-or-Drop? Adaptive Tokenizer for Compact Video Representation

arXiv:2608.24293v1 Announce Type: new Abstract: Latent diffusion models have emerged as a dominant framework for high-fidelity image and video synthesis, operating in compact latent spaces with varia...

By Yeonkyeong Lee, Hyunsung Go, Jongmin Kim, Sewoong Lim, Donghoon Lee

llms diffusion nlp benchmarks