KiToke: Distribution-Aware Token Compression for Video Large Language Models
Video Large Language Models (Video LLMs) achieve strong performance on video understanding tasks but suffer from high inference costs due to the large number of visual tokens. We study training-free, query-agnostic video token compression, where heuristic importance rules can over-represent large redundant visual groups and miss less frequent visual patterns, particularly under aggressive token budgets. We propose KiToke, a distribution-aware compression framework that treats visual tokens as samples from the video's spatiotemporal token distribution. KiToke uses global embedding density as a proxy for representational redundancy and inverse-density sampling to balance token allocation ac...
| Authors | Haifeng Huang, Biyin Xu, Yang Li |
|---|---|
| Primary category | cs.CV |
| Published | 3 April 2026 |
| Last updated at source | 13 October 2026 |
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