Sink-Token-Aware Pruning for Fine-Grained Video Understanding in Efficient Video LLMs
Video Large Language Models (Video LLMs) incur high inference latency due to a large number of visual tokens provided to LLMs. To address this, training-free visual token pruning has emerged as a solution to reduce computational costs; however, existing methods are primarily validated on Multiple-Choice Question Answering (MCQA) benchmarks, where coarse-grained cues often suffice. In this work, we reveal that these methods suffer a sharp performance collapse on fine-grained understanding tasks requiring precise visual grounding, such as hallucination evaluation. To explore this gap, we conduct a systematic analysis and identify sink tokens--semantically uninformative tokens that attract e...
| Authors | Kibum Kim, Jiwan Kim, Kyle Min et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 19 June 2026 |
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