From Token Importance to Conditional Removability: Rethinking Visual Token Pruning in Multimodal Large Language Models
cs.CV
Submitted: 2026-09-22
Updated: 2026-09-22
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
- Qwen2.5-VL Technical Report
- InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
- SparseVLM: Visual Token Sparsification for Efficient Vision-Language Model Inference
- QCPruner: Query-Conditioned Population Coverage for Visual Token Pruning
- Similarity-Aware Token Pruning: Your VLM but Faster
- PyramidDrop: Accelerating Your Large Vision-Language Models via Pyramid Visual Redundancy Reduction
- CAOTE: KV Cache Selection for LLMs via Attention Output Error-Based Token Eviction
- CriticalKV: Optimizing KV Cache Eviction from an Output Perturbation Perspective
- MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities
- Prune Redundancy, Preserve Essence: Vision Token Compression in VLMs via Synergistic Importance-Diversity
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