CRISP: Pre-LLM Yet Text-Driven Visual Token Pruning for Efficient LVLM Inference
Large Vision-Language Models (LVLMs) typically require processing hundreds to thousands of visual tokens, leading to substantial inference overhead. Existing visual token pruning methods either operate before the LLM using text-agnostic heuristics or prune inside the LLM at the cost of efficiency and noisy cross-modal attention. To address these limitations, we propose CRISP, a pre-LLM yet text-driven visual token pruning framework that preserves both instruction-relevant evidence and essential scene context. CRISP works in a two-stage pipeline: Stage 1 first identifies text-aligned visual tokens, and Stage 2 enhances contextual completeness through semantic diversity. Extensive experimen...
| Authors | Xu Li, Yi Zheng, Mengyang Zhao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 15 July 2026 |
| Last updated at source | 15 July 2026 |
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