Instruction-Guided Cross-Modal Clustering for Training-Free Visual Token Pruning in Vision-Language Models

Large vision-language models (LVLMs) have demonstrated remarkable capabilities in understanding multimodal data such as images and text. However, the number of visual tokens in these models often far exceeds that of textual tokens, resulting in substantial redundancy and high inference costs. Existing pruning methods primarily rely on either unimodal information or cross-modal attention mechanisms. The former often overlooks the semantic alignment between instructions and visual representations in the multimodal space, while the latter is prone to attention drift and dispersion, leading to significant performance degradation under high pruning ratios. All the above issues stem from the lack of effective textual guidance during the pruning process. To identify effective informational cues for guiding pruning, we conduct an in-depth analysis of the interaction between language instructions and visual features based on the cross-modal information bottleneck attribution (CIBA) method, revealing the presence of noun anchors. Based on this analysis, we propose the Instruction-Guided Cross-Modal Clustering Token Pruning (ICCTP) method, a plug-and-play, training-free pruning paradigm. Specifically, ICCTP first leverages global attention to retain a small set of visual tokens that preserve global context. It then extracts nouns from the instruction as clustering centers to perform cross-modal clustering over the remaining visual tokens. To balance semantic diversity and global relevance while reducing intra-cluster redundancy, we design an importance scoring mechanism. Finally, visual tokens within each cluster are pruned according to a specified pruning ratio. We evaluate ICCTP on multiple VLM architectures, including LLaVA-1.5-7B, LLaVA-1.5-13B, and LLaVA-NeXT-7B. Experimental results show that ICCTP maintains strong performance across various pruning rates without requiring retraining. Notably, even under an extreme setting where 94.4 % of visual tokens are removed, ICCTP retains 90.02% of the original accuracy while reducing TFLOPs by 82.36%.