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%.

This paper explores the application of spiking neural networks (SNNs), known for their low-power binary spikes, to bearing fault diagnosis, bridging the gap between high-performance AI algorithms and real-world industrial scenarios.In particular, we identify two key limitations of existing SNN fault diagnosis methods: inadequate encoding capacity that necessitates cumbersome data preprocessing, and non-spike-oriented architectures that constrain the performance of SNNs.To alleviate these problems, we propose a Multi-scale Residual Attention SNN (MRA-SNN) to simultaneously improve the efficiency, performance, and robustness of SNN methods.By incorporating a lightweight attention mechanism, we have designed a multi-scale attention encoding module to extract multiscale fault features from vibration signals and encode them as spatio-temporal spikes, eliminating the need for complicated preprocessing.Then, the spike residual attention block extracts high-dimensional fault features and enhances the expressiveness of sparse spikes with the attention mechanism for end-to-end diagnosis.In addition, the performance and robustness of MRA-SNN is further enhanced by introducing the lightweight attention mechanism within the spiking neurons to simulate the biological dendritic filtering effect.Extensive experiments on MFPT, JNU, Bearing, and Gearbox benchmark datasets demonstrate that MRA-SNN significantly outperforms existing methods in terms of accuracy, energy