SOURCE-LINKED INTELLIGENCE

Shallow to Deep: Aligning Token Pruning with Stage-wise Roles in LVLMs

arXiv · AI, language, vision and robotics · article · Sep 22, 2026 · UTC

Large Vision-Language Models (LVLMs) incur high computational costs from redundant visual tokens. Although training-free attention-based multi-layer pruning in the vision encoder stage has been explored as an effective strategy, we find that pruning in shallow layers consistently degrades performance. In this paper, we aim to understand this problem and seek a solution. By analyzing attention patterns across network depth, we find that shallow layers primarily function as edge detectors with chaotic attention maps, while deeper layers transition through local subject recognition and unstable s

Read original source ↗ Open in workspace

recordType

paper

region

Global