When Attention Collapses: Stage-Aware Visual Token Pruning from Structure to Semantics 事件

When Attention Collapses: Stage-Aware Visual Token Pruning from Structure to Semantics arXiv:2606.03569v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have demonstrated remarkable capabilities but suffer from significant computational overhead during inference. While visual token pruning offers a promising solution, existing methods predominantly rely on initial attention scores. This single-metric paradigm presents a critical flaw: high attention scores inherently collapse onto s