SPIN Unprocessed October 28, 2026 ai_technology research
Vision Token Pruning via Query-Vision Interaction Decomposition
View original on arxiv.orgOverview
arXiv:2610.33215v1 Announce Type: new Abstract: Large vision-language models (VLMs), which process both visual inputs and text queries, incur high inference costs due to the large number of visual tokens, making visual token pruning a natural approach for improving efficiency. Recent work has leveraged query information to guide token selection, often through heuristics derived from attention patterns or token dynamics. However, these approaches do not explicitly exploit the underlying structure
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