2 papers
cs.CV2026
From Token Importance to Conditional Removability: Rethinking Visual Token Pruning in Multimodal Large Language Models
Shengli He, Yongchao Liang, Roumeng He +5
Training-free visual-token pruning often uses token importance, redundancy, or related selection criteria as proxies for safe removal. We show that these signals alone do not fully…
cs.CV2026
QCPruner: Query-Conditioned Population Coverage for Visual Token Pruning
Shengli He, Yongchao Liang, Roumeng He +4
The high visual-token load in multimodal large language models (MLLMs) motivates training-free pruning to reduce later-layer computation, but under a fixed budget, pruning must pre…