Author

Jun-Jie Zeng

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Preprint Sep 2026

From Token Importance to Conditional Removability: Rethinking Visual Token Pruning in Multimodal Large Language Models

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 characterize removability, which is conditioned on both representation depth and the surrounding deletion set. Controlled interventions dem...

Sheng-Li He, Yong-Chao Liang, Rou-Meng He et al. · 0 citations

Preprint Sep 2026

QCPruner: Query-Conditioned Population Coverage for Visual Token Pruning

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 preserve query-relevant evidence while avoiding redundancy. Existing methods rank tokens, diversify selected subsets, or optimize coverage with...

Sheng-Li He, Yong-Chao Liang, Rou-Meng He et al. · 1 citation · ⚡1