Author

Can Wu

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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

#machine learning Preprint Sep 2026

Token Utility Is Selection-Conditioned: Coupled Selection of Prompt Context and Response Supervision for Efficient Instruction Tuning

Efficient large language model (LLM) instruction tuning requires selecting response supervision with supporting prompt context. Existing methods typically value both sides separately, risking selection-state mismatch between valuation and retained training subsets. BRIDGE (Budgeted Response-Prompt Interaction via Direc...

Can Wu, Xin-Rui Chen, Ou Wu et al. · 0 citations

Preprint Sep 2026

QCPruner: Query-Conditioned Population Coverage for Visual Token Pruning

QCPruner fuses two cross-modal cues into utility and applies it to both visual targets and candidate representatives within visual-affinity-based coverage, which achieves the highest average relative performance among evaluated complete-system pruning methods at every reported token budget.

Sheng-Li He, Yong-Chao Liang, Rou-Meng He et al. · 2 citations