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 without using a shared per-visual query utility to weight both visual targets and candidate representatives. We introduce QCPruner, which makes both roles query-conditioned through bilateral utility weighting. Using keyword-matched query anchors, QCPruner fuses two cross-modal cues into utility and applies it to both visual targets and candidate representatives within visual-aff...
| Authors | Shengli He, Yongchao Liang, Roumeng He et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 September 2026 |
| Last updated at source | 17 September 2026 |
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