Li Zheng

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.

Current multimodal large language models struggle with dynamic stance understanding, particularly when relational inference is required, revealing critical gaps in their reasoning capabilities.