1 citations · 1 across the 5 of their papers we have counts for
5 papers
GSTEP: Global Spatio-Temporal Density-Driven Visual Token Pruning for Efficient Video Large Language Models
Mengjie Zhang, Qihui Zhu, Tao Zhang +10
Video large language models (VideoLLMs) achieve strong video understanding performance, but their inference remains expensive due to the large number of redundant spatio-temporal v…
RP-OPSD: Resolution-Privileged On-Policy Self-Distillation for Multimodal Large Language Models
Qihui Zhu, Yuchen Wang, Zijian Wen +7
On-Policy Self-Distillation (OPSD) uses privileged information available only to the teacher to provide dense token-level supervision on trajectories generated by the student. Howe…
HardSecBench: Benchmarking the Security Awareness of LLMs for Hardware Code Generation
Qirui Chen, Jingxian Shuai, Shuangwu Chen +8
Large language models (LLMs) are increasingly used for hardware and firmware code generation, but existing studies primarily evaluate functional correctness while largely overlooki…
Rethinking Table Pruning in TableQA: From Sequential Revisions to Gold Trajectory-Supervised Parallel Search
Yu Guo, Shenghao Ye, Shuangwu Chen +8
Table Question Answering (TableQA) benefits significantly from table pruning, which extracts compact sub-tables by eliminating redundant cells to streamline downstream reasoning. H…
HAWK: Head Importance-Aware Visual Token Pruning in Multimodal Models
Qihui Zhu, Tao Zhang, Yuchen Wang +9
In multimodal large language models (MLLMs), the surge of visual tokens significantly increases the inference time and computational overhead, making them impractical for real-time…