1 citations · 1 across the 5 of their papers we have counts for
8 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…
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 Stepwise Model Routing: A Cost-Efficient Table Reasoning Perspective
Shenghao Ye, Yuxiang Wang, Yu Guo +3
Large Reasoning Models (LRMs) achieve strong performance on table reasoning tasks but incur substantial inference cost due to long reasoning traces. Stepwise model routing mitigate…
Rubric-Guided Process Reward for Stepwise Model Routing
Shenghao Ye, Yu Guo, Zhengheng Li +2
Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent methods formulate routing as a sequenti…
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…
When TableQA Meets Noise: A Dual Denoising Framework for Complex Questions and Large-scale Tables
Shenghao Ye, Yu Guo, Dong Jin +5
Table question answering (TableQA) is a fundamental task in natural language processing (NLP). The strong reasoning capabilities of large language models (LLMs) have brought signif…