7 papers
LatCom: Cross-Agent Latent Compression for Efficient Multi-Agent Collaboration
Shinan Zhang, Tao Zhang, Qihui Zhu +7
LLM-based multi-agent systems (MAS) increasingly use latent collaboration to avoid the information loss and repeated encoding-decoding overhead of natural-language communication. H…
SCORAS-MoE: Joint Compression and Resource-Adaptive Deployment of MoE-VLMs in LEO Satellite Networks
Tong Quan, Yuanlong Wan, Huasen He +4
Deploying large vision-language models (VLMs) onboard satellites enables onboard data processing and reduces raw data downlink. However, onboard inference faces two resource challe…
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…
Dynamic Mask Enhanced Intelligent Multi-UAV Deployment for Urban Vehicular Networks
Gaoxiang Cao, Wenke Yuan, Yunpeng Hou +3
Vehicular Ad Hoc Networks (VANETs) play a crucial role in realizing vehicle-road collaboration and intelligent transportation. However, urban VANETs often face challenges such as f…
Bridging Network Fragmentation: A Semantic-Augmented DRL Framework for UAV-aided VANETs
Gaoxiang Cao, Wenke Yuan, Huasen He +4
Urban Vehicular Ad-Hoc Networks (VANETs) can become fragmented because buildings obstruct wireless links and vehicle mobility continuously changes the network topology. Unmanned Ae…
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…