collaborators

8 papers

cs.RO2026

Multi-Agent Embodied Autonomous Driving (MAEAD): From V2X Information Exchange to Shared World Models

Senkang Hu, Zhengru Fang, Yihang Tao +4

Autonomous driving is shifting from isolated vehicle intelligence toward multi-agent embodied systems that share perception, infer intent, and coordinate action under uncertainty.…

cs.CV2026

V2VCrafter: Consistent Street-View Image Generation Across Vehicles

Yihang Tao, Yu Guo, Senkang Hu +4

Connected and autonomous driving (CAD) systems leverage vehicle-to-vehicle (V2V) communication for multi-agent collaborative perception, yet remain constrained by scarce annotated…

cs.NI2026

Collaborative Air-Ground Sensing, Communication, Computing, Storage, and Intelligence for Low-Altitude Economy

Yiqin Deng, Junhui Gao, Zihan Fang +3

Low-altitude economy (LAE) is transforming low-altitude airspace into a new cyber-physical infrastructure. Although air-ground communications have been widely studied, LAE is funda…

cs.NI2026

CA3D: Computing Accessibility-Aware Cooperative 3D Deployment of Multiple UAVs

Yiqin Deng, Zihan Fang, Yijie Wang +4

This letter investigates computing-accessibility-aware cooperative 3D deployment of multiple UAVs for task completion enhancement, termed CA3D. We first provide a theoretical analy…

cs.LG2026

Aggregation Alignment for Federated Learning with Mixture-of-Experts under Data Heterogeneity

Zihan Fang, Qianru Wang, Haonan An +4

Large language models (LLMs) increasingly adopt Mixture-of-Experts (MoE) architectures to scale model capacity while reducing computation. Fine-tuning these MoE-based LLMs often re…

cs.CL2026

Distribution-Aligned Decoding for Efficient LLM Task Adaptation

Senkang Hu, Xudong Han, Jinqi Jiang +5

Adapting billion-parameter language models to a downstream task is still costly, even with parameter-efficient fine-tuning (PEFT). We re-cast task adaptation as output-distribution…