4 citations · 9 across the 10 of their papers we have counts for
10 papers
Self-Induced Outcome Potential: Turn-Level Credit Assignment for Agents without Verifiers
Senkang Hu, Yong Dai, Xudong Han +4
Long-horizon LLM agents depend on intermediate information-gathering turns, yet training feedback is usually observed only at the final answer, because process-level rewards requir…
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…
Optimizing Agentic Reasoning with Retrieval via Synthetic Semantic Information Gain Reward
Senkang Hu, Yong Dai, Yuzhi Zhao +5
Agentic reasoning enables large reasoning models (LRMs) to dynamically acquire external knowledge, but yet optimizing the retrieval process remains challenging due to the lack of d…
DSP-Reg: Domain-Sensitive Parameter Regularization for Robust Domain Generalization
Xudong Han, Senkang Hu, Yihang Tao +4
Domain Generalization (DG) is a critical area that focuses on developing models capable of performing well on data from unseen distributions, which is essential for real-world appl…
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…
CP-uniGuard: A Unified, Probability-Agnostic, and Adaptive Framework for Malicious Agent Detection and Defense in Multi-Agent Embodied Perception Systems
Senkang Hu, Yihang Tao, Guowen Xu +5
Collaborative Perception (CP) has been shown to be a promising technique for multi-agent autonomous driving and multi-agent robotic systems, where multiple agents share their perce…
CP-Guard+: A New Paradigm for Malicious Agent Detection and Defense in Collaborative Perception
Senkang Hu, Yihang Tao, Zihan Fang +4
Collaborative perception (CP) is a promising method for safe connected and autonomous driving, which enables multiple vehicles to share sensing information to enhance perception pe…
CP-Guard: Malicious Agent Detection and Defense in Collaborative Bird's Eye View Perception
Senkang Hu, Yihang Tao, Guowen Xu +4
Collaborative Perception (CP) has shown a promising technique for autonomous driving, where multiple connected and autonomous vehicles (CAVs) share their perception information to…
AgentsCoMerge: Large Language Model Empowered Collaborative Decision Making for Ramp Merging
Senkang Hu, Zhengru Fang, Zihan Fang +4
Ramp merging is one of the bottlenecks in traffic systems, which commonly cause traffic congestion, accidents, and severe carbon emissions. In order to address this essential issue…
Towards Full-scene Domain Generalization in Multi-agent Collaborative Bird's Eye View Segmentation for Connected and Autonomous Driving
Senkang Hu, Zhengru Fang, Yiqin Deng +3
Collaborative perception has recently gained significant attention in autonomous driving, improving perception quality by enabling the exchange of additional information among vehi…