41 citations · 70 across the 35 of their papers we have counts for
7 papers · 1 filter
Failure-Informed Image Self-Augmentation for Multimodal Large Language Model Self-Improvement
Chunyang Jiang, Pingping Zhang, Yuzhi Zhao +9
Multimodal large language models (MLLMs) have achieved remarkable performance across vision-language tasks, but their progress depends heavily on large-scale, high-quality multimod…
Inference-Time Budget Control for LLM Search Agents
Zhengru Fang, Senkang Forest Hu, Zhonghao Chang +6
LLM search agents increasingly rely on tools at inference time, but their trajectories are often constrained by hard limits on both tool calls and generated tokens. Under such dual…
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…
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…
Channel-Aware Throughput Maximization for Cooperative Data Fusion in CAV
Haonan An, Zhengru Fang, Yuang Zhang +4
Connected and autonomous vehicles (CAVs) have garnered significant attention due to their extended perception range and enhanced sensing coverage. To address challenges such as bli…
AgentsCoDriver: Large Language Model Empowered Collaborative Driving with Lifelong Learning
Senkang Hu, Zhengru Fang, Zihan Fang +3
Connected and autonomous driving is developing rapidly in recent years. However, current autonomous driving systems, which are primarily based on data-driven approaches, exhibit de…
Adaptive Communications in Collaborative Perception with Domain Alignment for Autonomous Driving
Senkang Hu, Zhengru Fang, Haonan An +4
Collaborative perception among multiple connected and autonomous vehicles can greatly enhance perceptive capabilities by allowing vehicles to exchange supplementary information via…