5 papers
CAR: Query-Guided Confidence-Aware Reranking for Retrieval-Augmented Generation
Zhipeng Song, Yizhi Zhou, Xiangyu Kong +7
Retrieval-augmented generation (RAG) relies on evidence ranking to determine what information is exposed to the generator, yet existing retrieval and reranking methods primarily es…
Deep Reprogramming Distillation for Medical Foundation Models
Siyuan Du, Yuhang Zhou, Haolin Li +5
Medical foundation models pre-trained on large-scale datasets have shown powerful versatile performance. However, when adapting medical foundation models for specific medical scena…
Continual Task Learning through Adaptive Policy Self-Composition
Shengchao Hu, Yuhang Zhou, Ziqing Fan +4
Training a generalizable agent to continually learn a sequence of tasks from offline trajectories is a natural requirement for long-lived agents, yet remains a significant challeng…
Task-Aware Harmony Multi-Task Decision Transformer for Offline Reinforcement Learning
Ziqing Fan, Shengchao Hu, Yuhang Zhou +4
The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy applicable to diverse tasks without the need for online environmental interaction. Re…
LoRKD: Low-Rank Knowledge Decomposition for Medical Foundation Models
Haolin Li, Yuhang Zhou, Ziheng Zhao +5
The widespread adoption of large-scale pre-training techniques has significantly advanced the development of medical foundation models, enabling them to serve as versatile tools ac…