作者位次 #4/5ICLR 2026Poster· Deep Learning / Generative Models and Autoencoders
Doctor-R1: Mastering Clinical Inquiry with Experiential Agentic Reinforcement Learning
Yung Hwei Lai · Kaiming Liu · Ziyue Wang · Weizhi Ma · Yang Liu
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Yung Hwei Lai · Kaiming Liu · Ziyue Wang · Weizhi Ma · Yang Liu
Jingyi Ren · Ante Wang · Yunghwei Lai · Xiaolong Wang · Linlu Gong · Weitao Li · Weizhi Ma · Yang Liu
Reliable Large Language Models (LLMs) should abstain when confidence is insufficient. However, prior studies often treat refusal as a generic "I don’t know”, failing to distinguish input-level ambiguity (data uncertainty) from capability limitations (model uncertainty). This lack of distinction l...
Weitao Li · Boran Xiang · Xiaolong Wang · Jingyi Ren · Ante Wang · Zhinan Gou · Weizhi Ma · Yang Liu
Large Language Models (LLMs) have shown strong capabilities through two complementary paradigms: Retrieval-Augmented Generation (RAG) for knowledge grounding and Reinforcement Learning from Verifiable Rewards (RLVR) for complex reasoning. However, existing attempts to unify these paradigms remain...
Junkai Li · Yunghwei Lai · Tianyi Zhu · Zheng Long Lee · Weizhi Ma · Yang Liu
Formulating a treatment plan is inherently a complex reasoning and refinement task rather than a simple generation problem. However, existing large language models (LLMs) mainly rely on one-shot output without explicit verification, which may result in rough, incomplete, and potentially unsafe tr...
Jiayin Wang · Xiaoyu Zhang · Weizhi Ma · Zhiqiang Guo · Min Zhang
Recommendation model interpretation aims to reveal the relationships between inputs, model internal representations, and outputs to enhance the transparency, interpretability, and trustworthiness of recommendation systems. However, the inherent complexity and opacity of deep learning models pose...
Yuanqing Yu · Yifan Wang · Weizhi Ma · Zhiqiang Guo · Min Zhang