Extensive experiments on real-world EHR datasets across fidelity, privacy, and downstream utility evaluations demonstrate that SynEHR outperforms state-of-the-art models by generating more clinically coherent and temporally faithful longitudinal EHR data.
Xi-Miao Li, Lin Jiang, Rong-Chao Xu et al.· 0 citations
Evaluations across diverse in-distribution and out-of-distribution benchmarks show that ChainUQ consistently improves response-level uncertainty estimation, and can be directly transferred to new settings without additional fine-tuning.
Da-Hai Yu, Rong-Chao Xu, Lin Jiang et al.· 0 citations
EviRec is proposed, a continual evidence-learning framework that estimates how much historical evidence should be trusted separately for each candidate POI, and consistently outperforms state-of-the-art baselines, with the largest gains concentrated on cold-start queries.
SymboUQ is a symbolic uncertainty quantification framework that estimates final-answer reliability from reasoning traces by distinguishing symbolizability, whether a claim can be represented in the verifier's formal language, from semantic determinacy, whether its execution yields an entailed or contradicted verdict ra...
Da-Hai Yu, Lin Jiang, Rong-Chao Xu et al.· 0 citations
Large language models (LLMs) can generate fluent reasoning traces that nevertheless lead to incorrect answers, making response-level uncertainty estimation important for abstention, human review, and adaptive compute allocation. Existing approaches generally fall into three categories: passive single-trace methods use...
Da-Hai Yu, Lin Jiang, Rong-Chao Xu et al.· 0 citations
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