collaborators

5 papers

cs.AI2026

LEC: Linear Expectation Constraints for Selection-Conditioned Risk Control in Selective Prediction and Routing Systems

Zhiyuan Wang, Aniri, Tianlong Chen +4

Foundation models often generate unreliable answers, while heuristic uncertainty estimators fail to fully distinguish correct from incorrect outputs, causing users to accept errone…

eess.IV2025

Conformal Lesion Segmentation for 3D Medical Images

Binyu Tan, Zhiyuan Wang, Jinhao Duan +4

Medical image segmentation serves as a critical component of precision medicine, enabling accurate localization and delineation of pathological regions, such as lesions. However, e…

cs.CL2025

Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models

Qingni Wang, Tiantian Geng, Zhiyuan Wang +3

Multimodal Large Language Models (MLLMs) exhibit promising advancements across various tasks, yet they still encounter significant trustworthiness issues. Prior studies apply Split…

cs.CL2025

SConU: Selective Conformal Uncertainty in Large Language Models

Zhiyuan Wang, Qingni Wang, Yue Zhang +4

As large language models are increasingly utilized in real-world applications, guarantees of task-specific metrics are essential for their reliable deployment. Previous studies hav…

cs.CL2025

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees

Zhiyuan Wang, Jinhao Duan, Qingni Wang +4

Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approach…