Bingxu Han — accepted AI-conference papers

2026

Global-Local Confidence Fusion for Hallucination Detection in Mathematical Reasoning Task

Bo Zhang, Cong Gao, Linkang Yang, Bingxu Han, Minghao Hu, Zhunchen Luo +5

AAAI 2026technical

Large Reasoning Models (LRMs) achieve promising results on complex reasoning tasks but remain susceptible to hallucinations. Existing hallucination detection methods based on Large Language Models (LLMs) often focus solely on final answers, overlooking inconsistencies between the answer and reasonin

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2025

SafeConf: A Confidence-Calibrated Safety Self-Evaluation Method for Large Language Models

Bo Zhang, Cong Gao, Linkang Yang, Bingxu Han, Minghao Hu, Zhunchen Luo +5

EMNLP 2025

Large language models (LLMs) have achieved groundbreaking progress in Natural Language Processing (NLP). Despite the numerous advantages of LLMs, they also pose significant safety risks. Self-evaluation mechanisms have gained increasing attention as a key safeguard to ensure safe and controllable co

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