Large language models (LLMs) demonstrate superior reasoning capabilities compared to small language models (SLMs), but incur substantially higher costs. We propose COllaborative REAsoner (COREA), a system that cascades an SLM with an LLM to achieve a balance between accuracy and cost in complex reasoning tasks. COREA first attempts to answer questions using the SLM, which outputs both an answer and a verbalized confidence score. Questions with confidence below a predefined threshold are deferred to the LLM for more accurate resolution. We introduce a reinforcement learning-based training algorithm that aligns the SLM's confidence through an additional confidence calibration reward. Extensive experiments demonstrate that our method jointly improves the SLM's reasoning ability and confidence calibration across diverse datasets and model backbones. Compared to using the LLM alone, COREA reduces cost by 21.5% and 16.8% on out-of-domain math and non-math datasets, respectively, with only an absolute pass@1 drop within 2%.
Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
Institute for Artificial Intelligence（人工智能研究院）；Beijing National Research Center for Information Science and Technology（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）
Calibrated? Not for Everyone: How Sexual Orientation and Religious Markers Distort LLM Accuracy and Confidence in Medical QA
Department of Medical Informatics, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands（阿姆斯特丹大学医学信息学系，阿姆斯特丹大学医学中心，阿姆斯特丹，荷兰）；Amsterdam Public Health, Methodology, Amsterdam, The Netherlands（阿姆斯特丹公共卫生，方法学，阿姆斯特丹，荷兰）
Double-Calibration: Towards Reliable LLMs via Calibrating Knowledge and Reasoning Confidence
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China（中山大学计算机科学与工程学院，广州，中国）；Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR（香港理工大学计算机系，香港特别行政区）；School of Science and Technology, Hong Kong Metropolitan University, Hong Kong SAR（香港 Metropolitan 大学科技学院，香港特别行政区）
C$^2$GSPG: Confidence-calibrated Group Sequence Policy Gradient towards Self-aware Reasoning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高陵人工智能学院）；Tsinghua University（清华大学）；Beijing Key Laboratory of Research on Large Models（北京大模型研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search（下一代智能搜索工程研究中心）
CURE: Confidence-driven Unified Reasoning Ensemble Framework for Medical Question Answering
School of Information Technology and Computer Science, Nile University（信息科技与计算机科学学院，尼罗大学）；Graduate School of Information Science, University of Hyogo（信息科学研究生院，播磨大学）；Advanced Medical Engineering Research Institute, University of Hyogo（先进医疗工程研究所，播磨大学）
Uncertainty-Aware Calibrated Clinical Text Classification with Large Language Models
Faculty of Dental Medicine and Oral Health Sciences, McGill University（牙医学院与口腔健康科学学院，麦吉尔大学）；McGill University（麦吉尔大学）；Mila–Quebec Artificial Intelligence Institute（魁北克人工智能研究所）；School of Computer Science, McGill University（计算机科学学院，麦吉尔大学）
Towards Calibrated Robust Fine-Tuning of Vision-Language Models
University of Wisconsin--Madison（威斯康星大学麦迪逊分校）；KAIST AI（韩国科学技术院人工智能部门）；Chung-Ang University（中央大学）；NAVER AI Lab（NAVER人工智能实验室）；Carnegie Mellon University（卡内基梅隆大学）；Yonsei University（延世大学）
Calibrated Confidence Expression for Radiology Report Generation
Computer Aided Medical Procedures, Technical University of Munich, Germany（德国慕尼黑工业大学计算机辅助医疗程序）；Munich Center for Machine Learning (MCML), Germany（德国慕尼黑机器学习中心）；Department of Diagnostic and Interventional Radiology, TUM Klinikum rechts der Isar, Germany（德国慕尼黑工业大学伊萨尔河右岸医院诊断与介入放射科）；Department of Diagnostic and Interventional Neuroradiology, TUM Klinikum rechts der Isar, Germany（德国慕尼黑工业大学伊萨尔河右岸医院诊断与介入神经放射科）；Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Germany（德国汉堡-埃彭多夫大学医学中心诊断与介入放射学及核医学科）；AI for Image-Guided Diagnosis and Therapy, Technical University of Munich, Germany（德国慕尼黑工业大学人工智能图像引导诊断与治疗）
