As large language models (LLMs) are increasingly deployed in critical decision-making systems, the lack of reliable methods to measure their uncertainty presents a fundamental trustworthiness risk. We introduce a normalized confidence score based on output anchor token probabilities: classification labels for structured tasks and self-evaluation responses (Yes/No) for open-ended generation. This enables direct detection of errors and hallucinations with minimal overhead and without external validation. We make three key contributions. First, we propose a normalized confidence score and self-evaluation framework that exposes reliable confidence estimates for error detection across seven diverse benchmark tasks and five LLMs of varying architectures and sizes. Second, our theoretical analysis reveals that supervised fine-tuning (SFT) yields well-calibrated confidence through maximum-likelihood estimation, whereas reinforcement learning methods (PPO, GRPO) and DPO induce overconfidence via reward exploitation. Third, we propose post-RL SFT with self-distillation to restore confidence reliability in RL-trained models. Empirical results demonstrated that SFT improved average confidence-correctness AUROC from 0.806 to 0.879 and reduced calibration error from 0.163 to 0.034 on Qwen3-4B, while GRPO and DPO degraded confidence reliability. We demonstrated practical value through adaptive retrieval-augmented generation (RAG) that selectively retrieves context when the model lacks confidence, using only 58\% of retrieval operations to recover 95\% of the maximum achievable accuracy gain on TriviaQA
Stable but Wrong: When Learning Stabilizes Away from the Truth
China Mobile Research Institute（中国移动研究院）；China Mobile GBA (Greater Bay Area) Innovation Institute（中国移动广州湾创新院）；Institute of Microelectronics, Chinese Academy of Sciences（中国科学院微电子研究所）
R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China（上海可信计算实验室，华东师范大学，上海，中国）；Huazhong University of Science and Technology（华中科技大学）；Nanyang Technological University（南洋理工大学）
Reason-KE++: Aligning the Process, Not Just the Outcome, for Faithful LLM Knowledge Editing
Shanghai Jiao Tong University（上海交通大学）；The University of Sydney（悉尼大学）；Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区）；Nanyang Technological University（南洋理工大学）
Aligning Language Models with Selective Prediction
Department of Computer Science and Engineering（计算机科学与工程系）；Bioinformatics and Computational Biology Program（生物信息学与计算生物学项目）；Division of Biostatistics and Health Data Science（生物统计学与健康数据科学部）；University of Minnesota Twin Cities（明尼苏达大学双城分校）
Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, USA（加州大学圣地亚哥分校计算机科学与工程系，拉贾尔，加利福尼亚州，美国）；Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, California, USA（加州大学圣地亚哥分校Halıcıoğlu数据科学研究所，拉贾尔，加利福尼亚州，美国）；Department of Statistics, Stanford University, Stanford, California, USA（斯坦福大学统计学系，斯坦福，加利福尼亚州，美国）
Same-Number Citation Swaps: Stress-Testing Jev as a Financial Evidence Judge
Sofia University（索菲亚大学）；Indiana University（印第安纳大学）；University of California, San Diego（加州大学圣迭戈分校）；Northeastern University（东北大学）；Michigan State University（密歇根州立大学）
Fact-Level Confidence Calibration and Self-Correction
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Penn State University（宾夕法尼亚州立大学）
Empathy by Design: Aligning Large Language Models for Healthcare Dialogue
Department of Public Health Sciences, The University of Texas at El Paso, USA（公共卫生科学系，德克萨斯理工大学埃尔帕索分校）；Department of Nursing, The University of Texas at El Paso, USA（护理系，德克萨斯理工大学埃尔帕索分校）；Department of Rehabilitation Sciences, The University of Texas at El Paso, USA（康复科学系，德克萨斯理工大学埃尔帕索分校）
Shared Experience, Separate Learning: Companion Confidence Calibration for LLMs
State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Optimizing Calibration by Gaining Aware of Prediction Correctness
The Australian National University（澳大利亚国立大学）；Data61, The Commonwealth Scientific and Industrial Research Organisation（联邦科学与工业研究组织Data61）；The Hong Kong Polytechnic University（香港理工大学）；Stanford University（斯坦福大学）
The University of Hong Kong（香港大学）；Tsinghua University（清华大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Virginia（弗吉尼亚大学）；Peking University（北京大学）；WeChat AI（微信人工智能）；The Chinese University of Hong Kong（香港中文大学）
