AI 大模型

大模型对齐与安全

大模型对齐、安全、越狱、红队、提示注入和可信评测。

2025-10-14 至 2025-10-14 共收录 7 篇信号源：cs.CL, cs.AI, cs.CY, cs.LG

1. 幻觉与事实性 7 篇

2507.09279 2025-10-14 cs.CV cs.AI cs.CL 73%

Prompt4Trust: A Reinforcement Learning Prompt Augmentation Framework for Clinically-Aligned Confidence Calibration in Multimodal Large Language Models

Anita Kriz, Elizabeth Laura Janes, Xing Shen, Tal Arbel

机构 * McGill University（麦吉尔大学）； Mila – Quebec AI Institute（魁北克AI研究所）

专题命中幻觉与事实性：safety(abstract)；trustworthy(abstract)；分类 cs.CL、cs.AI

Comments Accepted to ICCV 2025 Workshop CVAMD

2510.11457 2025-10-14 cs.AI 57%

From <Answer> to <Think>: Multidimensional Supervision of Reasoning Process for LLM Optimization

Beining Wang, Weihang Su, Hongtao Tian, Tao Yang, Yujia Zhou, Ting Yao, Qingyao Ai, Yiqun Liu

机构 * Tsinghua University（清华大学）； Tencent Inc（腾讯公司）

专题命中幻觉与事实性：alignment(abstract)；分类 cs.AI

2510.11242 2025-10-14 astro-ph.EP astro-ph.IM cs.LG 57%

Analyzing Data Quality and Decay in Mega-Constellations: A Physics-Informed Machine Learning Approach

Katarina Dyreby, Francisco Caldas, Cláudia Soares

机构 * FCT-UNL, Portugal（葡萄牙FCT-UNL）

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments 76th International Astronautical Congress

2510.10462 2025-10-14 cs.CV cs.AI 57%

Learning from Disagreement: A Group Decision Simulation Framework for Robust Medical Image Segmentation

Chen Zhong, Yuxuan Yang, Xinyue Zhang, Ruohan Ma, Yong Guo, Gang Li, Jupeng Li

机构 * School of Electronics and Information Engineering, Beijing Jiaotong University, China（电子信息工程学院，北京交通大学）； Peking University School and Hospital of Stomatology, China（北京大学口腔医院）

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.AI

2509.17178 2025-10-14 cs.CL 57%

Attention Consistency for LLMs Explanation

Tian Lan, Jinyuan Xu, Xue He, Jenq-Neng Hwang, Lei Li

机构 * Milkuya Studio（Milkuya工作室）； ERTIM, INALCO（ERTIM与INALCO）； Sorbonne University（索邦大学）； IRD（国家农业与食品研究所）； University of Washington（华盛顿大学）； VitaSight

专题命中幻觉与事实性：trustworthy(abstract)；分类 cs.CL

2510.10290 2025-10-14 cs.SE cs.LG 57%

Grounded AI for Code Review: Resource-Efficient Large-Model Serving in Enterprise Pipelines

Sayan Mandal, Hua Jiang

专题命中幻觉与事实性：safety(abstract)；分类 cs.LG

Comments Submitted to MLSys 2026

2510.09913 2025-10-14 cs.CL 57%

Don't Throw Away Your Pretrained Model

Shangbin Feng, Wenhao Yu, Yike Wang, Hongming Zhang, Yulia Tsvetkov, Dong Yu

机构 * University of Washington（华盛顿大学）； Tencent AI Seattle Lab（腾讯AI西雅图实验室）

专题命中幻觉与事实性：alignment(abstract)；分类 cs.CL