A diagnostic policy specifies what test to perform next, based on the results of previous tests, and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeoffs between (a) the cost of tests and (b) the cost of misdiagnoses. An optimal diagnostic policy minimizes the expected total cost. We formalize this diagnosis process as a Markov Decision Process (MDP). We investigate two types of algorithms for solving this MDP: systematic search based on AO* algorithm and greedy search (particularly the Value of Information method). We investigate the issue of learning the MDP probabilities from examples, but only as they are relevant to the search for good policies. We do not learn nor assume a Bayesian network for the diagnosis process. Regularizers are developed to control overfitting and speed up the search. This research is the first that integrates overfitting prevention into systematic search. The paper has two contributions: it discusses the factors that make systematic search feasible for diagnosis, and it shows experimentally, on benchmark data sets, that systematic search methods produce better diagnostic policies than greedy methods.
EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learning
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Tongyi Lab , Alibaba Group（通义实验室，阿里巴巴集团）；Alibaba Group（阿里巴巴集团）；SUAT（深圳大学）
OGAM: Connecting Systematic Testing to Runtime Assurance through Object-Grounded Attention Monitoring for VLA Policies
Carl von Ossietzky University of Oldenburg（卡尔·冯·奥西茨基大学奥尔登堡分校）；Huadian Electric Power Research Institute（华电电力科学研究院）；Chalmers University of Technology（查尔姆斯理工大学）
Code Evolution for Control: Synthesizing Policies via LLM-Driven Evolutionary Search
Shenzhen Yuyi Tech. Co. Ltd.（深圳优衣科技有限公司）；City University of Hong Kong（香港城市大学）；Shenzhen University（深圳大学）；University of Electronic Science and Technology of China（电子科技大学）
Systematic Review of Cybersecurity in Banking: Evolution from Pre-Industry 4.0 to Post-Industry 4.0 in Artificial Intelligence, Blockchain, Policies and Practice
Adversarial Fragility and Language Vulnerability in Clinical AI: A Systematic Audit of Diagnostic Collapse Under Imperceptible Perturbations and Cross-Lingual Drift in Low-Resource Healthcare Settings
Centre for Clinical Intelligence & Safety（临床智能与安全中心）；Tomorrow University of Applied Sciences（明天应用科学大学）
Brittleness and Promise: Knowledge Graph Based Reward Modeling for Diagnostic Reasoning
University of Colorado Boulder（科罗拉多大学博尔德分校）；University of Colorado Anschutz（科罗拉多大学安舒茨分校）；University of Wisconsin - Madison（威斯康星大学麦迪逊分校）；Loyola University Chicago（芝加哥洛约拉大学）
Diagnostic performance of deep learning for predicting glioma isocitrate dehydrogenase and 1p/19q co-deletion in MRI: a systematic review and meta-analysis
EEG-FM-Bench: A Comprehensive Benchmark for the Systematic Evaluation and Diagnostic Analyses of EEG Foundation Models
School of Computer Science and Technology, Tongji University, Shanghai, China（同济大学计算机科学与技术学院，上海，中国）；Translational Research Center, Shanghai Yangzhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), China（上海杨氏康复医院（上海阳光康复中心）转化研究中心，中国）
Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy
University of Leeds（利兹大学）；Leeds Teaching Hospitals NHS Trust（利兹教学医院NHS信托）；Linköping University（林雪平大学）；Centre for Medical Image Science and Visualization (CMIV), Linköping University（林雪平大学医学图像科学与可视化中心）
