arXiv:2512.05270v1 Announce Type: cross Abstract: As mobile robots increasingly operate alongside humans in shared workspaces, ensuring safe, efficient, and interpretable Human-Robot Interaction (HRI) has become a pressing challenge. While substantial progress has been devoted to human behavior prediction, limited attention has been paid to how humans perceive, interpret, and trust robots' inferences, impeding deployment in safety-critical and socially embedded environments. This paper presents XR-DT, an eXtended Reality-enhanced Digital Twin framework for agentic mobile robots, that bridges physical and virtual spaces to enable bi-directional understanding between humans and robots. Our hierarchical XR-DT architecture integrates virtual-, augmented-, and mixed-reality layers, fusing real-time sensor data, simulated environments in the Unity game engine, and human feedback captured through wearable AR devices. Within this framework, we design an agentic mobile robot system with a unified diffusion policy for context-aware task adaptation. We further propose a chain-of-thought prompting mechanism that allows multimodal large language models to reason over human instructions and environmental context, while leveraging an AutoGen-based multi-agent coordination layer to enhance robustness and collaboration in dynamic tasks. Initial experimental results demonstrate accurate human and robot trajectory prediction, validating the XR-DT framework's effectiveness in HRI tasks. By embedding human intention, environmental dynamics, and robot cognition into the XR-DT framework, our system enables interpretable, trustworthy, and adaptive HRI.

arXiv:2509.25721v4 Announce Type: replace-cross Abstract: We present an extended version of the AI Productivity Index (APEX-v1-extended), a benchmark for assessing whether frontier models are capable of performing economically valuable tasks in four jobs: investment banking associate, management consultant, big law associate, and primary care physician (MD). This technical report details the extensions to APEX-v1, including an increase in the held-out evaluation set from n = 50 to n = 100 cases per job (n = 400 total) and updates to the grading methodology. We present a new leaderboard, where GPT5 (Thinking = High) remains the top performing model with a score of 67.0%. APEX-v1-extended shows that frontier models still have substantial limitations when performing typical professional tasks. To support further research, we are open sourcing n = 25 non-benchmark example cases per role (n = 100 total) along with our evaluation harness.

arXiv:2510.21084v2 Announce Type: replace-cross Abstract: Intelligent drug recommendation based on Electronic Health Records (EHRs) is critical for improving the quality and efficiency of clinical decision-making. By leveraging large-scale patient data, drug recommendation systems can assist physicians in selecting the most appropriate medications according to a patient's medical history, diagnoses, laboratory results, and comorbidities. Recent advances in large language models (LLMs) have shown remarkable capabilities in complex reasoning and medical text understanding, making them promising tools for drug recommendation tasks. However, the application of LLMs for Chinese clinical medication recommendation remains largely unexplored. In this work, we conduct a systematic investigation of LLM-based methodologies for Chinese discharge medication recommendation. We evaluate several representative LLM families (GLM, Llama, Qwen) under a unified methodological framework including zero-shot prompting, in-context learning, chain-of-thought prompting, and supervised fine-tuning using LoRA. We analyze model behavior across reasoning styles, error patterns, domain adaptation mechanisms, and robustness. Experimental results show that while supervised fine-tuning improves model performance, there remains substantial room for improvement, with the best model achieving the F1 score of 0.5648 and Jaccard score of 0.4477. Our findings highlight both the potential and limitations of LLMs for Chinese drug recommendation.

Background: Health research that uses predictive and/or generative AI is rapidly growing. Just as in traditional clinical studies, the way in which AI studies are conducted can introduce systematic errors. Transmission of this AI evidence into clinical practice and research needs critical appraisal tools for clinical decision makers and researchers. Objective: To identify existing tools for critical appraisal of clinical studies that use artificial intelligence (AI) and examine the concepts and domains these tools explore. Methods: Inclusion criteria in PCC framework P: (population) Artificial intelligence clinical studies. C (Concept): tools for critical appraisal and associated constructs such as: quality, reporting, validity, risk of bias, and applicability. C (context): in clinical practice context. In addition, bias classification and Chatbot assessment studies were included. We searched in medical and engineering databases (MEDLINE, EMBASE, CINAHL, PsycINFO and IEEE). We included clinical primary research with tools for critical appraisal. Classic reviews and systematic reviews were included in first phase of screening. They were excluded in the secondary phase, after identifying new tools by forward snowballing. We excluded non-human, computer and mathematical research, and letters, opinion papers and editorials. We used Rayyan for screening. Data extraction was done by two observers and discrepancies were solved by discussion. The protocol was previously registered in OSF (https://doi.org/10.17605/OSF.IO/ETYDS). We adhered to the PRISMA extension for Scoping reviews and to the PRISMA-Search extension for Reporting Literature in Systematic Reviews. Results: We retrieved 4393 unique records for screening. After excluding 3803 records, 119 were selected for full-text screening. From these, 59 were excluded. After inclusion of 10 studies via other methods, a total of 70 records were finally included. 46 of them were reporting guidelines (15 tools for critical appraisal, 2 for quality of study and 2 for risk of bias). Nine papers ware focused on bias classification or mitigation. We found 15 Chatbots assessment studies or systematic reviews of Chatbots studies (6 and 9 respectively) which are a very heterogeneous group. Conclusions: The results picture a landscape of the evidence tools where reporting tools predominate, followed by critical appraisal and risk of bias tools, and few tools for risk of bias. The mismatch of bias in AI and epidemiology should be considered for critical appraisal, especially regarding fairness and the mitigation bias in the AI. Finally, Chatbot assessment studies is a vast and evolving field in which progress in design, reporting and critical appraisal is necessary and urgent. Clinical Trial: https://doi.org/10.17605/OSF.IO/ETYDS

Oncogene, Published online: 08 December 2025; doi:10.1038/s41388-025-03650-3

Molecular stratification of esophageal adenocarcinoma: implications for prognosis and treatment strategy