Background: Gastroesophageal reflux disease (GERD) symptoms significantly affect patients’ quality of life. Patient-reported outcome (PRO) instruments for symptoms measurement in GERD patients is advocated by regulatory authority. Current tools for GERD symptoms evaluation are limited and the results can be biased by the recall bias. To better characterize the GERD symptoms, an e-diary was developed for daily GERD symptom monitoring. Objective: To build up and optimize a PRO-based e-diary, and to investigate the effect of symptom frequency on adherence. Methods: The GERD e-diary evaluated 8 daytime (acid regurgitation, cough, heartburn, sour taste in the mouth, hiccups, hoarseness, dysphagia, and chest pain) and 2 nighttime symptoms (acid regurgitation and cough) for consecutive 8 weeks. The adherence of e-diary, defined as daily completing rate of e-diary, was evaluated and optimized from First Stage to Third Stage with no reminder implemented in First Stage, sending reminding SMS (Short Message Service) text messaging upon detecting missing data in Second Stage (no reminder during the first 3 to 5 days after enrollment), and immediate installation of reminding system at enrollment in Third Stage. GERD symptom frequency was obtained by summation of the symptomatic days in each week. A multiple regression analysis was performed to examine the effects of system optimization and GERD symptom frequency on patient adherence, while controlling for potential confounding variables. Results: 138 GERD patients (M/F=70/68; age: mean 52.9, SD 12.3 years) were recruited. At First Stage, the adherence was 47.2%, 40% and 57.6% for nighttime, daytime and overall symptom. System optimization significantly improved adherence with increased adherence of nighttime symptoms by 12.5% (P=.005) and 10.9% (P=.01), daytime symptom by 21.7% (P

arXiv:2601.01576v1 Announce Type: cross Abstract: Evaluating novelty is critical yet challenging in peer review, as reviewers must assess submissions against a vast, rapidly evolving literature. This report presents OpenNovelty, an LLM-powered agentic system for transparent, evidence-based novelty analysis. The system operates through four phases: (1) extracting the core task and contribution claims to generate retrieval queries; (2) retrieving relevant prior work based on extracted queries via semantic search engine; (3) constructing a hierarchical taxonomy of core-task-related work and performing contribution-level full-text comparisons against each contribution; and (4) synthesizing all analyses into a structured novelty report with explicit citations and evidence snippets. Unlike naive LLM-based approaches, \textsc{OpenNovelty} grounds all assessments in retrieved real papers, ensuring verifiable judgments. We deploy our system on 500+ ICLR 2026 submissions with all reports publicly available on our website, and preliminary analysis suggests it can identify relevant prior work, including closely related papers that authors may overlook. OpenNovelty aims to empower the research community with a scalable tool that promotes fair, consistent, and evidence-backed peer review.

arXiv:2601.01627v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly deployed in healthcare field, it becomes essential to carefully evaluate their medical safety before clinical use. However, existing safety benchmarks remain predominantly English-centric, and test with only single-turn prompts despite multi-turn clinical consultations. To address these gaps, we introduce JMedEthicBench, the first multi-turn conversational benchmark for evaluating medical safety of LLMs for Japanese healthcare. Our benchmark is based on 67 guidelines from the Japan Medical Association and contains over 50,000 adversarial conversations generated using seven automatically discovered jailbreak strategies. Using a dual-LLM scoring protocol, we evaluate 27 models and find that commercial models maintain robust safety while medical-specialized models exhibit increased vulnerability. Furthermore, safety scores decline significantly across conversation turns (median: 9.5 to 5.0, $p

arXiv:2506.05623v2 Announce Type: replace-cross Abstract: Infrastructure-as-Code (IaC) generation holds significant promise for automating cloud infrastructure provisioning. Recent advances in Large Language Models (LLMs) present a promising opportunity to democratize IaC development by generating deployable infrastructure templates from natural language descriptions. However, current evaluation focuses on syntactic correctness while ignoring deployability, the critical measure of the utility of IaC configuration files. Six state-of-the-art LLMs performed poorly on deployability, achieving only 20.8$\sim$30.2% deployment success rate on the first attempt. In this paper, we construct DPIaC-Eval, the first deployability-centric IaC template benchmark consisting of 153 real-world scenarios cross 58 unique services. Also, we propose an LLM-based deployability-centric framework, dubbed IaCGen, that uses iterative feedback mechanism encompassing format verification, syntax checking, and live deployment stages, thereby closely mirroring the real DevOps workflows. Results show that IaCGen can make 54.6$\sim$91.6% generated IaC templates from all evaluated models deployable in the first 10 iterations. Additionally, human-in-the-loop feedback that provide direct guidance for the deployability errors, can further boost the performance to over 90% passItr@25 on all evaluated LLMs. Furthermore, we explore the trustworthiness of the generated IaC templates on user intent alignment and security compliance. The poor performance (25.2% user requirement coverage and 8.4% security compliance rate) indicates a critical need for continued research in this domain.

arXiv:2512.14329v2 Announce Type: replace-cross Abstract: Dynamic prediction of locomotor capacity after stroke could enable more individualized rehabilitation, yet current assessments largely provide static impairment scores and do not indicate whether patients can perform specific tasks such as slope walking or stair climbing. Here, we present a wearable-informed data-physics hybrid generative framework that reconstructs a stroke survivor's locomotor control from wearable inertial sensing and predicts task-conditioned post-stroke locomotion in new environments. From a single 20 m level-ground walking trial recorded by five IMUs, the framework personalizes a physics-based digital avatar using a healthy-motion prior and hybrid imitation learning, generating dynamically feasible, patient-specific movements for inclined walking and stair negotiation. Across 11 stroke inpatients, predicted postures reached 82.2% similarity for slopes and 69.9% for stairs, substantially exceeding a physics-only baseline. In a multicentre pilot randomized study (n = 21; 28 days), access to scenario-specific locomotion predictions to support task selection and difficulty titration was associated with larger gains in Fugl-Meyer lower-extremity scores than standard care (mean change 6.0 vs 3.7 points; $p

Nature Medicine, Published online: 05 January 2026; doi:10.1038/s41591-025-04080-0

This multicenter study demonstrates use of dried and capillary blood as a minimally invasive, scalable approach for Alzheimer’s biomarker testing in research, with potential as a widely scalable population-based research approach, especially in resource-limited settings.

npj Digital Medicine, Published online: 31 December 2025; doi:10.1038/s41746-025-02260-3

A clinically validated 3D deep learning approach for quantifying vascular invasion in pancreatic cancer

arXiv:2512.22334v1 Announce Type: new Abstract: We introduce SciEvalKit, a unified benchmarking toolkit designed to evaluate AI models for science across a broad range of scientific disciplines and task capabilities. Unlike general-purpose evaluation platforms, SciEvalKit focuses on the core competencies of scientific intelligence, including Scientific Multimodal Perception, Scientific Multimodal Reasoning, Scientific Multimodal Understanding, Scientific Symbolic Reasoning, Scientific Code Generation, Science Hypothesis Generation and Scientific Knowledge Understanding. It supports six major scientific domains, spanning from physics and chemistry to astronomy and materials science. SciEvalKit builds a foundation of expert-grade scientific benchmarks, curated from real-world, domain-specific datasets, ensuring that tasks reflect authentic scientific challenges. The toolkit features a flexible, extensible evaluation pipeline that enables batch evaluation across models and datasets, supports custom model and dataset integration, and provides transparent, reproducible, and comparable results. By bridging capability-based evaluation and disciplinary diversity, SciEvalKit offers a standardized yet customizable infrastructure to benchmark the next generation of scientific foundation models and intelligent agents. The toolkit is open-sourced and actively maintained to foster community-driven development and progress in AI4Science.

arXiv:2512.23184v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to simulate human behavior, but common practices to use LLM-generated data are inefficient. Treating an LLM's output ("model choice") as a single data point underutilizes the information inherent to the probabilistic nature of LLMs. This paper introduces and formalizes "model belief," a measure derived from an LLM's token-level probabilities that captures the model's belief distribution over choice alternatives in a single generation run. The authors prove that model belief is asymptotically equivalent to the mean of model choices (a non-trivial property) but forms a more statistically efficient estimator, with lower variance and a faster convergence rate. Analogous properties are shown to hold for smooth functions of model belief and model choice often used in downstream applications. The authors demonstrate the performance of model belief through a demand estimation study, where an LLM simulates consumer responses to different prices. In practical settings with limited numbers of runs, model belief explains and predicts ground-truth model choice better than model choice itself, and reduces the computation needed to reach sufficiently accurate estimates by roughly a factor of 20. The findings support using model belief as the default measure to extract more information from LLM-generated data.

arXiv:2512.23545v1 Announce Type: cross Abstract: Recent pathological foundation models have substantially advanced visual representation learning and multimodal interaction. However, most models still rely on a static inference paradigm in which whole-slide images are processed once to produce predictions, without reassessment or targeted evidence acquisition under ambiguous diagnoses. This contrasts with clinical diagnostic workflows that refine hypotheses through repeated slide observations and further examination requests. We propose PathFound, an agentic multimodal model designed to support evidence-seeking inference in pathological diagnosis. PathFound integrates the power of pathological visual foundation models, vision-language models, and reasoning models trained with reinforcement learning to perform proactive information acquisition and diagnosis refinement by progressing through the initial diagnosis, evidence-seeking, and final decision stages. Across several large multimodal models, adopting this strategy consistently improves diagnostic accuracy, indicating the effectiveness of evidence-seeking workflows in computational pathology. Among these models, PathFound achieves state-of-the-art diagnostic performance across diverse clinical scenarios and demonstrates strong potential to discover subtle details, such as nuclear features and local invasions.

Background: Digital therapeutics (DTx) for children and adolescents with mental health problems have been developed in the health care industry. Despite reports of side effects from DTx for children and adolescents, there have been no guidelines to address the prevention of DTx overdependence among young users. Objective: This study aimed to identify the requirements for guidelines to prevent DTx overdependence in children and adolescents and to develop and evaluate these guidelines. Methods: We conducted 2 phases. This study first involved a phase I survey to develop guidelines, including assessments of smartphone usage and mental health conditions. The second phase evaluated the guidelines’ effectiveness, reliability, necessity, and satisfaction using a visual analog scale through a randomized controlled trial. Participants—45 children and adolescents aged 9-16 years and 42 caregivers—were randomly assigned to the experimental and control groups. Results: Phase I revealed that blocking mobile applications and notifications (mean 8.5, SD 1.8) and parental monitoring (mean 8.5, SD 2.1) were effective preventive features. Caregivers, children, and adolescents expressed concerns about the side effects and overdependence of DTx and decreased effects due to nonindividualized guidelines in subjective responses to the phase I survey. Based on these insights, personalized guidelines for phase II were developed, in which overall mean visual analog scale scores for guideline evaluation were higher in the experimental group, except for necessity among caregivers (mean 8.5, SD 1.3 versus mean 8.7, SD 1.2). Conclusions: Both caregivers and children and adolescents demonstrated the need for guidelines to prevent overdependence on DTx distinct from smartphone usage. Tailored guidelines may be acceptable for use in real-world therapeutic protocols. Guidelines to prevent overdependence on DTx in children and adolescents and to achieve a balance between their benefits and risks need to be established. Trial Registration: Clinical Research Information Service (CRiS) of the Republic of Korea KCT0008893; https://cris.nih.go.kr/cris/search/detailSearch.do?seq=25609

npj Digital Medicine, Published online: 26 December 2025; doi:10.1038/s41746-025-02277-8

A novel evaluation benchmark for medical LLMs illuminating safety and effectiveness in clinical domains

arXiv:2512.16856v1 Announce Type: new Abstract: AI safety and alignment research has predominantly been focused on methods for safeguarding individual AI systems, resting on the assumption of an eventual emergence of a monolithic Artificial General Intelligence (AGI). The alternative AGI emergence hypothesis, where general capability levels are first manifested through coordination in groups of sub-AGI individual agents with complementary skills and affordances, has received far less attention. Here we argue that this patchwork AGI hypothesis needs to be given serious consideration, and should inform the development of corresponding safeguards and mitigations. The rapid deployment of advanced AI agents with tool-use capabilities and the ability to communicate and coordinate makes this an urgent safety consideration. We therefore propose a framework for distributional AGI safety that moves beyond evaluating and aligning individual agents. This framework centers on the design and implementation of virtual agentic sandbox economies (impermeable or semi-permeable), where agent-to-agent transactions are governed by robust market mechanisms, coupled with appropriate auditability, reputation management, and oversight to mitigate collective risks.

arXiv:2512.15769v1 Announce Type: cross Abstract: The increasing use of generative models such as diffusion models for synthetic data augmentation has greatly reduced the cost of data collection and labeling in downstream perception tasks. However, this new data source paradigm may introduce important security concerns. This work investigates backdoor propagation in such emerging generative data supply chains, namely Data-Chain Backdoor (DCB). Specifically, we find that open-source diffusion models can become hidden carriers of backdoors. Their strong distribution-fitting ability causes them to memorize and reproduce backdoor triggers during generation, which are subsequently inherited by downstream models, resulting in severe security risks. This threat is particularly concerning under clean-label attack scenarios, as it remains effective while having negligible impact on the utility of the synthetic data. Furthermore, we discover an Early-Stage Trigger Manifestation (ESTM) phenomenon: backdoor trigger patterns tend to surface more explicitly in the early, high-noise stages of the diffusion model's reverse generation process before being subtly integrated into the final samples. Overall, this work reveals a previously underexplored threat in generative data pipelines and provides initial insights toward mitigating backdoor risks in synthetic data generation.

arXiv:2512.16455v1 Announce Type: cross Abstract: In this paper, we describe a federated compute platform dedicated to support Artificial Intelligence in scientific workloads. Putting the effort into reproducible deployments, it delivers consistent, transparent access to a federation of physically distributed e-Infrastructures. Through a comprehensive service catalogue, the platform is able to offer an integrated user experience covering the full Machine Learning lifecycle, including model development (with dedicated interactive development environments), training (with GPU resources, annotation tools, experiment tracking, and federated learning support) and deployment (covering a wide range of deployment options all along the Cloud Continuum). The platform also provides tools for traceability and reproducibility of AI models, integrates with different Artificial Intelligence model providers, datasets and storage resources, allowing users to interact with the broader Machine Learning ecosystem. Finally, it is easily customizable to lower the adoption barrier by external communities.

arXiv:2512.09566v2 Announce Type: replace Abstract: Drug discovery is a time-consuming and expensive process, with traditional high-throughput and docking-based virtual screening hampered by low success rates and limited scalability. Recent advances in generative modelling, including autoregressive, diffusion, and flow-based approaches, have enabled de novo ligand design beyond the limits of enumerative screening. Yet these models often suffer from inadequate generalization, limited interpretability, and an overemphasis on binding affinity at the expense of key pharmacological properties, thereby restricting their translational utility. Here we present Trio, a molecular generation framework integrating fragment-based molecular language modeling, reinforcement learning, and Monte Carlo tree search, for effective and interpretable closed-loop targeted molecular design. Through the three key components, Trio enables context-aware fragment assembly, enforces physicochemical and synthetic feasibility, and guides a balanced search between the exploration of novel chemotypes and the exploitation of promising intermediates within protein binding pockets. Experimental results show that Trio reliably achieves chemically valid and pharmacologically enhanced ligands, outperforming state-of-the-art approaches with improved binding affinity (+7.85%), drug-likeness (+11.10%) and synthetic accessibility (+12.05%), while expanding molecular diversity more than fourfold. By combining generalization, plausibility, and interpretability, Trio establishes a closed-loop generative paradigm that redefines how chemical space can be navigated, offering a transformative foundation for the next era of AI-driven drug discovery.

arXiv:2511.07392v3 Announce Type: replace-cross Abstract: In robotic surgery, surgeons fully engage their hands and visual attention in procedures, making it difficult to access and manipulate multimodal patient data without interrupting the workflow. To overcome this problem, we propose a Voice-Interactive Surgical Agent (VISA) built on a hierarchical multi-agent framework consisting of an orchestration agent and three task-specific agents driven by Large Language Models (LLMs). These LLM-based agents autonomously plan, refine, validate, and reason to interpret voice commands and execute tasks such as retrieving clinical information, manipulating CT scans, or navigating 3D anatomical models within surgical video. We construct a dataset of 240 user commands organized into hierarchical categories and introduce the Multi-level Orchestration Evaluation Metric (MOEM) that evaluates the performance and robustness at both the command and category levels. Experimental results demonstrate that VISA achieves high stage-level accuracy and workflow-level success rates, while also enhancing its robustness by correcting transcription errors, resolving linguistic ambiguity, and interpreting diverse free-form expressions. These findings highlight the strong potential of VISA to support robotic surgery and its scalability for integrating new functions and agents.

arXiv:2512.01241v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized. We present NOHARM (Numerous Options Harm Assessment for Risk in Medicine), a benchmark using 100 real primary care-to-specialist consultation cases to measure frequency and severity of harm from LLM-generated medical recommendations. NOHARM covers 10 specialties, with 12,747 expert annotations for 4,249 clinical management options. Across 31 LLMs, potential for severe harm from LLM recommendations occurs in up to 22.2% (95% CI 21.6-22.8%) of cases, with harm of omission accounting for 76.6% (95% CI 76.4-76.8%) of errors. Safety performance is only moderately correlated (r = 0.61-0.64) with existing AI and medical knowledge benchmarks. The best models outperform generalist physicians on safety (mean difference 9.7%, 95% CI 7.0-12.5%), and a diverse multi-agent approach improves safety compared to solo models (mean difference 8.0%, 95% CI 4.0-12.1%). Therefore, despite strong performance on existing evaluations, widely used AI models can produce severely harmful medical advice at nontrivial rates, underscoring clinical safety as a distinct performance dimension necessitating explicit measurement.

arXiv:2512.13716v1 Announce Type: new Abstract: Personalized decision-making is essential for human-AI interaction, enabling AI agents to act in alignment with individual users' value preferences. As AI systems expand into real-world applications, adapting to personalized values beyond task completion or collective alignment has become a critical challenge. We address this by proposing a value-driven approach to personalized decision-making. Human values serve as stable, transferable signals that support consistent and generalizable behavior across contexts. Compared to task-oriented paradigms driven by external rewards and incentives, value-driven decision-making enhances interpretability and enables agents to act appropriately even in novel scenarios. We introduce ValuePilot, a two-phase framework consisting of a dataset generation toolkit (DGT) and a decision-making module (DMM). DGT constructs diverse, value-annotated scenarios from a human-LLM collaborative pipeline. DMM learns to evaluate actions based on personal value preferences, enabling context-sensitive, individualized decisions. When evaluated on previously unseen scenarios, DMM outperforms strong LLM baselines, including GPT-5, Claude-Sonnet-4, Gemini-2-flash, and Llama-3.1-70b, in aligning with human action choices. Our results demonstrate that value-driven decision-making is an effective and extensible engineering pathway toward building interpretable, personalized AI agents.