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Establishment and Optimization of a Patient-Reported Outcome–Based Electronic-Diary for Symptoms Evaluation in Patients With Gastroesophageal Reflux Disorder: Prospective Cohort Study

✇Journal of Medical Internet Research
By: Yun-Chun Chen · Yen-Po Wang · Jui-Hsuan Hung · Da-Wei Wang · Shang-Liang Wu · Li-Fen Chen · Yueh-Hsin Ping · Mei-Lien Pan · Ching-Liang Lu
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
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PRIME: an interpretable artificial intelligence model based on liquid biopsy improves prediction of progression risk in non-small cell lung cancer

✇MRD
By: Yu Wang · Yong-Bo Xiang · Xiao-Wei Chen · Tao Zhang · Jian-Yang Wang · Wen-Yang Liu · Lei Deng · Lu-Hua Wang · Shu-Geng Gao · Nan Bi

Mil Med Res. 2026 Jan 6;12(1):94. doi: 10.1186/s40779-025-00679-z.

ABSTRACT

BACKGROUND: Despite the predictive impact of circulating tumor DNA (ctDNA) minimal residual disease (MRD), accurate prediction of failure risk after curative-intent treatments for early-stage or localized non-small cell lung cancer (NSCLC) patients to guide personalized therapy remains challenging. This study aimed to develop and validate an interpretable artificial intelligence-assisted model using global data resources.

METHODS: Liquid biopsy data, blood-based genomic alterations, clinicopathological features, and survival outcomes of stage I-III NSCLC patients who underwent surgery or definitive chemoradiotherapy were collected from 6 cohorts. PRIME (Progression Risk prediction by Interpretable Machine learning on ctDNA-MRD, Mutations, and clinical-therapeutic features) was trained by 6 machine learning algorithms across 4 cohorts and validated in 2 independent cohorts. Model performance was evaluated by the area under the curve (AUC) and interpreted by SHapley Additive exPlanations (SHAP). Whole-exome sequencing (WES) or whole-genome sequencing (WGS) of tumor tissue from 430 stage II-III NSCLC patients and RNA-sequencing (RNA-seq) data from 1149 subjects, sourced from The Cancer Genome Atlas, were used to validate the prognostic effect of mutations identified in peripheral blood and investigate the underlying mechanisms.

RESULTS: A global dataset encompassing 781 blood samples from 493 patients was analyzed. Clinical stage, pre-treatment ctDNA, post-treatment MRD, blood-based Kelch-like ECH-associated protein 1 (KEAP1), serine/threonine kinase 11 (STK11), and cyclin-dependent kinase inhibitor 2A (CDKN2A) mutations, and treatment modality were significantly associated with the risk of disease progression and were thereby included in the model training. WES/WGS and RNA-seq confirmed the poor prognostic effect of KEAP1, STK11, and CDKN2A mutations, which were characterized by the suppressive tumor microenvironment and attenuated humoral immunity. The neural network (NN) model exhibited optimal prediction of treatment failure risk in the training (AUC = 0.85, 95% CI 0.81-0.89) and validation sets (AUC = 0.82, 95% CI 0.74-0.89). SHAP analysis indicated that MRD (+0.306), treatment modality (+0.128), and pre-treatment ctDNA (+0.043) ranked in the top 3 contributions. NN-PRIME outperformed single liquid biopsy biomarkers and clinical-therapeutic signatures, and demonstrated consistent robustness across different clinical scenarios. High-risk patients identified by NN-PRIME had poorer prognoses but derived significant benefits from adjuvant therapy after surgery.

CONCLUSIONS: As an interpretable model integrating readily-accessible and crucial clinical-genomic predictors, PRIME achieves enhanced performance, allowing for early outcome prediction, refined risk stratification, and personalized clinical decision-making.

PMID:41491583 | PMC:PMC12771999 | DOI:10.1186/s40779-025-00679-z

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Agentic AI for Autonomous, Explainable, and Real-Time Credit Risk Decision-Making

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chandra Sekhar Kubam
arXiv:2601.00818v1 Announce Type: new Abstract: Significant digitalization of financial services in a short period of time has led to an urgent demand to have autonomous, transparent and real-time credit risk decision making systems. The traditional machine learning models are effective in pattern recognition, but do not have the adaptive reasoning, situational awareness, and autonomy needed in modern financial operations. As a proposal, this paper presents an Agentic AI framework, or a system where AI agents view the world of dynamic credit independent of human observers, who then make actions based on their articulable decision-making paths. The research introduces a multi-agent system with reinforcing learning, natural language reasoning, explainable AI modules, and real-time data absorption pipelines as a means of assessing the risk profiles of borrowers with few humans being involved. The processes consist of agent collaboration protocol, risk-scoring engines, interpretability layers, and continuous feedback learning cycles. Findings indicate that decision speed, transparency and responsiveness is better than traditional credit scoring models. Nevertheless, there are still some practical limitations such as risks of model drift, inconsistencies in interpreting high dimensional data and regulatory uncertainties as well as infrastructure limitations in low-resource settings. The suggested system has a high prospective to transform credit analytics and future studies ought to be directed on dynamic regulatory compliance mobilizers, new agent teamwork, adversarial robustness, and large-scale implementation in cross-country credit ecosystems.
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Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rong Zhou · Dongping Chen · Zihan Jia · Yao Su · Yixin Liu · Yiwen Lu · Dongwei Shi · Yue Huang · Tianyang Xu · Yi Pan · Xinliang Li · Yohannes Abate · Qingyu Chen · Zhengzhong Tu · Yu Yang · Yu Zhang · Qingsong Wen · Gengchen Mai · Sunyang Fu · Jiachen Li · Xuyu Wang · Ziran Wang · Jing Huang · Tianming Liu · Yong Chen · Lichao Sun · Lifang He
arXiv:2601.01321v1 Announce Type: new Abstract: Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities through the integration of artificial intelligence technologies. This paper presents a unified four-stage framework that systematically characterizes AI integration across the digital twin lifecycle, spanning modeling, mirroring, intervention, and autonomous management. By synthesizing existing technologies and practices, we distill a unified four-stage framework that systematically characterizes how AI methodologies are embedded across the digital twin lifecycle: (1) modeling the physical twin through physics-based and physics-informed AI approaches, (2) mirroring the physical system into a digital twin with real-time synchronization, (3) intervening in the physical twin through predictive modeling, anomaly detection, and optimization strategies, and (4) achieving autonomous management through large language models, foundation models, and intelligent agents. We analyze the synergy between physics-based modeling and data-driven learning, highlighting the shift from traditional numerical solvers to physics-informed and foundation models for physical systems. Furthermore, we examine how generative AI technologies, including large language models and generative world models, transform digital twins into proactive and self-improving cognitive systems capable of reasoning, communication, and creative scenario generation. Through a cross-domain review spanning eleven application domains, including healthcare, aerospace, smart manufacturing, robotics, and smart cities, we identify common challenges related to scalability, explainability, and trustworthiness, and outline directions for responsible AI-driven digital twin systems.
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Beyond Gemini-3-Pro: Revisiting LLM Routing and Aggregation at Scale

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shengji Tang · Weihao Lin · Jingqi Ye · Hao Li · Bo Zhang · Shuyue Hu · Tao Chen · Wangli Ouyang · Lei Bai · Peng Ye
arXiv:2601.01330v1 Announce Type: new Abstract: Large Language Models (LLMs) have rapidly advanced, with Gemini-3-Pro setting a new performance milestone. In this work, we explore collective intelligence as an alternative to monolithic scaling, and demonstrate that open-source LLMs' collaboration can surpass Gemini-3-Pro. We first revisit LLM routing and aggregation at scale and identify three key bottlenecks: (1) current train-free routers are limited by a query-based paradigm focusing solely on textual similarity; (2) recent aggregation methods remain largely static, failing to select appropriate aggregators for different tasks;(3) the complementarity of routing and aggregation remains underutilized. To address these problems, we introduce JiSi, a novel framework designed to release the full potential of LLMs' collaboration through three innovations: (1) Query-Response Mixed Routing capturing both semantic information and problem difficulty; (2) Support-Set-based Aggregator Selection jointly evaluating the aggregation and domain capacity of aggregators; (3) Adaptive Routing-Aggregation Switch dynamically leveraging the advantages of routing and aggregation. Comprehensive experiments on nine benchmarks demonstrate that JiSi can surpass Gemini-3-Pro with only 47% costs by orchestrating ten open-source LLMs, while outperforming mainstream baselines. It suggests that collective intelligence represents a novel path towards Artificial General Intelligence (AGI).
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Yuan3.0 Flash: An Open Multimodal Large Language Model for Enterprise Applications

✇cs.AI, q-bio.NC updates on arXiv.org
By: YuanLab. ai · : · Shawn Wu · Sean Wang · Louie Li · Darcy Chen · Allen Wang · Jiangang Luo · Xudong Zhao · Joseph Shen · Gawain Ma · Jasper Jia · Marcus Mao · Claire Wang · Hunter He · Carol Wang · Zera Zhang · Jason Wang · Chonly Shen · Leo Zhang · Logan Chen · Qasim Meng · James Gong · Danied Zhao · Penn Zheng · Owen Zhu · Tong Yu
arXiv:2601.01718v1 Announce Type: new Abstract: We introduce Yuan3.0 Flash, an open-source Mixture-of-Experts (MoE) MultiModal Large Language Model featuring 3.7B activated parameters and 40B total parameters, specifically designed to enhance performance on enterprise-oriented tasks while maintaining competitive capabilities on general-purpose tasks. To address the overthinking phenomenon commonly observed in Large Reasoning Models (LRMs), we propose Reflection-aware Adaptive Policy Optimization (RAPO), a novel RL training algorithm that effectively regulates overthinking behaviors. In enterprise-oriented tasks such as retrieval-augmented generation (RAG), complex table understanding, and summarization, Yuan3.0 Flash consistently achieves superior performance. Moreover, it also demonstrates strong reasoning capabilities in domains such as mathematics, science, etc., attaining accuracy comparable to frontier model while requiring only approximately 1/4 to 1/2 of the average tokens. Yuan3.0 Flash has been fully open-sourced to facilitate further research and real-world deployment: https://github.com/Yuan-lab-LLM/Yuan3.0.
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OpenNovelty: An LLM-powered Agentic System for Verifiable Scholarly Novelty Assessment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ming Zhang · Kexin Tan · Yueyuan Huang · Yujiong Shen · Chunchun Ma · Li Ju · Xinran Zhang · Yuhui Wang · Wenqing Jing · Jingyi Deng · Huayu Sha · Binze Hu · Jingqi Tong · Changhao Jiang · Yage Geng · Yuankai Ying · Yue Zhang · Zhangyue Yin · Zhiheng Xi · Shihan Dou · Tao Gui · Qi Zhang · Xuanjing Huang
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.
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Multimodal Fact-Checking: An Agent-based Approach

✇cs.AI, q-bio.NC updates on arXiv.org
By: Danni Xu · Shaojing Fan · Harry Cheng · Mohan Kankanhalli
arXiv:2512.22933v3 Announce Type: replace Abstract: The rapid spread of multimodal misinformation poses a growing challenge for automated fact-checking systems. Existing approaches, including large vision language models (LVLMs) and deep multimodal fusion methods, often fall short due to limited reasoning and shallow evidence utilization. A key bottleneck is the lack of dedicated datasets that provide complete real-world multimodal misinformation instances accompanied by annotated reasoning processes and verifiable evidence. To address this limitation, we introduce RW-Post, a high-quality and explainable dataset for real-world multimodal fact-checking. RW-Post aligns real-world multimodal claims with their original social media posts, preserving the rich contextual information in which the claims are made. In addition, the dataset includes detailed reasoning and explicitly linked evidence, which are derived from human written fact-checking articles via a large language model assisted extraction pipeline, enabling comprehensive verification and explanation. Building upon RW-Post, we propose AgentFact, an agent-based multimodal fact-checking framework designed to emulate the human verification workflow. AgentFact consists of five specialized agents that collaboratively handle key fact-checking subtasks, including strategy planning, high-quality evidence retrieval, visual analysis, reasoning, and explanation generation. These agents are orchestrated through an iterative workflow that alternates between evidence searching and task-aware evidence filtering and reasoning, facilitating strategic decision-making and systematic evidence analysis. Extensive experimental results demonstrate that the synergy between RW-Post and AgentFact substantially improves both the accuracy and interpretability of multimodal fact-checking.
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Deployability-Centric Infrastructure-as-Code Generation: Fail, Learn, Refine, and Succeed through LLM-Empowered DevOps Simulation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianyi Zhang · Shidong Pan · Zejun Zhang · Zhenchang Xing · Xiaoyu Sun
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.
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Membership Inference Attacks on LLM-based Recommender Systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiajie He · Min-Chun Chen · Xintong Chen · Xinyang Fang · Yuechun Gu · Keke Chen
arXiv:2508.18665v4 Announce Type: replace-cross Abstract: Large language models (LLMs) based recommender systems (RecSys) can adapt to different domains flexibly. It utilizes in-context learning (ICL), i.e., prompts, to customize the recommendation functions, which include sensitive historical user-specific item interactions, encompassing implicit feedback such as clicked items and explicit product reviews. Such private information may be exposed by novel privacy attacks. However, no study has been conducted on this important issue. We design several membership inference attacks (MIAs) aimed to revealing whether system prompts include victims' historical interactions. The attacks are \emph{Similarity, Memorization, Inquiry, and Poisoning attacks}, each utilizing unique features of LLMs or RecSys. We have carefully evaluated them on five of the latest open-source LLMs and three well-known RecSys benchmark datasets. The results confirm that the MIA threat to LLM RecSys is realistic: inquiry and poisoning attacks show significantly high attack advantages. We also discussed possible methods to mitigate such MIA threats. We have also analyzed the factors affecting these attacks, such as the number of shots in system prompts, the position of the victim in the shots, the number of poisoning items in the prompt,etc.
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Wearable-informed generative digital avatars predict task-conditioned post-stroke locomotion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yanning Dai · Chenyu Tang · Ruizhi Zhang · Wenyu Yang · Yilan Zhang · Yuhui Wang · Junliang Chen · Xuhang Chen · Ruimou Xie · Yangyue Cao · Qiaoying Li · Jin Cao · Tao Li · Hubin Zhao · Yu Pan · Arokia Nathan · Xin Gao · Peter Smielewski · Shuo Gao
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
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A machine learning-derived polygenic risk score reveals that healthy lifestyle counteracts obesity-related mortality

✇npj Digital Medicine
By: Lushan Xiao · Shengxing Liang · Lin Zeng · Shumin Cai · Jiaren Wang · Chang Hong · Yan Li · Ruining Li · Pu Jiang · Zebin Xie · Ting Li · Shanshan Wu · Li Liu · Gongfa Wu · Weinan Lai

npj Digital Medicine, Published online: 06 January 2026; doi:10.1038/s41746-025-02314-6

A machine learning-derived polygenic risk score reveals that healthy lifestyle counteracts obesity-related mortality
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