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Received — 4 February 2026 ⏭ Favourites

Digital intervention <i>mylovia</i> improves sexual functioning in women with sexual dysfunction in randomized controlled trial

✇npj Digital Medicine
By: Wiebke Blaszcyk · Melanie Büttner · Linda T. Betz · Antje Riepenhausen · Gitta A. Jacob · Jan Philipp Klein · Johanna Schröder
3 February 2026 at 08:00

npj Digital Medicine, Published online: 03 February 2026; doi:10.1038/s41746-026-02385-z

Digital intervention mylovia improves sexual functioning in women with sexual dysfunction in randomized controlled trial

Integrative proteogenomics maps multifactorial aetiology, progression and therapeutic vulnerabilities in gastric cancer

✇Omics in Gastric
By: Ya-Hsuan Chang · Tzu-Chan Hong · Kuen-Tyng Lin · Yi-Jing Hsiao · Hsiang-En Hsu · Juanilita T Waniwan · Rodrigo Espinoza Silva · I-Rue Lai · Po-Chu Lee · Ming-Tsan Lin · Chia-Tung Shun · Min-Shu Hsieh · Yi-Ju Chen · Song-Wei Wang · Wen-Hung Hsu · I-Chen Wu · Yao-Kuang Wang · Ching-Chun Li · Jaw-Yuan Wang · Yin-Chen Hsu · Hao Fang · Ze-Shiang Lin · Wen-Hsin Chang · Jyun-Hong Lin · Yan-Si Chen · Ying-Chieh Ko · Chi-Ya Shen · Yan-Ming Chen · Chia-Yu Wang · Yu-Teng Jheng · Wen-Yi Liu · Yu-Tai Wang · Chang-Wei Yeh · Pei-Rong Huang · Jyh-Ming Liou · Li-Tzong Chen · Chia-Li Han · Deng-Chyang Wu · Hsuan-Yu Chen · Sung-Liang Yu · Ming-Shiang Wu · Yu-Ju Chen · Taiwan Cancer Moonshot Program
30 January 2026 at 19:00

Gut. 2026 Jan 30:gutjnl-2025-337247. doi: 10.1136/gutjnl-2025-337247. Online ahead of print.

ABSTRACT

BACKGROUND: Gastric cancer, with disproportionately higher incidence in East Asia, arises from complex host-microbiome-environment interactions beyond Helicobacter pylori (HP) infection. However, the molecular architecture linking environmental carcinogens, microbial succession and host response remains unclear.

OBJECTIVE: To delineate multifactorial aetiologies and clinically actionable subtypes/biomarkers of gastric cancer through integrative proteogenomic, microbial and environmental exposure profiling.

DESIGN: We established a multiomics atlas of paired tumour, adjacent mucosa tissues and blood from 154 treatment-naïve Taiwanese patients, integrating whole-exome sequencing, RNA-seq, proteome and phosphoproteome profiling with carcinogen signatures, HP status, microbiome composition and refined anatomical mapping. Cell-based functional assays tested carcinogen effects. Microbial subtype was assessed in an independent cohort.

RESULTS: A polycyclic-aromatic-hydrocarbon signature, dibenz[a,h]acridine, emerged as a high-risk exposure promoting invasion, immune suppression and poor survival, significantly exceeding nitrosamine-linked risk in this cohort. Multilayer integration defined three initiation ecologies: HP-driven inflammatory, non-HP microbiome-enriched immune-silent and HP-free microbially depleted states. Among HP-negative tumours, a Streptococcus-enriched subtype associated with tight-junction (CLDN18.2/ZO-1/OCLN) disruption and epithelial-mesenchymal transition, whereas a subset of clinically aggressive cases retained CLDN18.2-high epithelial-stable subtype for therapeutic accessibility. An independent cohort revealed gastric juice-derived Streptococcus anginosus abundance inversely correlated with tight-junction proteins. Anatomical mapping reveals location-specific, sex-specific, subtype-specific oncogenic networks and kinase activity, including CDK4 activation in clinical biomarker-negative tumours. Decision-tree models combining exposure and proteome-immune states refined recurrence and survival prediction beyond stage.

CONCLUSION: This proteogenomic framework defines exposure-informed and microbiome-informed gastric cancer subtypes, providing a molecular schema for patient stratification, prevention and actionable therapeutic vulnerabilities.

PMID:41617485 | DOI:10.1136/gutjnl-2025-337247

Liquid biopsy biomarkers for accurate detection of malignant pulmonary nodules: a meta-analytic approach

✇Pulmonary nodule
By: Alberto Caballero-Vazquez · Marta Garcia-Cerezo · Laura R Fernandez-Castro · Concepcion Morales-Garcia · Antonio Jesus Lainez Ramos-Bossini · Fabian Vergara-Rubio · Francisco Gabriel Ortega-Sanchez · Jose Manuel Sanchez-Maldonado
29 January 2026 at 19:00

Discov Oncol. 2026 Jan 29;17(1):178. doi: 10.1007/s12672-025-03646-1.

ABSTRACT

Pulmonary nodules are a common radiological finding that can be classified as either benign or Malignant, with significant clinical implications. The early detection of malignant nodules is critically important for improving the prognosis of lung cancer, which remains the leading cause of cancer-related mortality worldwide. Traditional imaging techniques have Limitations in accurately classifying pulmonary nodules. Liquid biopsy, a minimally invasive method that evaluates circulating components in the Blood, presents promising diagnostic potential in this context. This study aims to evaluate the diagnostic capacity of multiple liquid biopsy biomarkers for early and accurate differentiation between benign and Malignant pulmonary nodules. Accordingly, we conducted a comprehensive study involving a meta-analysis, selecting 16 eligible studies that utilised liquid biopsy to assess various circulating biomarkers in the diagnostic yield. The most significant results were linked to circulating free DNA (cfDNA). However, other components, including circulating tumour cells (CTCs), microRNAs/pfeRNAs, extracellular vesicles (EVs), serological markers, and imaging techniques, also provided valuable information. Similarly, integrating multi-omics data with machine learning models has been shown to enhance the ability to differentiate between benign and malignant pulmonary nodules, thereby supporting early diagnosis and improved management for patients with lung cancer.

PMID:41612093 | PMC:PMC12855667 | DOI:10.1007/s12672-025-03646-1

Received — 30 January 2026 ⏭ Favourites

The Relationship Between Physician Self-Disclosure and Patient Acquisition in Digital Health Markets: Cross-Sectional Study

✇Journal of Medical Internet Research
By: Quanchen Liu · Pengqing Yin · Jing Fan
30 January 2026 at 06:00
Background: Online health communities have evolved into digital marketplaces where physicians have to compete for patients. Existing research examines physician-patient dynamics through a patient-centric lens, treating physicians as passive recipients of ratings and reviews, while the strategic role of physician self-disclosure remains unexamined. This gap constrains a comprehensive understanding of how physicians can actively shape patient decisions, making the investigation of strategic self-disclosure imperative. Objective: This study aims to investigate the relationship between physician self-disclosure breadth (scope of information) and depth (detailed expertise) and patient decision-making, as well as whether regional digital health care level (DHL) moderates these relationships. Methods: We conducted a cross-sectional analysis of observational data to test these relationships. Data were collected from China’s online health care platform Haodf from September to December 2024. Self-disclosure breadth (including clinical performance, academic experience, and social reputation), self-disclosure depth (including expertise coverage, richness, and granularity), and patient decision-making (total visits) were captured through manual content coding and quantitative measurement. We used structured content analysis to extract the disclosure components, informational scope, and descriptive details of each profile. Then, using validated operational formulas, we calculated the composite indices for disclosure breadth and depth based on the coded dimensions. The study generated 1798 final physician samples with complete data across 14 focal variables. The hypotheses were tested using an ordinary least squares regression model, and 4 robustness checks were conducted, including variable substitution and different resampling techniques. Results: In the primary ordinary least squares regression models, self-disclosure breadth was significantly and positively associated with patient visits (β=0.255, 95% CI 0.054-0.456; P=.01), as was self-disclosure depth (β=0.098, 95% CI 0.030-0.167; P=.005). The breadth×DHL interaction was positive and significant (β=0.261, 95% CI 0.061-0.461; P=.01). Similarly, the depth×DHL interaction was positive and significant (β=0.070, 95% CI 0.002-0.138; P=.045). It should be noted that the association for self-disclosure breadth was stronger than that of self-disclosure depth. DHL strengthened the relationship between the disclosure strategies with patient visits. This contextual amplification indicates that DHL serves as a critical boundary condition, determining the degree to which physician self-disclosure strategies translate into patient acquisition outcomes. Conclusions: This study reconceptualizes physicians as strategic agents shaping patient decision-making through purposeful self-disclosure. Different from existing studies treating physicians as passive recipients of ratings and reviews, our research demonstrates that physicians can strategically shape patient acquisition through self-disclosure breadth and depth. This study brings new insights to digital health markets by demonstrating that self-disclosure operates as a viable patient acquisition mechanism, wherein the DHL acts as a critical boundary condition. The findings have real-world implications: (1) physicians can leverage evidence-based disclosure strategies, (2) platforms should implement context-adaptive features, and (3) policymakers should prioritize digital infrastructure investments to enhance physicians' competitive capabilities and patient decision-making quality.
Received — 28 January 2026 ⏭ Favourites

Agentic Digital Twins: A Taxonomy of Capabilities for Understanding Possible Futures

✇cs.AI, q-bio.NC updates on arXiv.org
By: Christopher Burr · Mark Enzer · Jason Shepherd · David Wagg
28 January 2026 at 13:00
arXiv:2601.18799v1 Announce Type: cross Abstract: As digital twins (DTs) evolve to become more agentic through the integration of artificial intelligence (AI), they acquire capabilities that extend beyond dynamic representation of their target systems. This paper presents a taxonomy of agentic DTs organised around three fundamental dimensions: the locus of agency (external, internal, distributed), the tightness of coupling (loose, tight, constitutive), and model evolution (static, adaptive, reconstructive). From the resulting 27-configuration space, we identify nine illustrative configurations grouped into three clusters: "The Present" (existing tools and emerging steering systems), "The Threshold" (where emergent properties appear and coupling becomes constitutive), and "The Frontier" (where systems gain reconstructive capabilities). Our analysis explores how agentic DTs exercise performative power--not merely representing physical systems but actively participating in constituting them. Using traffic navigation systems as examples, we show how even passive tools can exhibit emergent performativity, while advanced configurations risk performative lock-in. Drawing on performative prediction theory, we trace a progression from passive tools through active steering to ontological reconstruction, examining how constitutive coupling enables systems to create self-validating realities. Understanding these configurations is essential for navigating the transformation from DTs as mirror worlds to DTs as architects of new ontologies.

Is On-Policy Data always the Best Choice for Direct Preference Optimization-based LM Alignment?

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zetian Sun · Dongfang Li · Xuhui Chen · Baotian Hu · Min Zhang
28 January 2026 at 13:00
arXiv:2508.10530v2 Announce Type: replace Abstract: The alignment of language models~(LMs) with human preferences is critical for building reliable AI systems. The problem is typically framed as optimizing an LM policy to maximize the expected reward that reflects human preferences. Recently, Direct Preference Optimization~(DPO) was proposed as a LM alignment method that directly optimize the policy from static preference data, and further improved by incorporating on-policy sampling~(i.e., preference candidates generated during the training loop) for better LM alignment. However, we show on-policy data is not always optimal, with systematic effectiveness difference emerging between static and on-policy preference candidates. For example, on-policy data can result in a $3\times$ effectiveness compared with static data for Llama-3, and a $0.4\times$ effectiveness for Zephyr. To explain the phenomenon, we propose the alignment stage assumption, which divides the alignment process into two distinct stages: the preference injection stage, which benefits from diverse data, and the preference fine-tuning stage, which favors high-quality data. Through theoretical and empirical analysis, we characterize these stages and propose an effective algorithm to identify the boundaries between them. We perform experiments on $5$ models~(Llama, Zephyr, Phi-2, Qwen, Pythia) and $2$ alignment methods~(DPO, SLiC-HF) to show the generalizability of alignment stage assumption and the effectiveness of the boundary measurement algorithm.

Rethinking the AI Scientist: Interactive Multi-Agent Workflows for Scientific Discovery

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lukas Weidener · Marko Brki\'c · Mihailo Jovanovi\'c · Ritvik Singh · Chiara Baccin · Emre Ulgac · Alex Dobrin · Aakaash Meduri
28 January 2026 at 13:00
arXiv:2601.12542v2 Announce Type: replace Abstract: Artificial intelligence systems for scientific discovery have demonstrated remarkable potential, yet existing approaches remain largely proprietary and operate in batch-processing modes requiring hours per research cycle, precluding real-time researcher guidance. This paper introduces Deep Research, a multi-agent system enabling interactive scientific investigation with turnaround times measured in minutes. The architecture comprises specialized agents for planning, data analysis, literature search, and novelty detection, unified through a persistent world state that maintains context across iterative research cycles. Two operational modes support different workflows: semi-autonomous mode with selective human checkpoints, and fully autonomous mode for extended investigations. Evaluation on the BixBench computational biology benchmark demonstrated state-of-the-art performance, achieving 48.8% accuracy on open response and 64.4% on multiple-choice evaluation, exceeding existing baselines by 14 to 26 percentage points. Analysis of architectural constraints, including open access literature limitations and challenges inherent to automated novelty assessment, informs practical deployment considerations for AI-assisted scientific workflows.

General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yue Jian · Curtis Wu · Danny Reidenbach · Aditi S. Krishnapriyan
28 January 2026 at 13:00
arXiv:2406.16821v2 Announce Type: replace-cross Abstract: Structure-based drug design (SBDD) aims to generate ligands that bind strongly and specifically to target protein pockets. Recent diffusion models have advanced SBDD by capturing the distributions of atomic positions and types, yet they often underemphasize binding affinity control during generation. To address this limitation, we introduce \textbf{\textnormal{\textbf{BADGER}}}, a general \textbf{binding-affinity guidance framework for diffusion models in SBDD}. \textnormal{\textbf{BADGER} }incorporates binding affinity awareness through two complementary strategies: (1) \textit{classifier guidance}, which applies gradient-based affinity signals during sampling in a plug-and-play fashion, and (2) \textit{classifier-free guidance}, which integrates affinity conditioning directly into diffusion model training. Together, these approaches enable controllable ligand generation guided by binding affinity. \textnormal{\textbf{BADGER} } can be added to any diffusion model and achieves up to a \textbf{60\% improvement in ligand--protein binding affinity} of sampled molecules over prior methods. Furthermore, we extend the framework to \textbf{multi-constraint diffusion guidance}, jointly optimizing for binding affinity, drug-likeness (QED), and synthetic accessibility (SA) to design realistic and synthesizable drug candidates.

AI-generated data contamination erodes pathological variability and diagnostic reliability

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hongyu He · Shaowen Xiang · Ye Zhang · Yingtao Zhu · Jin Zhang · Hao Deng · Emily Alsentzer · Qingyu Chen · Kun-Hsing Yu · Andrew Marshall · Tingting Chen · Srinivas Anumasa · Daniel Ebner · Dean Ho · Kee Yuan Ngiam · Ching-Yu Cheng · Dianbo Liu
28 January 2026 at 13:00
arXiv:2601.12946v3 Announce Type: replace-cross Abstract: Generative artificial intelligence (AI) is rapidly populating medical records with synthetic content, creating a feedback loop where future models are increasingly at risk of training on uncurated AI-generated data. However, the clinical consequences of this AI-generated data contamination remain unexplored. Here, we show that in the absence of mandatory human verification, this self-referential cycle drives a rapid erosion of pathological variability and diagnostic reliability. By analysing more than 800,000 synthetic data points across clinical text generation, vision-language reporting, and medical image synthesis, we find that models progressively converge toward generic phenotypes regardless of the model architecture. Specifically, rare but critical findings, including pneumothorax and effusions, vanish from the synthetic content generated by AI models, while demographic representations skew heavily toward middle-aged male phenotypes. Crucially, this degradation is masked by false diagnostic confidence; models continue to issue reassuring reports while failing to detect life-threatening pathology, with false reassurance rates tripling to 40%. Blinded physician evaluation confirms that this decoupling of confidence and accuracy renders AI-generated documentation clinically useless after just two generations. We systematically evaluate three mitigation strategies, finding that while synthetic volume scaling fails to prevent collapse, mixing real data with quality-aware filtering effectively preserves diversity. Ultimately, our results suggest that without policy-mandated human oversight, the deployment of generative AI threatens to degrade the very healthcare data ecosystems it relies upon.

Products, Performance, and Technological Development of Ambulatory Oxygen Therapy Devices: Scoping Review

✇Journal of Medical Internet Research
By: Shohei Kawachi · Mariana Hoffman · Lorena Romero · Magnus Ekström · Jerry A Krishnan · Anne E Holland
28 January 2026 at 03:00
Background: Ambulatory oxygen therapy is prescribed for patients with chronic lung diseases who experience exertional hypoxemia. However, available devices may not adequately meet user requirements, and their performance characteristics are heterogeneous. Objective: This study aims to identify devices available for delivery of ambulatory oxygen therapy, the technologies that they use to generate oxygen, the performance characteristics of each device, and the development status. Methods: We used medical and engineering databases to identify peer-reviewed papers (eg, MEDLINE, IEEE). Gray literature was used to identify additional descriptions of ambulatory oxygen devices in military medicine, space exploration, or patents. The last search was conducted in September 2025. Documents that described a device that can deliver oxygen in an ambulatory context (defined as weighing less than 10 kg) and were written in English were included. Search results were screened for inclusion by 2 independent reviewers. Data were synthesized by descriptively mapping the performance of each product, the technology used, and the development status of emerging technologies. Results: From 9702 records identified, a total of 166 met eligibility criteria (106 scientific publications and 60 gray literature). We identified 33 portable oxygen concentrators (POCs; 29 commercially available), 10 oxygen cylinders, and 6 portable liquid oxygen (LOX) devices. The POC products showed a trade-off between portability and oxygen delivery capacity (maximum flow rate ranging from 2.0 to 6.0 L/min; device weight ranging from 1.0 to 9.1 kg). Pressure swing adsorption with zeolite was the most common oxygen generation technology in POCs on the market. The mean maximum continuous operating time of POCs was 3.8 hours. Two prototype POCs (maximum flow rate of 4-6 L/min and device weight of 8-9 kg) were developed for space exploration using modified adsorbents. LOX devices were the lightest and had the longest continuous operating time. Innovations in delivery included the downsizing of a POC by using nanozeolite as an adsorbent and pulse oximeter oxygen saturation (SpO2)–targeted automatic titration of oxygen delivery based on the user’s SpO2. Conclusions: This scoping review is the first study to integrate medical, engineering, and gray literature on ambulatory oxygen devices and their development. Although prior literature has narratively explained the products and technologies, no previous research has systematically investigated them. This review showed that POCs available to consumers may not meet the needs of patients in terms of flow rate, portability, and operating time. LOX devices offered superior performance but are limited by high costs. Limitations of this review include the difficulty of comparing product performance across oxygen delivery settings and that the records were largely obtained from English-language sources. Innovation in ambulatory oxygen technology has been limited over the past decade, highlighting urgent need for research and development of new lightweight devices with higher oxygen delivery. Clinical Trial: OSF Registries 10.17605/OSF.IO/QS7FX; https://osf.io/qs7fx

Publisher Correction: Best practice recommendations and considerations for designing and electronically implementing event-driven diaries in clinical trials

✇npj Digital Medicine
By: Florence D. Mowlem · Jeremiah J. Trudeau · Jill V. Platko · Jos Bloemers · Emuella Flood · Jessica L. Abel · Kelly Dumais · Paul O’Donohoe · Sabrina Grant · Ryan Naville-Cook · Onyekachukwu Illoh · Shannon D. Keith · Jing Ju · Megan Fitter · Dorothee Oberdhan · Randall Winnette · Manuela Bossi · Naomi Suminski · Sonya Eremenco · Lindsay Hughes · Amy Fasiczka · Luisana Rojas · Michelle Campbell · Scottie Kern
27 January 2026 at 08:00

npj Digital Medicine, Published online: 27 January 2026; doi:10.1038/s41746-026-02396-w

Publisher Correction: Best practice recommendations and considerations for designing and electronically implementing event-driven diaries in clinical trials
Received — 27 January 2026 ⏭ Favourites

High-Fidelity Longitudinal Patient Simulation Using Real-World Data

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yu Akagi · Tomohisa Seki · Hiromasa Ito · Toru Takiguchi · Kazuhiko Ohe · Yoshimasa Kawazoe
27 January 2026 at 13:00
arXiv:2601.17310v1 Announce Type: new Abstract: Simulation is a powerful tool for exploring uncertainty. Its potential in clinical medicine is transformative and includes personalized treatment planning and virtual clinical trials. However, simulating patient trajectories is challenging because of complex biological and sociocultural influences. Here, we show that real-world clinical records can be leveraged to empirically model patient timelines. We developed a generative simulator model that takes a patient's history as input and synthesizes fine-grained, realistic future trajectories. The model was pretrained on more than 200 million clinical records. It produced high-fidelity future timelines, closely matching event occurrence rates, laboratory test results, and temporal dynamics in real patient future data. It also accurately estimated future event probabilities, with observed-to-expected ratios consistently near 1.0 across diverse outcomes and time horizons. Our results reveal the untapped value of real-world data in electronic health records and introduce a scalable framework for in silico modeling of clinical care.

Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity

✇cs.AI, q-bio.NC updates on arXiv.org
By: Onyedikachi Hope Amaechi-Okorie · Branislav Radeljic
27 January 2026 at 13:00
arXiv:2601.18641v1 Announce Type: cross Abstract: Speech remains one of the most visible yet overlooked vectors of inclusion and exclusion in contemporary society. While fluency is often equated with credibility and competence, individuals with atypical speech patterns are routinely marginalized. Given the current state of the debate, this article focuses on the structural biases that shape perceptions of atypical speech and are now being encoded into artificial intelligence. Automated speech recognition (ASR) systems and voice interfaces, trained predominantly on standardized speech, routinely fail to recognize or respond to diverse voices, compounding digital exclusion. As AI technologies increasingly mediate access to opportunity, the study calls for inclusive technological design, anti-bias training to minimize the impact of discriminatory algorithmic decisions, and enforceable policy reform that explicitly recognize speech diversity as a matter of equity, not merely accessibility. Drawing on interdisciplinary research, the article advocates for a cultural and institutional shift in how we value voice, urging co-created solutions that elevate the rights, representation, and realities of atypical speakers in the digital age. Ultimately, the article reframes speech inclusion as a matter of equity (not accommodation) and advocates for co-created AI systems that reflect the full spectrum of human voices.

MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications

✇cs.AI, q-bio.NC updates on arXiv.org
By: Praveenkumar Kanithi · Cl\'ement Christophe · Marco AF Pimentel · Tathagata Raha · Prateek Munjal · Nada Saadi · Hamza A Javed · Svetlana Maslenkova · Nasir Hayat · Ronnie Rajan · Shadab Khan
27 January 2026 at 13:00
arXiv:2409.07314v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become saturated and increasingly disconnected from the functional requirements of clinical workflows. To bridge the gap between theoretical capability and verified utility, we introduce MEDIC, a comprehensive evaluation framework establishing leading indicators across various clinical dimensions. Beyond standard question-answering, we assess operational capabilities using deterministic execution protocols and a novel Cross-Examination Framework (CEF), which quantifies information fidelity and hallucination rates without reliance on reference texts. Our evaluation across a heterogeneous task suite exposes critical performance trade-offs: we identify a significant knowledge-execution gap, where proficiency in static retrieval does not predict success in operational tasks such as clinical calculation or SQL generation. Furthermore, we observe a divergence between passive safety (refusal) and active safety (error detection), revealing that models fine-tuned for high refusal rates often fail to reliably audit clinical documentation for factual accuracy. These findings demonstrate that no single architecture dominates across all dimensions, highlighting the necessity of a portfolio approach to clinical model deployment. As part of this investigation, we released a public leaderboard on Hugging Face.\footnote{https://huggingface.co/spaces/m42-health/MEDIC-Benchmark}

Pretrain Value, Not Reward: Decoupled Value Policy Optimization

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chenghua Huang · Lu Wang · Fangkai Yang · Pu Zhao · Zhixu Li · Qingwei Lin · Dongmei Zhang · Saravan Rajmohan · Qi Zhang
27 January 2026 at 13:00
arXiv:2502.16944v2 Announce Type: replace-cross Abstract: In this paper, we explore how directly pretraining a value model simplifies and stabilizes reinforcement learning from human feedback (RLHF). In reinforcement learning, value estimation is the key to policy optimization, distinct from reward supervision. The value function predicts the \emph{return-to-go} of a partial answer, that is, how promising the partial answer is if it were continued to completion. In RLHF, however, the standard pipeline first pretrains a reward model and then learns a value function online, even though no new reward signals are available once preference data is collected. This makes critic learning redundant, as the process of training a reward model and then deriving a value model is informationally equivalent to directly pretraining a value model. Importantly, this requires no additional supervision, and our value model is trained on exactly the same data used for reward modeling. Building on this insight, we introduce \emph{Decoupled Value Policy Optimization} (DVPO), a framework that pretrains a \emph{Global Value Model} (GVM) offline and freezes it as a universal critic for policy learning. The GVM provides stable, fine-grained credit assignment without critic drift or trajectory sampling. Experiments across MT-Bench, Alpaca-Eval, and Arena-Hard demonstrate that DVPO matches or surpasses state-of-the-art RLHF methods. These results highlight RLHF can be reframed as policy-only optimization guided by a single pretrained value model.

uPVC-Net: A Universal Premature Ventricular Contraction Detection Deep Learning Algorithm

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hagai Hamami · Yosef Solewicz · Daniel Zur · Yonatan Kleerekoper · Joachim A. Behar
27 January 2026 at 13:00
arXiv:2506.11238v2 Announce Type: replace-cross Abstract: Introduction: Premature Ventricular Contractions (PVCs) are common cardiac arrhythmias originating from the ventricles. Accurate detection remains challenging due to variability in electrocardiogram (ECG) waveforms caused by differences in lead placement, recording conditions, and population demographics. Methods: We developed uPVC-Net, a universal deep learning model to detect PVCs from any single-lead ECG recordings. The model is developed on four independent ECG datasets comprising a total of 8.3 million beats collected from Holter monitors and a modern wearable ECG patch. uPVC-Net employs a custom architecture and a multi-source, multi-lead training strategy. For each experiment, one dataset is held out to evaluate out-of-distribution (OOD) generalization. Results: uPVC-Net achieved an AUC between 97.8% and 99.1% on the held-out datasets. Notably, performance on wearable single-lead ECG data reached an AUC of 99.1%. Conclusion: uPVC-Net exhibits strong generalization across diverse lead configurations and populations, highlighting its potential for robust, real-world clinical deployment.

On the Fundamental Limits of LLMs at Scale

✇cs.AI, q-bio.NC updates on arXiv.org
By: Muhammad Ahmed Mohsin · Muhammad Umer · Ahsan Bilal · Zeeshan Memon · Muhammad Ibtsaam Qadir · Sagnik Bhattacharya · Hassan Rizwan · Abhiram R. Gorle · Maahe Zehra Kazmi · Nukhba Amir · Ali Subhan · Muhammad Usman Rafique · Zihao He · Pulkit Mehta · Muhammad Ali Jamshed · John M. Cioffi
27 January 2026 at 13:00
arXiv:2511.12869v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have benefited enormously from scaling, yet these gains are bounded by five fundamental limitations: (1) hallucination, (2) context compression, (3) reasoning degradation, (4) retrieval fragility, and (5) multimodal misalignment. While existing surveys describe these phenomena empirically, they lack a rigorous theoretical synthesis connecting them to the foundational limits of computation, information, and learning. This work closes that gap by presenting a unified, proof-informed framework that formalizes the innate theoretical ceilings of LLM scaling. First, computability and uncomputability imply an irreducible residue of error: for any computably enumerable model family, diagonalization guarantees inputs on which some model must fail, and undecidable queries (e.g., halting-style tasks) induce infinite failure sets for all computable predictors. Second, information-theoretic and statistical constraints bound attainable accuracy even on decidable tasks, finite description length enforces compression error, and long-tail factual knowledge requires prohibitive sample complexity. Third, geometric and computational effects compress long contexts far below their nominal size due to positional under-training, encoding attenuation, and softmax crowding. We further show how likelihood-based training favors pattern completion over inference, how retrieval under token limits suffers from semantic drift and coupling noise, and how multimodal scaling inherits shallow cross-modal alignment. Across sections, we pair theorems and empirical evidence to outline where scaling helps, where it saturates, and where it cannot progress, providing both theoretical foundations and practical mitigation paths like bounded-oracle retrieval, positional curricula, and sparse or hierarchical attention.

Empowering LLMs for Structure-Based Drug Design via Exploration-Augmented Latent Inference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xuanning Hu · Anchen Li · Qianli Xing · Jinglong Ji · Hao Tuo · Bo Yang
27 January 2026 at 13:00
arXiv:2601.15333v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) possess strong representation and reasoning capabilities, but their application to structure-based drug design (SBDD) is limited by insufficient understanding of protein structures and unpredictable molecular generation. To address these challenges, we propose Exploration-Augmented Latent Inference for LLMs (ELILLM), a framework that reinterprets the LLM generation process as an encoding, latent space exploration, and decoding workflow. ELILLM explicitly explores portions of the design problem beyond the model's current knowledge while using a decoding module to handle familiar regions, generating chemically valid and synthetically reasonable molecules. In our implementation, Bayesian optimization guides the systematic exploration of latent embeddings, and a position-aware surrogate model efficiently predicts binding affinity distributions to inform the search. Knowledge-guided decoding further reduces randomness and effectively imposes chemical validity constraints. We demonstrate ELILLM on the CrossDocked2020 benchmark, showing strong controlled exploration and high binding affinity scores compared with seven baseline methods. These results demonstrate that ELILLM can effectively enhance LLMs capabilities for SBDD.

Multimodal digital biopsy for preoperative prediction of occult peritoneal metastasis in gastric cancer

✇npj Digital Medicine
By: Sheng Chen · Ping’an Ding · Yihao Yang · Shuo Ma · Honghai Guo · Xiao Han · Jiaxuan Yang · Wenqian Ma · Ning Meng · Zhijia Xia · Xiaolong Li · Lilong Zhang · Yanlong Shi · Zhenjiang Guo · Kaixuan Gao · Renjun Gu · Hong Long · Lingjiao Meng · Qun Zhao
26 January 2026 at 08:00

npj Digital Medicine, Published online: 26 January 2026; doi:10.1038/s41746-025-02268-9

Multimodal digital biopsy for preoperative prediction of occult peritoneal metastasis in gastric cancer

Feasibility, Acceptability, and Perspectives Regarding the Use of Activity Tracking Wearable Devices Among Home Health Aides: Mixed Methods Study

✇Journal of Medical Internet Research
By: Ian René Solano-Kamaiko · Michael Dicpinigaitis · Melissa Tan · Irene Yang · Kexin Cheng · Ronica Peramsetty · Michelle Shum · Yanira Escamilla · Jennifer Bayly · Meghan Reading Turchioe · Ariel Avgar · Aditya Vashistha · Nicola Dell · Madeline R Sterling
27 January 2026 at 02:00
Background: Home health aides and attendants (HHAs) provide in-home care to the growing population of older adults who want to age in place. Despite their vital role in patient care, HHAs are an underserved and vulnerable population of health care professionals who often experience poor health themselves. Activity tracking devices offer a promising way to improve HHAs’ health-related awareness and promote health behavior change, particularly regarding physical activity and sleep quality, 2 areas in which the workforce struggles. Objective: This study aimed to understand how feasible it is for HHAs to use activity tracking devices and assess their perceptions of such devices for improving their health. Specifically, we conducted (1) a field study to assess the use, feasibility, and acceptability of these devices among HHAs and (2) a qualitative study to understand HHAs’ perspectives on and reactions to activity trackers on and off the job. Methods: We partnered with the 1199 Service Employees International Union Training and Employment Fund to conduct a field study with home care agency–employed HHAs working in New York City, New York. Participants wore activity tracking devices for 4 weeks that collected data on physical activity and sleep. The HHAs were subsequently interviewed on their experiences with and attitudes toward the devices and asked to reflect on personalized visualizations of their data to prompt them to think aloud. Quantitative data were analyzed using descriptive statistics. Qualitative data were analyzed using grounded theory. Results: A total of 17 HHAs participated; their mean age was 48.7 (SD 12.2) years, 15 (88%) were women, 11 (65%) identified as Black, 5 (29%) identified as Hispanic or Latinx, and they had worked as HHAs for a mean of 11.7 (SD 7.5) years. In total, 94% (n=16) of the HHAs wore their activity trackers for the full 28-day study period. Participants took a mean of 10,230 (SD 3586) daily steps during the study period and slept for a mean of 6.27 (SD 0.58) hours per night. Overall, 4 key themes emerged: (1) activity tracking devices enhanced participants’ health awareness by providing empirical data for self-reflection; (2) this increased awareness led to positive behavior changes, including setting and achieving health-related goals; (3) HHAs believed that these devices could improve not only their own health but also that of their patients through positive behavior changes; and (4) despite this optimism, participants emphasized that their ability to modify sleep and activity patterns was constrained by social and occupational determinants, with sleep improvements being particularly challenging. Conclusions: Our findings suggest that appropriately designed personal tracking interventions could offer a promising approach to supporting positive health-related changes in this historically overlooked workforce, potentially improving their well-being and the quality of care they provide to their patients.
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