The Digital First Primary Care (DFPC) model, introduced by NHS England, aims to enhance healthcare accessibility and efficiency by leveraging digital tools such as telemedicine, digital triage, and virtual consultations. In this structured narrative review, we synthesized UK-focused empirical, policy, and implementation literature to examine DFPC through the patient-centered care (PCC) domains of access, autonomy, […]
The economics of digitally integrated wellness services in heritage regions
Wellness tourism is among the fastest-growing segments of the global health economy, yet its development in Central Asian heritage regions remains constrained by fragmented service delivery, limited digital infrastructure, and a shortage of evidence-based planning tools. In this Perspective, we argue that advancing wellness tourism in such regions requires coupling econometric diagnosis of revenue drivers […]
Screening anxiety via contrastive autobiographical recall
IntroductionLanguage offers a low-burden and scalable pathway for digital anxiety screening, particularly in telehealth or repeated-monitoring settings where spontaneous speech may already be available. This study introduces a contrastive autobiographical recall framework that uses short positive and negative personal memories to capture within person affective shifts in language. By modelling how the same individual expresses […]
Beyond Binary: Speech Representations Across the Cognitive Score Hierarchy
arXiv:2605.27189v1 Announce Type: cross Abstract: This study examines the relationship between speech representations and the hierarchical structure of cognitive assessment in mild cognitive impairment. Utilizing 5,754 German neuropsychological assessment recordings, we evaluate six cognitive tasks across three score levels: task, domain, and global levels. We compare hand-crafted acoustic features with self-supervised learning (SSL) embeddings. Results […]
Uniboost: Global Coordination with Value Alignment for Fair and Efficient Traffic Allocation
arXiv:2605.26424v1 Announce Type: cross Abstract: With the rapid evolution of internet services, recommendation systems have become indispensable. In particular, the blending (re-ranking) stage plays a pivotal role in allocating traffic across diverse business objectives. However, existing approaches often suffer from coupled allocation plans, score inflation, and a lack of interpretability. To address these challenges, we […]
Why LLMs Hallucinate on Structured Knowledge: A Mechanistic Analysis of Reasoning over Linearized Representations
arXiv:2605.26362v1 Announce Type: cross Abstract: In many reasoning tasks, large language models (LLMs) rely on structured external knowledge, such as graphs and tables, which is typically linearized into sequential token representations. However, even when sufficient knowledge is available, LLMs can still produce hallucinated outputs, and the underlying mechanisms behind such failures remain poorly understood. We […]
Confounder Detection via Treatment Intent: A New Observational Study Design
arXiv:2605.26413v1 Announce Type: cross Abstract: Understanding the effects of interventions is central to scientific progress, with randomized controlled trials (RCTs) regarded as the gold standard for causal inference in many applied fields. However, RCTs are costly, time-consuming, and often constrained by ethical or practical limitations, motivating the need for causal methods able to draw conclusions […]
Ethical Fairness without Demographics in Human-Centered AI
arXiv:2603.13373v3 Announce Type: replace-cross Abstract: In ubiquitous and mobile health systems, computational models infer human states from wearable, behavioral, and physiological sensing data. In these settings, high accuracy alone is insufficient; models must act ethically and equitably across diverse people, contexts, and devices. However, fairness methods that rely on demographic or heterogeneous attributes during training […]
Evaluating the Relevance of Uncertainty Estimators for LLM Hallucination
arXiv:2605.27016v1 Announce Type: cross Abstract: Large language models (LLMs) are prone to hallucinations, i.e., statements unsupported by the input or training data, hindering reliable deployment. In parallel, numerous uncertainty estimation (UE) methods have been proposed to quantify model confidence and are often implicitly treated as proxies for model failure. However, the relationship between uncertainty and […]
CyberEvolver: Structured Self-Evolution for Cybersecurity Agents On the Fly
arXiv:2605.26195v1 Announce Type: cross Abstract: LLM-based agents are increasingly used for cybersecurity tasks, but most existing systems rely on fixed, human-designed scaffolds that struggle to adapt across diverse targets and failure modes. We introduce textscCyberEvolver, a self-evolving cybersecurity agent framework that iteratively revises its own scaffold based on experience from failed execution attempts. Self-evolution in […]
LitSeg: Narrative-Aware Document Segmentation for Literary RAG
arXiv:2605.27156v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge, particularly for long-tail domains such as literary works. However, the critical step of document segmentation in RAG remains largely underexplored. Existing strategies are typically semantically blind and overlook the complicated narrative structures of literary works, often resulting in […]
TADDLE: A Tool-Augmented Agent for Detecting Deficient LLM-Generated Peer Reviews
arXiv:2605.26911v1 Announce Type: new Abstract: LLM-generated peer reviews are increasingly common at major venues, yet their deficiencies are hard to detect because they are uniformly fluent and well-structured. Existing work either classifies authorship without judging quality, or scores quality with features designed for human-written reviews; no prior system detects deficiencies in LLM-generated reviews at the […]