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 […]
Portable automated rapid testing for auditory assessment: repeated at-home testing in older adults
IntroductionHearing challenges are prevalent in older adults and are associated with age-related cognitive decline. However, measuring age-related changes in hearing faces critical barriers related to accessibility and scalability. High-fidelity tests of central auditory functions are often unavailable to the individuals for whom auditory monitoring is most critical, particularly older adults.MethodsThis study evaluated the feasibility and […]
Why health information technology safety problems remain invisible
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VCap: Hypergeometric Rewards for Weak-to-Strong Visual Captioning
arXiv:2605.28023v1 Announce Type: cross Abstract: Visual captioning requires models to capture visual content faithfully while minimizing both omission and hallucination. As the dominant paradigm for captioning, MLLMs have achieved strong performance through scaling and high-quality data. Recently, RL has emerged as a key route to driving MLLMs toward higher precision and broader coverage, however, existing […]
Thinking as Compression: Your Reasoning Model is Secretly a Context Compressor
arXiv:2605.28713v1 Announce Type: new Abstract: Context compression aims to shorten long context inputs with minimal information loss for LLM inference acceleration. While existing methods have shown promise, they typically rely on complex compression modules or compression-specific training, leaving the intrinsic capabilities of LLMs underexplored. In contrast, this work reveals that a thinking model itself can […]
Benchmarking AI for low-resource contexts: Thinking beyond leaderboards
arXiv:2605.28508v1 Announce Type: new Abstract: Existing AI evaluation practices often fail to capture how systems actually perform in low-resource environments, where operational constraints shape usability as much as model quality. Through a structured analysis of existing benchmark families across speech, chat/RAG, and vision systems, we identify critical gaps between laboratory evaluation practices and real-world deployment […]
SmartDirector: Keyframe-Conditioned Cinematic Video Generation with Narrative Pacing Control
arXiv:2605.27891v1 Announce Type: cross Abstract: The narrative quality of a video fundamentally determines its perceptual value. Although existing video generation methods can produce visually appealing content, they predominantly rely on sparse conditioning signals such as text prompts or first/last frames, which limits precise control over narrative structure and temporal pacing. In this paper, we propose […]
Conditionally Site-Independent Neural Evolution of Antibody Sequences
arXiv:2602.18982v4 Announce Type: replace-cross Abstract: Common deep learning approaches for antibody engineering focus on modeling the marginal distribution of sequences. By treating sequences as independent samples, however, these methods overlook affinity maturation as a rich and largely untapped source of information about the evolutionary process by which antibodies explore the underlying fitness landscape. In contrast, […]
TRACER: Turn-level Regret Matching with Inner Reinforcement Credit for Cooperative Multi-LLM Reasoning
arXiv:2605.28699v1 Announce Type: new Abstract: Large language models increasingly rely on either reinforcement learning or multi-agent prompting to improve reasoning, yet these two paradigms remain difficult to combine. Directly applying single-agent reinforcement learning to multi-turn multi-agent systems faces following dilemmas: i) Sparse rewards, role-level free-riding and excessive training overhead. ii) Agents only imitate to collaborate. […]
Towards Rigorous Explainability by Feature Attribution
arXiv:2604.15898v2 Announce Type: replace Abstract: For around a decade, non-symbolic methods have been the option of choice when explaining complex machine learning (ML) models. Unfortunately, such methods lack rigor and can mislead human decision-makers. In high-stakes uses of ML, the lack of rigor is especially problematic. One prime example of provable lack of rigor is […]