Background: Background: Digital health technologies have the potential to revolutionize the screening, diagnostic support, monitoring and intervention of early cognitive change. However, the full spectrum of their application and the existing evidence base in this specific patient population have not been systematically delineated. Objective: Objective: To review and synthesize digital health technologies' applications, roles, and challenges in patients with early cognitive changes. Methods: Methods: This scoping review followed the enhanced Arksey & O'Malley Framework and PRISMA-ScR guidelines. A comprehensive search of four databases (PubMed, Embase, Web of Science, and Cochrane Library) was conducted from their inception until May 31, 2024. Studies were selected and data were extracted using the Population-Concept-Context framework, focusing on digital health interventions for patients with early cognitive changes. Results: Results: A total of 163 articles were included in this review, revealing a notable increase in the use of digital health technologies for patients with early cognitive changes since 2020. Of the studies, 162 focused on Mild Cognitive Impairment(95.1%), 10 on Subjective Cognitive Decline (6.1%), and 7 examined caregiver support (4.3%). The technologies were categorized into six groups: Smartphone/Computer Application, Virtual Reality, Artificial Intelligence/Big Data, Robotics, the Internet of Things, and Telemedicine. The clinical outcomes demonstrated statistically significant improvements in cognitive performance, and patient-reported outcomes including overall well-being, quality of life and social engagement. But digital health technologies also present implementation challenges, such as Virtual Reality induced vestibular symptoms, connectivity issues in telemedicine, and unintended negative consequences. Conclusions: Conclusion: This review affirms the efficacy of digital health technologies (DHTs) in screening, diagnosing, intervening in, and monitoring early cognitive changes. However, challenges such as cost, technical complexity, and user engagement currently impede broader adoption. In conclusion, while DHTs demonstrably enhance healthcare professional efficiency in managing early cognitive impairment—by facilitating clinical decision-making, optimizing patient management, and enabling personalized care—overcoming existing implementation barriers remains critical. Furthermore, rigorous assessment of their long-term effects through future research is essential. Collectively, these findings underscore the substantial potential of DHTs to transform cognitive health management and patient care, offering valuable insights for healthcare professionals, researchers, and policymakers to optimize these solutions. Clinical Trial: Trial Registration: No Trial Registration.

arXiv:2512.08674v1 Announce Type: new Abstract: Multimodal clinical reasoning in the field of gastrointestinal (GI) oncology necessitates the integrated interpretation of endoscopic imagery, radiological data, and biochemical markers. Despite the evident potential exhibited by Multimodal Large Language Models (MLLMs), they frequently encounter challenges such as context dilution and hallucination when confronted with intricate, heterogeneous medical histories. In order to address these limitations, a hierarchical Multi-Agent Framework is proposed, which emulates the collaborative workflow of a human Multidisciplinary Team (MDT). The system attained a composite expert evaluation score of 4.60/5.00, thereby demonstrating a substantial improvement over the monolithic baseline. It is noteworthy that the agent-based architecture yielded the most substantial enhancements in reasoning logic and medical accuracy. The findings indicate that mimetic, agent-based collaboration provides a scalable, interpretable, and clinically robust paradigm for automated decision support in oncology.

arXiv:2512.02906v1 Announce Type: cross Abstract: Understanding high-resolution images remains a significant challenge for multimodal large language models (MLLMs). Recent study address this issue by dividing the image into smaller crops and computing the semantic similarity between each crop and a query using a pretrained retrieval-augmented generation (RAG) model. The most relevant crops are then selected to localize the target object and suppress irrelevant information. However, such crop-based processing can fragment complete objects across multiple crops, thereby disrupting the computation of semantic similarity. In our experiments, we find that image crops of objects with different sizes are better handled at different resolutions. Based on this observation, we propose Multi-resolution Retrieval-Detection (MRD), a training-free framework for high-resolution image understanding. To address the issue of semantic similarity bias caused by objects being split across different image crops, we propose a multi-resolution semantic fusion method, which integrates semantic similarity maps obtained at different resolutions to produce more accurate semantic information and preserve the integrity of target objects. Furthermore, to achieve direct localization of target objects at a global scale, we introduce an open-vocalbulary object detection (OVD) model that identifies object regions using a sliding-window approach.Experiments on high-resolution image understanding benchmarks using different MLLMs demonstrate the effectiveness of our approach.

arXiv:2510.26012v3 Announce Type: replace Abstract: The rapid growth of research literature, particularly in large language models (LLMs), has made producing comprehensive and current survey papers increasingly difficult. This paper introduces autosurvey2, a multi-stage pipeline that automates survey generation through retrieval-augmented synthesis and structured evaluation. The system integrates parallel section generation, iterative refinement, and real-time retrieval of recent publications to ensure both topical completeness and factual accuracy. Quality is assessed using a multi-LLM evaluation framework that measures coverage, structure, and relevance in alignment with expert review standards. Experimental results demonstrate that autosurvey2 consistently outperforms existing retrieval-based and automated baselines, achieving higher scores in structural coherence and topical relevance while maintaining strong citation fidelity. By combining retrieval, reasoning, and automated evaluation into a unified framework, autosurvey2 provides a scalable and reproducible solution for generating long-form academic surveys and contributes a solid foundation for future research on automated scholarly writing. All code and resources are available at https://github.com/annihi1ation/auto_research.

npj Digital Medicine, Published online: 18 November 2025; doi:10.1038/s41746-025-02066-3

Reply to: Utilizing foundation models for developing clinical tools

arXiv:2410.21348v3 Announce Type: replace-cross Abstract: With the increasing application of large language models (LLMs) in the medical domain, evaluating these models' performance using benchmark datasets has become crucial. This paper presents a comprehensive survey of various benchmark datasets employed in medical LLM tasks. These datasets span multiple modalities including text, image, and multimodal benchmarks, focusing on different aspects of medical knowledge such as electronic health records (EHRs), doctor-patient dialogues, medical question-answering, and medical image captioning. The survey categorizes the datasets by modality, discussing their significance, data structure, and impact on the development of LLMs for clinical tasks such as diagnosis, report generation, and predictive decision support. Key benchmarks include MIMIC-III, MIMIC-IV, BioASQ, PubMedQA, and CheXpert, which have facilitated advancements in tasks like medical report generation, clinical summarization, and synthetic data generation. The paper summarizes the challenges and opportunities in leveraging these benchmarks for advancing multimodal medical intelligence, emphasizing the need for datasets with a greater degree of language diversity, structured omics data, and innovative approaches to synthesis. This work also provides a foundation for future research in the application of LLMs in medicine, contributing to the evolving field of medical artificial intelligence.

arXiv:2511.05385v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) utilizes external knowledge to augment Large Language Models' (LLMs) reliability. For flexibility, agentic RAG employs autonomous, multi-round retrieval and reasoning to resolve queries. Although recent agentic RAG has improved via reinforcement learning, they often incur substantial token overhead from search and reasoning processes. This trade-off prioritizes accuracy over efficiency. To address this issue, this work proposes TeaRAG, a token-efficient agentic RAG framework capable of compressing both retrieval content and reasoning steps. 1) First, the retrieved content is compressed by augmenting chunk-based semantic retrieval with a graph retrieval using concise triplets. A knowledge association graph is then built from semantic similarity and co-occurrence. Finally, Personalized PageRank is leveraged to highlight key knowledge within this graph, reducing the number of tokens per retrieval. 2) Besides, to reduce reasoning steps, Iterative Process-aware Direct Preference Optimization (IP-DPO) is proposed. Specifically, our reward function evaluates the knowledge sufficiency by a knowledge matching mechanism, while penalizing excessive reasoning steps. This design can produce high-quality preference-pair datasets, supporting iterative DPO to improve reasoning conciseness. Across six datasets, TeaRAG improves the average Exact Match by 4% and 2% while reducing output tokens by 61% and 59% on Llama3-8B-Instruct and Qwen2.5-14B-Instruct, respectively. Code is available at https://github.com/Applied-Machine-Learning-Lab/TeaRAG.

arXiv:2412.16378v4 Announce Type: replace-cross Abstract: To mitigate reward hacking from response verbosity, modern preference optimization methods are increasingly adopting length normalization (e.g., SimPO, ORPO, LN-DPO). While effective against this bias, we demonstrate that length normalization itself introduces a failure mode: the URSLA shortcut. Here models learn to satisfy the alignment objective by prematurely truncating low-quality responses rather than learning from their semantic content. To address this, we introduce REFA, a new alignment framework that proposes probabilistic control on a structural token that controls termination. Our core innovation is a new class of regularizers that operate directly on the probability of the End-of-Sequence (EOS) token, a previously unexploited control lever. This token-level intervention provides a principled solution to the URSLA shortcut, ensuring genuine quality improvements. Furthermore, it unlocks a versatile mechanism for managing the alignment-efficiency tradeoff, enabling practitioners to fine-tune models that adhere to specific token budgets. Empirically, REFA achieves a 60.29% win rate and a 52.17% length-controlled win rate on AlpacaEval2 with Llama-3-8B-Instruct, demonstrating the power of our token-level control paradigm.

arXiv:2510.21031v1 Announce Type: cross Abstract: The emergence of foundation models (FMs) has enabled the development of highly capable and autonomous agents, unlocking new application opportunities across a wide range of domains. Evaluating the architecture of agents is particularly important as the architectural decisions significantly impact the quality attributes of agents given their unique characteristics, including compound architecture, autonomous and non-deterministic behaviour, and continuous evolution. However, these traditional methods fall short in addressing the evaluation needs of agent architecture due to the unique characteristics of these agents. Therefore, in this paper, we present AgentArcEval, a novel agent architecture evaluation method designed specially to address the complexities of FM-based agent architecture and its evaluation. Moreover, we present a catalogue of agent-specific general scenarios, which serves as a guide for generating concrete scenarios to design and evaluate the agent architecture. We demonstrate the usefulness of AgentArcEval and the catalogue through a case study on the architecture evaluation of a real-world tax copilot, named Luna.

Oncogene, Published online: 01 March 2025; doi:10.1038/s41388-025-03322-2

Preclinical application of a CD155 targeting chimeric antigen receptor T cell therapy for digestive system cancers

A multiscale drug discovery platform integrating human induced pluripotent stem cells, 3D-engineered heart tissues, and animal models identifies artesunate as a safe and potent antifibrotic compound.

Nature, Published online: 08 May 2024; doi:10.1038/s41586-024-07370-8

Enhanced light–molecule interactions in high-finesse fibre-based Fabry–Pérot microcavities are used to detect and profile individual unlabelled solution-phase biomolecules, leading to potential applications in the life and chemical sciences.

Nature, Published online: 12 July 2023; doi:10.1038/s41586-023-06327-7

Retrotransposons hijack the alternative end-joining DNA repair process of the host for a circularization step to synthesize their second-strand DNA.