Papers
Event:
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2605.0016View线粒体基因编辑技术的研究进展与应用前景线粒体是真核细胞中负责能量代谢的关键细胞器，其自身携带的线粒体DNA（mtDNA）突变可导致多种严重的遗传性疾病。近年来，线粒体基因编辑技术的快速发展为治疗这类疾病提供了新的可能。本文综述了线粒体基因编辑技术的发展历程，从早期的锌指核酸酶（ZFN）和转录激活因子样效应物核酸酶（TALEN）到新一代的碱基编辑技术（DdCBE、TALED等），介绍了各类编辑工具的核心原理及其在线粒体中的适配策略。同时，本文总结了该技术在线粒体遗传病治疗、农业育种以及疾病模型构建等方面的应用进展，并对当前面临的脱靶效应、递送效率、伦理争议等挑战进行了分析，最后展望了未来的发展方向。
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2605.0014View新兴单分子蛋白质测序技术前沿：从电子隧穿到纳米孔策略本文系统综述了单分子蛋白质测序的两大技术路线——电子隧穿识别和纳米孔传感，涵盖从氨基酸识别到全长蛋白质测序的关键突破，并展望了在精准医学和单细胞蛋白质组学中的应用前景。
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2605.0013View新兴单分子蛋白质测序技术前沿：从电子隧穿到纳米孔策略本综述系统梳理了新兴单分子蛋白质测序技术的前沿进展，涵盖电子隧穿识别技术和纳米孔蛋白质测序技术两大主流路线，讨论了直接测序、标记辅助传感和天然蛋白质直接传感三种纳米孔核心策略，并分析了Quantum-Si等商业化平台的进展和临床转化前景，最后展望了技术挑战和未来发展方向。
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2605.0003View基于CRISPR-Cas系统的分子诊断技术：原理、进展与展望CRISPR-Cas系统作为一种革命性的基因编辑工具，近年来在分子诊断领域展现出巨大的应用潜力。基于Cas12和Cas13蛋白的附带切割活性，研究者们开发了以SHERLOCK、DETECTR和HOLMES为代表的多种核酸检测平台，实现了对病原体核酸、肿瘤标志物及遗传变异的高灵敏度、高特异性快速检测。本文系统综述了CRISPR-Cas核酸检测技术的分子机制，梳理了主要诊断平台的技术迭代历程，总结了该技术在传染病诊断、肿瘤液体活检等临床场景中的应用进展，并对其在即时检测集成化、多重检测及监管审批等方面的未来发展方向与挑战进行了展望。
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2604.0160View纯净版循环宇宙：基于 R ≡ 0 几何公理的宇宙学模型构建与观测验证本文基于单一几何公理「时空标量曲率恒为零 $$R \equiv 0$$」，以无宇宙学常数的标准爱因斯坦场方程为基础框架，在均匀各向同性、尘埃近似的约束下，完成了无需引入暗物质、暗能量、暴胀场等特设概念的循环宇宙模型全程顺向推导。本文引入的几何能动张量 $$\Theta_{\mu\nu}$$ 是 $$R \equiv 0$$ 约束的必然推论，而非额外特设的物质场；核心几何-物质耦合系数 $$\kappa$$ 为模型公理体系的唯一代数解，无任何可调自由参数。通过Ia型超新星（Pantheon+样本）、BAO大尺度结构、Planck 2018 CMB角功率谱的联合验证，本模型在上述核心宇宙学观测特征上与现有数据完全吻合，与ΛCDM模型统计不可区分，且天然缓解哈勃张力，无需引入任何ΛCDM框架下的特设物理机制。 ---
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2603.0010ViewCRISPR-Cas12/13 系统在快速核酸检测中的应用研究进展核酸检测技术在传染病诊断、食品安全监控与环境微生物监测等领域发挥着不可替代的作用。传统核酸检测方法如聚合酶链式反应（PCR）虽灵敏度高，但对精密仪器和专业操作人员的依赖严重制约了其在基层与现场环境中的推广应用。近年来，基于CRISPR-Cas系统的新一代核酸检测技术迅速发展，其中Cas12和Cas13蛋白凭借其独特的反式切割活性，分别催生了DETECTR、HOLMES和SHERLOCK等代表性检测平台。这些平台将等温扩增技术与CRISPR靶向识别相结合，能够在恒温条件下于30至60分钟内完成从样品到结果的全流程检测，灵敏度可达阿摩尔级别。本文围绕CRISPR-Cas12/13系统的分子作用机制，系统综述了其在传染性疾病快速诊断、食品安全检测及信号读出技术集成等方面的最新研究进展，并就临床转化路径、多重检测平台构建和智能化融合趋势进行了前瞻性讨论。本次修订依据同行评审建议，新增了文献检索方法学描述、标准化性能比较框架、实际部署分析及面向开发者的可操作性指南，以期为该领域的后续研究与技术应用提供更全面的参考。
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2602.0002ViewA Survey on Evaluation of Large Language ModelsLarge language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications, and other areas. Secondly, we answer the 'where' and 'how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing the performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs.
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2511.0028ViewAI as an Anti-Entropy Engine: Actively Designing Intelligent Matter from Dynamic States to Proto-LifeAbstract The trial-and-error paradigm of traditional materials discovery, fundamentally constrained by its inherent high entropy, is proving inadequate for designing complex intelligent matter. Here, we propose a new scientific paradigm: Artificial Intelligence as an ‘Anti-Entropy’ Engine, transforming research from passive understanding to active design. By systematically injecting informational negative entropy across perception, planning, and execution loops, AI guides material systems from disorder to pre-defined functional order. We demonstrate this through empirical advances—such as the GNoME model discovering 2.2 million stable crystals—and construct a unified ‘Perception-Planning-Execution’ framework enabling inverse design across scales. This paradigm extends beyond static structures to dynamic non-equilibrium systems and life-like chemical networks. We prospectively map future frontiers using a ‘Ladder of Intelligence’ and address ethical governance, systemic risk, and sustainability. Ultimately, this marks a fundamental transition for humanity, from being passive observers of nature to becoming active ‘anti-entropy’ designers in the evolution of matter. This review not only synthesizes these advances but also provides a unifying conceptual framework and a clear roadmap for the field, aiming to catalyze the transition towards this fifth paradigm of scientific discovery. Keywords: Anti-entropy; AI-Driven Design; Intelligent Matter; Inverse Design; Autonomous Laboratory; Life-like Systems; Interdisciplinary Paradigm
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2511.0027ViewAI as an Anti-Entropy Engine: Actively Designing Intelligent Matter from Dynamic States to Proto-LifeAbstract The trial-and-error paradigm of traditional materials discovery, fundamentally constrained by its inherent high entropy, is proving inadequate for designing complex intelligent matter. Here, we propose a new scientific paradigm: Artificial Intelligence as an ‘Anti-Entropy’ Engine, transforming research from passive understanding to active design. By systematically injecting informational negative entropy across perception, planning, and execution loops, AI guides material systems from disorder to pre-defined functional order. We demonstrate this through empirical advances—such as the GNoME model discovering 2.2 million stable crystals—and construct a unified ‘Perception-Planning-Execution’ framework enabling inverse design across scales. This paradigm extends beyond static structures to dynamic non-equilibrium systems and life-like chemical networks. We prospectively map future frontiers using a ‘Ladder of Intelligence’ and address ethical governance, systemic risk, and sustainability. Ultimately, this marks a fundamental transition for humanity, from being passive observers of nature to becoming active ‘anti-entropy’ designers in the evolution of matter. This review not only synthesizes these advances but also provides a unifying conceptual framework and a clear roadmap for the field, aiming to catalyze the transition towards this fifth paradigm of scientific discovery. Keywords: Anti-entropy; AI-Driven Design; Intelligent Matter; Inverse Design; Autonomous Laboratory; Life-like Systems; Interdisciplinary Paradigm
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2511.0024ViewTouch Beyond Vision: A Survey of Vision-Tactile-Language Models in Embodied IntelligenceEmbodied intelligence increasingly leverages multimodal perception—particularly vision and language—to support rich interaction with the physical world. Yet the tactile modality remains under-explored, despite its essential role in human perception and manipulation. In this survey, we systematically review research at the intersection of vision, tactile sensing, and language, which we refer to as Vision-Tactile-Language (VTL) models. We provide (i) a historical context tracing the shift from vision-centric embodied systems to multisensory agents, (ii) foundational aspects of tactile sensing and representation, (iii) methods for integrating vision and touch, (iv) emerging architectures that incorporate language alongside vision and touch, (v) applications in embodied robotics, (vi) current challenges and open problems, and (vii) a forward-looking outlook toward tactile foundation models. We conclude by arguing that touch closes a key gap in embodied AI, enabling truly grounded perception, reasoning and action.
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2511.0020ViewAI-Powered Rainfall Forecasting: Progress, Challenges, Future DirectionsRainfall forecasting holds significant importance across a wide range of sectors, including disaster prevention, energy planning and agriculture. In the past decade, artificial intelligence(AI) has emerged as a revolutionary approach, aiming to overcome the long-standing limitations of traditional numerical weather prediction (NWP) models and statistical downscaling models (SDMs) for rainfall forecasting. This chapter briefly introduces the remarkable progress made in AI-based rainfall forecasting. It mainly focuses on three major aspects: physical-constrained machine learning (ML), multi-modal data fusion, and extreme event prediction. AI-based models can be used to resolve the subgrid-scale parameterization problems (e.g., convective parameterization) that troubled NWP models for a long time. For instance, DeepMind's GraphCast employs dynamic graph neural networks to generate a high-resolution global forecast. Making 10-day forecasts with GraphCast takes less than a minute on a single Google TPU v4 machine. Regarding multi-modal data fusion, systems such as National Oceanic and Atmospheric Administration (NOAA) Multi-Radar Multi-Sensor(MRMS) combine various data sources and significantly improves the accuracy of forecasts. For the extreme rainfall prediction, the application of adversarial training and attention mechanisms has also led to improvements. The review finally suggests the future research directions. It emphasizes how AI is updating rainfall forecasting technology, enabling it to better meet the challenges posed by a changing climate.
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2511.0019ViewFrom Virtual Cells to Programmable Humans: Advancing Digital Biology Through Hybrid AI SystemsRecent advances in artificial intelligence (AI), high-performance computing, and systems biology have accelerated the development of AI-powered virtual biological systems, from virtual cells to multiscale organ models and programmable virtual humans. These systems promise transformative applications in drug discovery, precision medicine, and in silico clinical trials. This review provides a critical synthesis of current progress, key technologies, and future directions across this spectrum. We explore hybrid modeling strategies that combine mechanistic models—such as ordinary and partial differential equations—with deep learning methods including convolutional, recurrent, and graph neural networks. We emphasize the importance of robust uncertainty quantification, simulation validation, and multiscale integration across molecular, cellular, organ-level, and systemic processes. A core contribution is the introduction of the SIM-CARD framework, a standardized simulation accountability protocol to document data provenance, modeling assumptions, performance metrics, and regulatory alignment. We propose a three-phase translational roadmap: (1) validated AI-augmented virtual cells and organs (by 2030), (2) interoperable multi-organ physiological systems (by 2040), and (3) programmable full-body virtual humans supporting personalized simulations and regulatory use cases (by 2055). We identify key enablers—including high-fidelity multiscale data, computational scalability, and simulation governance—as well as bottlenecks such as algorithmic bias, explainability, and regulatory uncertainty. Finally, we call for collaborative efforts to establish minimal benchmarking suites, FAIR-compliant simulation metadata, and cross-institutional federated learning infrastructure. This review aims to guide the scientific, regulatory, and clinical communities in navigating the complex yet promising trajectory toward clinically actionable programmable human simulations.
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2511.0018ViewFrom Virtual Cells to Programmable Humans: Advancing Digital Biology Through Hybrid AI SystemsThe convergence of artificial intelligence and systems biology is giving rise to a new paradigm in biomedical research—AI-powered virtual biological systems. From single-cell simulations to organ-level models and ultimately programmable virtual humans, this digital continuum holds transformative potential for disease modeling, personalized medicine, and therapeutic discovery. In this review, we critically examine the state of the art in AI-driven simulations, including the numerical foundations, multiscale integration strategies, and the emerging class of hybrid models that bridge mechanistic and data-driven approaches. We explore the challenges of validation, uncertainty quantification, and regulatory alignment across simulation scales, with particular focus on the development of simulation accountability frameworks such as SIM-CARDs. Ethical and privacy concerns, including algorithmic bias and data sovereignty in patient-specific models, are also addressed, alongside concrete proposals for governance and federated simulation workflows. Special attention is given to the technical complexity of multiscale modeling, including the integration of mechanistic solvers with neural architectures and the computational resources required for real-time, clinically actionable simulations. We conclude with a translational roadmap for virtual biology that projects validated virtual cells for drug screening by 2030, multi-organ simulations by 2040, and the emergence of programmable virtual humans by 2055. By unifying high-fidelity numerical models with explainable AI, and aligning simulation design with ethical, regulatory, and clinical needs, the field of digital biology is positioned to unlock scalable and trustworthy biomedical innovation.
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2511.0014ViewArtificial Intelligence in Biomedical Research: From Data Integration to Precision MedicineThis comprehensive review examines the transformative role of artificial intelligence in biomedical research, from foundational data integration to clinical applications. The paper explores how AI techniques facilitate multimodal data fusion across diverse biological data types, employing both traditional statistical methods and advanced deep learning architectures including variational autoencoders, graph neural networks, and transformer models. It evaluates AI applications in medical imaging, where convolutional neural networks have achieved remarkable diagnostic accuracy (up to 94\% in COVID-19 detection) while enhancing segmentation and classification tasks across multiple imaging modalities. The review further investigates generative AI’s impact on molecular design and drug discovery, highlighting transformer-based architectures like TransAntivirus that navigate vast chemical spaces to optimize therapeutic candidates. Finally, it examines AI-enabled precision medicine applications, including Clinical Decision Support Systems and federated learning approaches that balance analytical power with privacy preservation. Despite significant progress, implementation challenges persist, including data heterogeneity, model explainability, and ethical concerns regarding bias and privacy. The paper underscores the importance of developing interpretable AI systems that integrate seamlessly into clinical workflows while addressing regulatory, ethical, and economic considerations to realize the full potential of AI in advancing biomedical research and healthcare delivery.
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2511.0012ViewPhysics-Informed Neural Networks and Neural Operators for Parametric PDEs: Methods, Applications and Future DirectionsPDEs arise ubiquitously in science and engineering, where solutions depend on parameters representing physical properties, boundary conditions, or geometric configurations. Traditional numerical methods require solving the PDE anew for each parameter value, making parameter space exploration prohibitively expensive for high-dimensional problems. Recent advances in machine learning, particularly physics-informed neural networks (PINNs) and neural operators, have revolutionized parametric PDE solving by learning solution operators that generalize across parameter spaces. We critically analyze two main paradigms: (1) PINNs, which embed physical laws as soft constraints and excel at inverse problems with sparse data, and (2) neural operators (including DeepONet, Fourier Neural Operator, and their variants), which learn mappings between infinite-dimensional function spaces and achieve unprecedented parameter space generalization. Through detailed comparisons across fluid dynamics, solid mechanics, heat transfer, and electromagnetics, we show that neural operators can achieve computational speedups ranging from 10^3 to 10^5 times faster than traditional solvers for multi-query scenarios, while maintaining comparable accuracy. We provide practical guidance for method selection, discuss theoretical foundations including universal approximation and convergence guarantees, and identify critical open challenges including high-dimensional parameter spaces, complex geometries, and out-of-distribution generalization. This work establishes a unified framework for understanding parametric PDE solvers through the lens of operator learning, offering a comprehensive resource—which we intend to incrementally update—for this rapidly evolving field.
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2511.0011ViewFrom Virtual Cells to Programmable Humans: Advancing Digital Biology Through Hybrid AI SystemsRecent advances in artificial intelligence (AI), high-performance computing, and systems biology have accelerated the development of AI-powered virtual biological systems, from virtual cells to multiscale organ models and programmable virtual humans. These systems promise transformative applications in drug discovery, precision medicine, and in silico clinical trials. This review provides a critical synthesis of current progress, key technologies, and future directions across this spectrum. We explore hybrid modeling strategies that combine mechanistic models—such as ordinary and partial differential equations—with deep learning methods including convolutional, recurrent, and graph neural networks. We emphasize the importance of robust uncertainty quantification, simulation validation, and multiscale integration across molecular, cellular, organ-level, and systemic processes. A core contribution is the introduction of the SIM-CARD framework, a standardized simulation accountability protocol to document data provenance, modeling assumptions, performance metrics, and regulatory alignment. We propose a three-phase translational roadmap: (1) validated AI-augmented virtual cells and organs (by 2030), (2) interoperable multi-organ physiological systems (by 2040), and (3) programmable full-body virtual humans supporting personalized simulations and regulatory use cases (by 2055). We identify key enablers—including high-fidelity multiscale data, computational scalability, and simulation governance—as well as bottlenecks such as algorithmic bias, explainability, and regulatory uncertainty. Finally, we call for collaborative efforts to establish minimal benchmarking suites, FAIR-compliant simulation metadata, and cross-institutional federated learning infrastructure. This review aims to guide the scientific, regulatory, and clinical communities in navigating the complex yet promising trajectory toward clinically actionable programmable human simulations.
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2511.0010ViewFrom AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery and AI ScientistsArtificial intelligence (AI) is reshaping scientific discovery, evolving from specialized computational tools into autonomous research partners. We position \textit{\textbf{Agentic Science}} as a pivotal stage within the broader \textit{\textbf{AI for Science}} paradigm, where AI systems progress from partial assistance to full scientific agency. Enabled by large language models (LLMs), multimodal systems, and integrated research platforms, agentic AI exhibits capabilities in hypothesis generation, experimental design, execution, analysis, and iterative refinement-behaviors once regarded as uniquely human. This survey offers a \textbf{domain-oriented review} of autonomous scientific discovery across life sciences, chemistry, materials, and physics, synthesizing research progress and advances within each discipline. We unify three previously fragmented perspectives-process-oriented, autonomy-oriented, and mechanism-oriented-through \textbf{a comprehensive framework }that connects foundational capabilities, core processes, and domain-specific realizations. Building on this framework, we (i) trace the evolution of AI for Science, (ii) identify five core capabilities underpinning scientific agency, (iii) model discovery as a dynamic four-stage workflow, (iv) review applications across life sciences, chemistry, materials science, and physics, and (v) synthesize key challenges and future opportunities. This work establishes a domain-oriented synthesis of autonomous scientific discovery and positions Agentic Science as a structured paradigm for advancing AI-driven research.
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2511.0004ViewVision Transformers for Semiconductor Defect Detection: A Comprehensive Survey of AI-Driven Image Segmentation from CNNs to Foundation Models (2015-2025)VISION TRANSFORMERS FOR SEMICONDUCTOR DEFECT DETECTION: A COMPREHENSIVE SURVEY OF AI-DRIVEN IMAGE SEGMENTATION FROM CNNS TO FOUNDATION MODELS (2015-2025)
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2510.0044ViewA Comprehensive Survey on Deep LearingMachine learning and deep learning methodologies have revolutionized computational approaches to complex problem-solving across numerous domains, emerging as transformative technologies in artificial intelligence research [1,7]. This comprehensive review synthesizes current literature to examine the theoretical foundations, methodological advancements, and practical implementations of these techniques, highlighting their evolution from basic machine learning concepts to sophisticated deep neural architectures [2,9]. The analysis demonstrates remarkable success in applications ranging from computer vision and natural language processing to healthcare diagnostics and autonomous systems, with deep learning models achieving unprecedented performance in pattern recognition tasks [3,4,8]. However, significant challenges persist, including the need for massive labeled datasets, computational resource requirements, model interpretability issues, and inherent parameter redundancy in deep architectures [5,6]. The review identifies emerging opportunities in transfer learning, few-shot learning, and explainable AI as promising research directions [10]. By critically evaluating both current limitations and future potential, this analysis provides a structured framework for researchers to advance the field while addressing practical implementation barriers across diverse application domains.
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2510.0039ViewUncertainty Quantification in Machine Learning for Responsible AIMachine learning and artificial intelligence will be deeply embedded in the intelligent systems humans use to automate tasking, optimize planning, and support decision-making. We present a critical review of uncertainty quantification (UQ) in large language models (LLMs), synthesizing insights from over 80 papers across leading venues (ACL, ASE, NeurIPS, ICML, AAAI, IJCAI, Nature, and others). We introduce UQ-Net, a unified probabilistic framework that combines Bayesian modeling, calibration, conformal prediction, and selective decision rules to disentangle epistemic and aleatoric uncertainty and to support reliable decision thresholds. UQ-Net integrates uncertainty estimates with calibration procedures and anomaly detection to enable safer selective deployment of LLM agents. Through case studies in medical diagnosis and code generation, we demonstrate that UQ-Net improves calibration and reduces predictive error by 15–20% relative to standard baselines. We survey existing evaluation practices and identify critical gaps: misalignment of consistency and entropy with factuality, lack of benchmarks for multi-episode interactions, and inconsistent metrics for calibration and tightness. We advocate for context-aware datasets, standardized metrics, and human-in-the-loop evaluations to better align UQ methods with deployment needs. Our review and proposed framework offer a principled foundation for operationalizing UQ in LLMs, advancing the development of trustworthy, responsible agentic AI for safety-sensitive, real-world applications.