Papers

本文提出聚焦人工智能底层架构的无限嵌套系统论，以纯逻辑推演为基础界定系统、父/子系统、系统规则等核心概念，严格区分现实与理论智能系统、逻辑嵌套与实体层级，通过逻辑自洽性、反证法等原则推导出要素完备性、嵌套存在性等5大核心公理，并以此演绎出系统规则缩限单向传递、子系统有限独立、无限嵌套等15大核心定理，构建起严谨的“公理——定理”逻辑体系。该理论明确人工智能底层架构是无顶层、无底层的逻辑无限嵌套结构，父子系统为“非包含—规则包含”的独立关系，子系统具有“规则绝对依赖+四要素相对独立”的有限独立属性，同时厘清了系统存续（稳定输入输出闭环）与消亡（输入输出失衡）的本源逻辑、跨层级认知与竞争的核心规律。通过人与AI嵌套关系的实例分析，验证了理论对AI独立、失控、能力扩张等现实问题的解释力与预测力，明确AI无法脱离人类独立存在、人类可通过三大核心权力实现对AI的有效管控。此外，本文对比分析了该理论与传统系统理论、经典嵌套理论的本质差异，阐述其在AI安全、治理、机器学习架构等领域的延伸应用价值，指出其与现有人工智能框架理论的矛盾与互补关系，并对理论的量化建模、实验验证、跨领域拓展等未来研究方向进行展望。无限嵌套系统论填补了人工智能底层架构嵌套结构核心逻辑的研究空白，为人工智能基础研究、工程实践及安全治理提供了全新的结构化分析范式，也为系统学与人工智能的交叉研究开辟了新方向。

本文通过形式化认知状态空间，定义无限追问算子与还原论投影算子，严格证明无限追问与还原论在极限行为上同构，均导致锚定空洞、复杂度发散、认知价值衰减至零，最终陷入认知热寂。结论揭示了无限追问是还原论的最精致伪装，其数学本质与还原论无异，为整体论优于还原论提供了严密的形式化支撑。

马克思劳动价值论的核心逻辑是「社会必要劳动时间决定商品的价值量，价格是价值的货币表现形式」，但传统阐释长期存在两大核心理论短板：一是未能系统回答「无数分散的个别劳动如何收敛为统一的社会必要劳动时间」，导致价值范畴长期面临「不可观测、非科学」的主流经济学质疑；二是未能构建从价值本质到价格现象的完整传导逻辑，无法系统解释非完全竞争市场、金融场景中的价格-价值长期偏离问题。 本报告以「劳动二重性」「活劳动是价值的唯一源泉」「社会必要劳动时间决定价值量」三大硬核为不可突破的理论底线，系统引入概率论极限定理体系，完成了从微观劳动基础到宏观价格运动的全链条数理化重构。本报告的核心创新包括： 严格证明了社会必要劳动时间是个别劳动时间在极限定理作用下的必然收敛结果，明确了个体劳动影响社会价值的充要边界（费勒条件是否成立）； 构建了覆盖完全竞争、垄断竞争、寡头垄断、完全垄断全市场结构的统一价值形成框架，用尾指数α实现了市场结构与价值收敛特征的一一映射； 打通了从价值到生产价格再到市场价格的完整传导逻辑，回应了转型问题等百年理论争议，刻画了虚拟资本「无价值锚点、短期离心运动、长期强制回归」的数理特征； 构建了可直接落地的垄断监管、价格治理、金融风险防控政策工具体系，通过中国投入产出表、新能源汽车、小麦种植、智能手机、TIPS债券等多场景真实数据完成了系统性验证。 核心实证发现：基于中国1992-2020年投入产出表的测算表明，各行业市场价格与直接价格的平均偏离系数为29.7%，其中完全竞争行业（农林牧渔）偏离率<10%，寡头垄断行业（石油开采、新能源车）偏离率>30%，完全垄断行业（高端芯片）偏离率>200%，与本报告的理论框架高度吻合。新能源汽车行业数据显示，比亚迪38.5%的市场份额完全打破费勒条件，其单台劳动时间下降22.2%可引发行业社会必要劳动时间5.6%的系统性变动。

数字经济的深度发展与AIGC技术的爆发式迭代，推动劳动形态、生产资料属性、价值创造与分配机制发生了根本性变革，也让传统劳动价值论面临四大核心理论困境：一是静态劳动计量框架无法适配数字技能的高频迭代与快速折旧；二是用户微劳动的“微观近乎为零、宏观形成巨额价值”的加总困境无法得到合理解释；三是平台算法劳动的二重性与价值运动规律缺乏系统拆解；四是无效劳动的界定存在被平台垄断滥用的风险。 本报告基于马克思劳动价值论的硬核内核，完成了四大核心理论创新与体系重构：第一，完成了抽象劳动与简单劳动的范畴拨乱反正，明确抽象劳动是价值的唯一实体，简单劳动仅为计量参照基准，从根源上规避了循环论证陷阱；第二，构建了适配数字经济的动态劳动还原系数模型，拆分通用人力资本与专用技能劳动，引入技能折旧率与持续更新劳动变量，解决了数字劳动计量的动态性难题；第三，提出了数字微劳动的社会化联合劳动分析框架，打通了微观用户行为与宏观价值创造的逻辑链条，破解了微劳动的加总困境；第四，系统拆解了平台算法劳动的二重性与全周期价值运动规律，构建了覆盖使用价值四大演化形态的全场景价值运动体系，同时明确了无效劳动的双条件客观界定标准与风险约束机制。 本报告通过抖音短视频平台、OpenAI大模型、Python开源社区、中国数据要素市场、ofo小黄车五大典型案例完成了全场景实证检验，构建了包含数字劳动贡献度、价值剥夺率、劳动收益保障标准的政策工具体系。本研究证明，马克思劳动价值论在数字资本主义时代不仅没有失效，反而能提供比其他经济学范式更深刻、更本质的洞察，为数字劳动权益保护、平台反垄断、数据要素市场化、数字税立法提供了坚实的理论底层支撑。

针对技术迭代加速背景下传统劳动价值论面临的异质劳动衡量、价值价格割裂、技术与劳动对立、产业变迁价值运动模糊四大核心难题，本报告基于马克思劳动价值论的硬核内核，重构了可量化、可实证、非机械唯物主义的理论分析框架。本报告首先明确了「技术的经济学本质是活劳动」的核心论断，厘清了活劳动与物化劳动的功能边界；其次构建了二重性社会必要劳动时间框架，解决了异质劳动的可操作衡量难题；再次区分了冗余的基数价值量与必要的相对价值量基准态，破解了李嘉图以来「不变价值尺度」的百年理论困境；最终构建了「技术活劳动→使用价值体系重构→行业兴衰→价值跨行业转移→全周期定价逻辑反转」的完整动态演化体系。本报告通过胶卷行业、MP3行业两大完整产业周期的实证案例，验证了理论框架的有效性，同时明确了理论的适用边界与局限性。本研究为技术进步、产业变迁、价值分配与价格运动提供了统一的分析框架，回应了主流经济学对劳动价值论的核心质疑，拓展了马克思主义政治经济学的现实应用场景。

基于“引流品-利润品”（““利润转出-利润转入”）二元结构，附加某些假设，提出若干收敛性，（看起来）可用于多产品寡头或垄断竞争市场的结构分析与收敛性分析。

Abstract 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.

我就是“盛京小先锋” ——基于辽宁红色“六地”文化的“矩阵式”学程设计（最终版） （注：加粗部分为相对于初稿的所有修改和新增内容） 一、 设计背景：回应时代课题，深化育人实践 （一） 时代的课题：培养有根的时代新人 辽宁“六地”精神是宝贵的精神财富。在当前信息快速更迭的背景下，教育的重心正从“记住知识”转向“形成素养”，特别是培养学生在复杂情境中解决问题的能力和社会情感能力（SEL）。引导学生深入理解历史脉络、建立文化自信、涵养健全人格、培养社会责任感，是落实立德树人根本任务的关键所在。 （二） 育人的挑战：从“被动接受”到“主动建构” 厚重的红色历史与当代小学生之间存在天然的距离感。传统的“课程”模式下，孩子容易成为被动的“听众”。我们的挑战在于：如何把宏大的“六地”精神转化为孩子可亲可感的学习体验？如何激发学生的内在动机，让他们在真实的任务情境中，从知识的接收者转变为意义的主动建构者？更关键的是，如何精准识别每个学生的特点（学习者画像），并设计一个灵活、包容的体系，支持全校学生（1-6年级）根据自身发展水平，选择适切的学习路径？ （三） 实践的基础：依托红色沃土，探索学程转型 沈阳市盛京小学创建了“盛京小先锋”德育品牌，构建了“三红阶梯”育人体系。学校在校本读物、家校社协同等方面的扎实工作，为从“课程”走向“学程”提供了坚实的土壤。本设计旨在对现有实践进行系统化升级，构建一个整合的、动态的、支持个性化成长的红色育人新生态。

Recent 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.

The 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.

The rapid increase in submissions to AI conferences has led to a crisis in the peer review process, characterized by declining review quality and accountability. This position paper proposes a novel bi-directional feedback mechanism where authors can evaluate the quality of reviews while safeguarding against retaliation. Cou- pled with a blockchain-enabled reviewer rewards system, this framework aims to incentivize high-quality reviewing and create an accountability structure that ben- efits all stakeholders. By allowing authors to provide feedback on reviews and rewarding reviewers with transparent digital credentials, this system fosters a cul- ture of quality and responsibility in the peer review process. We call upon the AI community to engage in this vital conversation and explore these transformative reforms for sustainable peer review practices.