Papers

本文从统一代谢因果场（Unified Metabolico-Causal Field）框架出发，重新审视“民科”与“官科”的二元对立。通过分析华罗庚先生的学术成长路径，论证其作为自学成才（民间科学）先驱的历史地位。进一步，基于整体论数学基础（范畴论、马尔可夫范畴、信息论），证明科学的本质在于逻辑自洽与实证检验，与知识传播渠道无关。渠道多样性是知识代谢场的健康投影，任何试图以资格认证代替逻辑审查的做法都是还原论等级制的残余。本文为“民科”正名，并倡导开放、包容、以逻辑为准绳的科学共同体。

本文是《从数学基础到系统哲学的完整理论链——范畴论下的整体论统一代谢因果场》的升级版。我们将《整体论的历史性突破》中所建立的\textbf{元基础证明}（基于ZFC集合论的真理函数定理与整体-部分对应定理）作为整个理论链的奠基性公理系统，然后在范畴论框架下将“整体是函数，部分是子函数”自然推广为预层函子语义，进而引入时空切片、代谢因果、朱--梁代谢元、权重函子等结构，最终融合为\textbf{朱--梁统一代谢因果场}。我们严格证明：统一场在截面层与代谢元逆向极限同构，代谢、生成、因果三者统一于同一存在函子（朱--梁一体性原理），并以代谢元的内生因果闭合消解“第一推动力”千年难题。本升级版彻底封死了来自还原论立场的质疑：任何还原论批评者必须首先否定元基础中的整体-部分对应定理——而这是不可能的。整体论由此获得从集合论到范畴论、从静态对应到动态演化的完整数学基础。\textbf{新增第13章}展示统一代谢因果场在数论中的深刻应用：严格证明黎曼猜想所有非平凡零点均位于临界线 $\Re(s)=1/2$，并附有完整的证明细节附录及对还原论批评的元层次驳斥。

本文在统一代谢因果场框架下，利用整体论数学的代谢元构造与逆向极限理论，严格证明哥德巴赫猜想：每个大于2的偶数都可以表示为两个素数之和。证明全程依赖于《从数学基础到系统哲学的完整理论链》\cite{zhu2026a}中建立的核心概念与定理，并将素数集合和偶数集合统一建模为代谢元，通过熵守恒、互信息极大化以及平衡态统计，严格导出偶数表示为两个素数之和的渐近公式，从而证明所有偶数均具有该表示。本证明展示了整体论数学在处理数论核心问题上的强大解释力。

本文严格论证“整体范畴论涵盖还原集合论，而非相反”这一命题。我们首先明确定义“涵盖”为语法层可解释性（interpretability）：存在一个从集合论公理体系 $\ZFC$ 到范畴论公理体系 $\CETCS$ 的递归可计算翻译，使得 $\ZFC$ 的每个定理在翻译后成为 $\CETCS$ 的定理。通过分析集合范畴 $\Set$ 作为范畴论的特例、Lawvere 的 $\ETCS$ 公理化、高阶范畴与同伦类型论的发展，以及 Yoneda 嵌入所揭示的对象由其关系网络决定的整体论本质，我们证明：范畴论在概念丰富性、表达能力和元数学基础地位上严格包含集合论，而集合论无法反向涵盖范畴论。本文的贡献包括：(1) 给出了从 $\ZFC$ 到 $\CETCS$ 的一个具体解释映射框架，并详细处理了外延性、迭代隶属与良基公理的翻译；(2) 系统比较了相关文献（$\ETCS$、代数集合论、类理论、∞-范畴编码）与本文论断的关系；(3) 通过拟范畴的编码示例量化了集合论表达的“不自然性”，并提出了可量化的衡量指标（编码长度、宇宙层数、证明复杂度）；(4) 明确了结论对宇宙公理或类理论的依赖程度，并讨论了反向不成立的精确条件。本文严格界定了整体范畴论与还原集合论在数学基础层面的区别与适用边界，不涉及两种框架在具体数学问题中的深入应用，旨在为后续合理选择或融合两种视角提供理论基础。

本文是《从数学基础到系统哲学的完整理论链——范畴论下的整体论统一代谢因果场》的升级版。我们将《整体论的历史性突破》中所建立的\textbf{元基础证明}（基于ZFC集合论的真理函数定理与整体-部分对应定理）作为整个理论链的奠基性公理系统，然后在范畴论框架下将“整体是函数，部分是子函数”自然推广为预层函子语义，进而引入时空切片、代谢因果、朱--梁代谢元、权重函子等结构，最终融合为\textbf{朱--梁统一代谢因果场}。我们严格证明：统一场在截面层与代谢元逆向极限同构，代谢、生成、因果三者统一于同一存在函子（朱--梁一体性原理），并以代谢元的内生因果闭合消解“第一推动力”千年难题。本升级版彻底封死了来自还原论立场的质疑：任何还原论批评者必须首先否定元基础中的整体-部分对应定理——而这是不可能的。整体论由此获得从集合论到范畴论、从静态对应到动态演化的完整数学基础。

本文在统一代谢因果场框架下，利用整体论数学的代谢元构造与逆向极限理论，严格证明庞加莱猜想：任何一个单连通的三维闭流形必同胚于三维球面 \(S^3\)。证明全程依赖于《从数学基础到系统哲学的完整理论链》\cite{zhu2026a}中建立的核心概念与定理，并将三维闭流形建模为代谢元，Ricci流作为代谢过程，通过熵守恒、不可约分解、逆向极限与统一场同构，导出流形必为球面。同时揭示佩雷尔曼的Ricci流证明是代谢元框架在微分几何范畴中的特例实现，两者在元逻辑上完全同构。本证明展示了整体论数学在处理几何拓扑核心问题上的强大解释力。

还原论医学将人体拆解为孤立器官、细胞、分子，试图通过局部机制解释疾病并构建治疗方案，但其根本缺陷在于忽视了生命作为代谢元的整体因果闭合性。本文基于朱--梁统一代谢因果场（Zhu--Liang unified metabolico-causal field）框架，从范畴论与信息论出发，证明人体是多层嵌套的代谢元系统，健康即因果闭合的持续，疾病即因果链的投影断裂。结合现代医学前沿实例（肠道微生物组、肿瘤免疫、糖尿病、心力衰竭、精准医疗、中医、数字孪生等），揭示整体论对医学的突破性贡献：统一东西方医学、重构治疗逻辑、指导精准医疗升级。最终结论：医学的根基只能是整体论，未来医学必须从整体出发，否则因果链必然断裂。

本文在范畴论框架下严格证明“整体是函数，部分是子函数”这一命题。运用 Yoneda 嵌入，任意对象 $X$（整体）与其可表函子 $h_X = \Hom(X,-)$ 等同，该函子可视为一种“函数”。对于子对象 $i: A \hookrightarrow X$（部分），嵌入诱导自然变换 $\res: h_X \to h_A$，其分量为限制映射 $f \mapsto f \circ i$。从而部分对应函子 $h_A$，限制自然变换精确表达了部分作为整体的子函数。该结果不依赖于任何具体数学结构，揭示了整体–部分关系的本质函子性。本文进一步在范畴论框架下证明：存在是无限因果、不可还原、是同构，将存在论奠基于精确的数学结构之上。

Recently developed fused-ring organic electron-acceptors such as Y6 have strong oscillator strength, good charge-carrier transport and low bandgaps. They therefore have enormous current technical application to optoelectronic devices, such as solar cells. Due to the large number of atoms involved in representative aggregates of these materials, we need an efficient electronic structure method to model them. Standard density functional theory poorly describe charge-transfer states, and were developed for vacuum calculations of individual molecules. In this work we tune a range-separated hybrid functional for Y6. We characterise representative dimers of the solid-state and show that Y6 dimers show the extensive solvatochromic effects are due, in part, to oscillator strength borrowing. We provide an explanation for the short optimally tuned range-separation parameter, based in the Penn model for the frequency dependent dielectric of a semiconductor. We caution that standard range-separated hybrids are less accurate than global hybrids for these, and similar, materials. We show how reducing the range-separation length improves the accuracy of standard functionals, without an involved tuning process.

在企业预算约束下，数字化投入与绿色化投入往往竞争同一笔资源，二者能否形成协同取决于技术收益能否跨部门兑现。本文基于企业层面面板数据，检验人工智能对数字化-绿色化协同转型的影响。固定效应结果显示，人工智能系数为0.0158061（p<0.01）；工具变量2SLS结果为0.0188387（p<0.01），第一阶段F值1864.52。异质性结果表明，效应主要出现在公平竞争程度较高地区和非龙头企业。机制检验显示，人工智能通过缓解信息不对称、降低融资约束和提升组织适应能力促进协同，同时提高数字风险暴露和金融化倾向。拓展结果显示，人工智能还能提升绿色创新、企业韧性与全要素生产率。本文据此提出“技术扩散-竞争治理-风险约束”协同治理框架。

本文在朱梁真理函数定理 3.0–3.8 版的基础上，引入新的奠基思路：从否定之 否定这一元逻辑出发，直接推导真理度规定理，进而导出真理函数定理与递归元公 理 A1–A4。这一新思路比原有从元事实出发的路径更彻底、更清晰，将整个理论 奠基于递归演化的内在逻辑之上。我们同时保留元事实路径作为历史背景与辅助阐 释，两条路径最终汇合于统一的朱梁渡劫递归元范式。本文系统整合了熵减推论与 真理的动态本体论内涵（时序性、整体性、可表达性、不可孤立僵化），并从度规定 理出发重新阐释了这些核心思想。文中对渡劫公理 A5 进行了严格的数学构造，证 明了递归元范畴的正合性、劫数对象的存在性及其自同构群的同构性。这些工作将 真理从静态函数提升为动态的渡劫递归元，揭示了真理在代谢过程中的矛盾消解机 制。本文所有证明均为完整形式，不依赖于外部引用。

This paper investigates a hierarchical approach to the optimal scheduling of flexibility offered as transactive energy by thermostatically controlled loads (TCLs). The two-stage scheduling framework includes the lower stage in which TCLs are aggregated as a virtual battery. The aggregated TCL power can offer the required flexibility for the upper stage with significant impacts on power system scheduling as transactive energy. Comparisons are also made between the virtual battery model of TCLs and a conventional battery model. At the lower stage, a transactive control strategy is also employed to regulate TCLs for preserving the end-user's information privacy. At the upper stage, a transactive energy market is developed in which peer-to-peer trading of the available TCL flexibility is considered among aggregators. Accordingly, TCL scheduling at power system and device levels are coordinated to regulate TCLs in a distributed fashion. The simulation results demonstrate that the scalability concerns of traditionally centralized operations are addressed by the proposed distributed alternative solution. The upper stage transactive energy market allows aggregators to trade energy effectively without any significant concerns for maintaining the information privacy. The results also point out that the lower stage virtual battery model can accurately characterize the TCL flexibility where TCLs can be effectively regulated in the proposed energy trading model.

Large 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.

Network Intrusion Detection Systems (NIDS) play a vital role in protecting digital infrastructures against increasingly sophisticated cyber threats. In this paper, we extend ODXU, a Neurosymbolic AI (NSAI) framework that integrates deep embedded clustering for feature extraction, symbolic reasoning using XGBoost, and comprehensive uncertainty quantification (UQ) to enhance robustness, interpretability, and generalization in NIDS. The extended ODXU incorporates score-based methods (e.g., Confidence Scoring, Shannon Entropy) and metamodel-based techniques, including SHAP values and Information Gain, to assess the reliability of predictions. Experimental results on the CIC-IDS-2017 dataset show that ODXU outperforms traditional neural models across six evaluation metrics, including classification accuracy and false omission rate. While transfer learning has seen widespread adoption in fields such as computer vision and natural language processing, its potential in cybersecurity has not been thoroughly explored. To bridge this gap, we develop a transfer learning strategy that enables the reuse of a pre-trained ODXU model on a different dataset. Our ablation study on ACI-IoT-2023 demonstrates that the optimal transfer configuration involves reusing the pre-trained autoencoder, retraining the clustering module, and fine-tuning the XGBoost classifier, and outperforms traditional neural models when trained with as few as 16,000 samples (approximately 50% of the training data). Additionally, results show that metamodel-based UQ methods consistently outperform score-based approaches on both datasets.

Background & aims A central pathogenic mechanism in alcohol-related liver disease (ALD) involves a disruption of the gut barrier function and thus the bidirectional communication between the gut and liver, referred as the "gut-liver axis". While gamma-aminobutyric acid (GABA) and type A GABA receptors (GABAARs) are present in the intestinal epithelium, their role in the gut-liver axis and contribution to the pathogenesis of ALD, remains poorly understood. Methods A Gao-binge mouse model of ALD, as well as Gabra1IEC-KO mice and intestinal cell lines were employed as approaches to assess the role of GABAARs-mediated signaling in ethanol-induced liver injury. Results Administration of GABA markedly improved liver function and intestinal barrier integrity in ALD mice. This improvement was associated with a downregulation of intestinal cytochrome P450 2E1 (CYP2E1) expression and a reduction of oxidative stress. These beneficial effects of GABA on intestinal integrity and liver function were substantially diminished by the GABAAR antagonist, picrotoxin. In addition, picrotoxin blocked GABA’s effect on CYP2E1 expression,, thereby preventing the attentuation of oxidative stress in the intestines of ALD mice. Moreover, when ethanol-stimulated cell models were subjected to pharmacological or genetic inhibition of CYP2E1, GABA treatment failed to produce any decrease in ROS levels. Finally, results from the intestinal epithelial-specific Gabra1 knockout mouse model demonstrated that the benifical effect of GABA on liver function in ALD is mediated by its activation of intestinal epithelial GABAARs. Remarkably, the pivotal role of intestinal CYP2E1 was robustly validated by confirming its dysregulated expression in patient-derived clinical samples. Conclusions Our results suggest that activation of intestinal GABAAR-mediated signaling reduces intestinal CYP2E1 expression and oxidative stress, thereby improving intestinal barrier function and alleviating ethanol-induced liver injury. Such findings suggest that intestinal GABA signaling offers a promising avenue for the development of novel strategies in the treatment of ALD. Powered

2024 年，可计算离散整体几何结构实验室发起了一场覆盖全国多所高校及科研机构的巡回艺术展。展览内容聚焦前沿几何拓扑理论与概念，尤其凸显各类整体几何结构。 全国巡回艺术展借助全新计算机算法、原创代码及计算机图形学渲染技术生成的图片与视频，将抽象的内蕴几何结构转化为直观的视觉呈现，并以巨幅海报的形式展出。这些展览内容的创新之处在于体现了数学家近几十年来发展的内蕴整体几何拓扑概念。目前，这场巡回艺术展已走进十余所高校，且仍在持续推进中，整个巡回展览预期将历时十年，100所高校。通过这种新颖的艺术展形式，全国多所高校不同专业的师生得以直观了解此前鲜少接触的几何拓扑概念，激发了研究兴趣，为深入探索前沿几何拓扑理论理论及其应用奠定了基础，也为理工科的各个专业，如力学、机械、计算机、物理、材料等，通过对前沿几何拓扑理论的应用进行跨学科、交叉学科的融合铺平了道理。通过此次全国巡回艺术展也就在艺术领域开拓了一个全新的“整体几何结构 几何拓扑艺术”流派。

Large language model (LLM) agents have evolved towards greater autonomy with the advancement of model context protocols. Self-controlled agents, such as Codex and Claude Code, highlight the need for novel organizational frameworks that facilitate agent-level autonomy. In this paper, we propose a tree-based orchestration system, \ourMethod, which utilizes a PUCT-style search to dynamically allocate agent actions while maintaining autonomy. This approach offers three key benefits: (i) full agent autonomy for critical tasks like planning and tool use, (ii) a generalized mechanism for inter-agent experience sharing, and (iii) scalability as the number of agents increases. We demonstrate the system’s effectiveness through the general matrix multiplication kernel optimization, achieving 80\% of the performance of the cuBLAS code. Additionally, the system exhibits a scaling phenomenon as the number of agents increases. Our approach provides a solution for organizing increasingly autonomous agents.