Papers

针对传统多智能体仿真中依赖三维体素建模（O(L³) 算力复杂度）与消息通信协议所带来的“算力墙”与“状态分叉”瓶颈，本文提出了一种名为“灵境”的新型统一信息场引擎架构。该架构受钱学森“灵境”理论与场论启发，通过引入统一信息场替代离散状态变量，利用泡壁面积律（O(L²)）实现计算复杂度的维度降级，并设计了基于版本号与统一求解的无通讯共识协议（协议6）。实验表明，本引擎在 N=32 的稀疏网格场景下，算力消耗增长仅为传统体素建模的 1/8，且能在零消息传递的情况下保证多智能体状态的 100% 确定性一致。本研究为国产算力平台承载超大规模具身智能与科学仿真提供了全新的理论支撑与工程实现路径。

ARC-AGI-3 评估的不是模式识别能力，而是流体智能——系统在面对从未见过的隐藏环境时，能否通过探索快速归纳规则、设定目标并高效执行。当前前沿 LLM 在该基准上平均分仅 0.51%，证明纯大模型推理路线在此场景下不成立。 本方案提出 Lingjing-Solo（灵境单智体框架），将"统一信息场"思想从多智能体协同场景平移至单智能体交互学习场景。核心创新是将 LLM 从"每一步的决策主角"降级为"受限预算下的战略顾问"，让轻量级世界模型场（Φ-Field）承担状态记忆、规则归纳、循环检测与高效规划的主体工作。 框架设计为五层管道：感知编码 → 世界模型场 → 探索假设 → 规划执行 → 反思触发。在 Kaggle 无网络评测约束下可优雅降级为纯规则模式。本说明书完整描述架构设计、关键算法、设计决策依据及验证方案。

本文在高维时空几何统一理论的公理体系内，构造十六维流形上泛函积分测度的数学方案，适用于纤维拓扑在相变时刻发生变化的物理情形。通过格点正规化与ζ函数正规化分别处理外部空间与紧致纤维方向的无穷维积分，建立泡壁界面上规范相容的衔接条件，并在此框架内证明陈-西蒙斯拓扑不变量的路径积分守恒性以及演化算符的半经典幺正性。本文给出半经典计算方案，包括瞬子解的鞍点近似与涨落行列式的热核展开，并列出具体的数值验证任务清单。所有未验证的假设均被明确标注，每一推导步骤的前提条件均被陈述。路径积分在此被理解为对经典几何动力学的一种数学重新表述，所用正规化方法均来自椭圆算子谱理论，不涉及引力量子化。该构造为高维时空几何统一理论中宇宙循环机制提供了严格的路径积分数学基础。

本文在高维时空几何统一理论的公理体系内，系统解决了暗能量本质与纤维模量稳定化两个关键遗留问题，严格区分了严格定理、第一原理数值方案、模型估计和开放问题四个逻辑层次。 在暗能量方面，本文严格证明了三个递进结论。第一，量子涨落的紫外发散不作为爱因斯坦方程的源项出现，它们仅重正化了一个已存在的裸参数。第二，四维弗里德曼方程中的宇宙学常数是该二阶微分方程的积分常数，其数值由初始条件或边界条件决定，而非由局域能量密度的积分产生。第三，该积分常数的数值等价于渐近德西特时空共形边界上边界条件的选择，当前观测的哈勃参数正是这一红外边界条件在有限时间的局部表现。暗能量的数值来源由此被重新定义为纯粹的红外边界条件，而非紫外发散的抵消残余。紫外有限卡西米尔残余在四维有效作用量中贡献的常数项可通过重新定义积分常数被规范吸收，不产生等级问题。 在模量稳定化方面，本文证明引入规范通量和非微扰瞬子效应后，纤维体积模量和威尔逊相位被同一有效势同时稳定。体积模量的稳定点与规范耦合的重整化群约束自洽，威尔逊相位被扭曲卡西米尔能量的全局极小值唯一锁定于最大通量态。完整纤维模量空间的所有平直方向均被破除，纤维几何是完全刚性的。 理论状态已从开放问题更新为严格闭合。剩余任务全部是工程性数值计算，不威胁理论的逻辑自洽性。本文给出明确的可证伪性预言：暗能量状态方程为严格常数，紧致化标度受明确上界约束，模量参数待数值计算完成后获得第一原理确定。

Abstract The Erd˝os–Gy´arf´as conjecture asks whether every finite simple graph of minimum degree at least three contains a cycle whose length is a power of two. Suppose that a counterexample G exists and choose one first with minimum order and, subject to that, minimum size. Write a =|V3(G)|, b =|V≥4(G)|. Building on the minimal-counterexample structure developed by Markstr¨om and Carr, we obtain two computer-assisted additive constraints on the degree distribution. First, a ≥2b+8 for every such minimal counterexample. Equivalently, |V3(G)| ≥ 2|V(G)|+8 3 Second, in the range b ≥ 14 we prove the stronger bound a ≥2b+10. . The proof suppresses certain cubic vertices to a simple 2-degenerate graph Q on the high-degree vertices and uses the exact identity a =2b+d+E+z, d=2b−e(Q), d≡z (mod2). For large b, fixed-defect extremal bounds on Q suffice. The remaining small orders are reduced to finite families. Their lift obstructions are verified using independent forbidden-cycle imple mentations, exact component enumerations, and small integer charging certificates. For the +10 theorem, the final path-only obstruction consists of 1943 static endpoint-budget certificates λ : E(Q) → {0,1,2}. Full source code, finite case data, certificates, hashes, and reproduction scripts are separated from the manuscript in the accompanying reproducibility package.

Battery-electric ferry ports are emerging as demanding nodes in maritime electrification: one shore transformer must deliver megawatt-scale charging in minute-long berth windows while serving terminal buildings, refrigerated cargo, electrified equipment, and cold-weather battery limits. We introduce QuantumFerryV2B, a real-data-grounded Quantum-AI framework that controls this coupled system as one weather-robust, thermally aware Vehicle-to-Building (V2B) problem rather than isolated charging, building, or battery subproblems. The framework combines a three-region benchmark, a physics-based energy and battery-thermal evaluator, a chance-constrained P90 weather reserve learned by a SciML surrogate, a QPU-ready QUBO day-ahead scheduler with deterministic feasibility repair, and a physics-shielded variational quantum circuit (VQC) controller. The benchmark uses Entur ferry timetables, Danish AIS traces, Washington State Ferries GTFS schedules, Open-Meteo weather and marine fields, NREL/OEDI port-load profiles, CALCE low-temperature lithium-ion data, EnergyPlus terminal simulation, and OpenModelica battery-thermal validation. Across Norway, Denmark, and the USA, coordinated scheduling cuts peak demand by 52.5--56.1\% and operating cost by 33.0--38.5\% relative to uncontrolled opportunity charging. Thermal preconditioning is necessary in cold-wave cases; the SciML P90 reserve converts brittle point-optimal schedules from 45--70\% held-out feasibility to 98--100\% robust feasibility at below 2.5\% cost premium. The QUBO scheduler matches the exact optimizer on demand peak, while the physics-shielded VQC sustains zero missed departures under IBM Heron-r2-class hardware noise with 16x slower parameter growth than a matched neural network. QuantumFerryV2B provides a reproducible benchmark and deployable Quantum-AI control architecture for robust ferry-port electrification.

本文在高维时空几何统一理论的公理体系内，以神经网络计算的几何本征值作为唯一的几何输入，完成了从额外维度几何构造到低能物理预言的完整第一原理推导。本工作严格区分四个层次：严格推导（代数恒等式）、第一原理数值计算（给定方程的唯一数值解）、模型估计（依赖标准宇宙学假设）和开放问题（框架已建立，计算待完成）。 从十六维爱因斯坦-嘉当作用量出发，本文完成规范耦合常数的完整维度约化，揭示其与总纤维体积的正确关系。规范耦合跑动与纤维几何自洽性将紧致化标度严格限制在远低于普朗克能标的范围内，彻底排除了超普朗克能标的可能性。本文证明所有费米子质量比是紧致化标度的严格不变量，完全由纤维拓扑决定。通过孤子方程数值求解得到全部费米子质量谱，未使用任何粒子物理质量作为拟合参数。本文严格证明普朗克常数的几何起源，其数值与实验偏差极小；严格证明真空能二次发散的自动消失，并报告四次发散系数与纤维拓扑积分的数值一致性。本文还严格证明玻恩规则在极端小的扩散参数下在所有物理精度内成立。 本文报告的全部可观测物理量中，仅一个混合角参数存在约一个标准差的偏差，其余均在实验误差范围内与观测一致。模量稳定化和暗能量抵消的解析严格性被明确列为后续研究方向。该理论将标准模型的多个自由参数压缩为两个几何输入，确立了从几何第一原理出发统一描述物质、力和时空的可能路径。

在“高维时空几何统一理论”的公理体系内，从十六维爱因斯坦-嘉当动力学出发，推导粒子物理标准模型的核心结构。希格斯场被识别为纤维三维球面的最低阶无迹张量球谐形变模式，其二次质量参数和四次自耦合常数由该球面的里奇曲率和挠率分布决定。本文给出里奇标量二阶变分的完整显式计算，并建立挠率模方变分的推导框架。通过阿蒂亚-辛格指数定理在乘积流形上的应用，证明手征费米子的存在是纤维拓扑的必然结果，据此给出中微子狄拉克或马约拉纳性质的几何判据。证明质子绝对稳定——其衰变被八维纤维的拓扑障碍禁止。论证超对称伙伴粒子在理论中的非必要性。所有推导均标注逻辑地位，明确区分严格证明与待完成的计算任务。

科技情报机构是国家创新体系中最古老、也最容易被低估的一类基础设施。本文以"国外经验—国内困境—现实需求—技术机遇—制度建议"为逻辑主线，系统考察科技情报机构的价值定位问题。研究发现：国外已形成"政府智库—公益法人—市场化机构"三元并存的科技情报供给格局，其共同特征是方法论产品化、数据资产化与决策嵌入制度化；而我国地方科技情报机构在历轮事业单位分类改革与政府机构改革中经历了大规模撤并重组，市级情报所普遍出现"定位漂移、主业空心、人才断层"的职能弱化现象，县区一级科技管理机构的撤并进一步切断了情报服务的末端触点。与此同时，地方产业决策对高质量技术情报的需求不降反升——近年来一批地方重大产业项目的失败，其病理并非"缺少专家意见"，而是"缺少可核验的系统性情报证据链"。本文提出：人工智能时代恰恰是最契合科技情报机构发展的时代，其长期积累的多源数据资产、合规信息获取渠道与"技术—产业—政策"三元交叉领域知识，正是通用大模型最稀缺的补充要素；情报机构应以"本地产业知识图谱＋检索增强生成＋多智能体监测"为技术骨架，完成从"报告交付"到"决策订阅"的业务模式重构。文章最后从机构定位、能力建设、制度约束、区域策略等方面提出九条建议，并特别指出：欠发达地区真正的风险不是"高科技搞不成"，而是因缺乏情报能力而"既不敢搞、又乱搞"。

本文在“高维时空几何统一理论”及其宇宙循环框架的基础上，系统解决四个深层理论问题：纤维几何的“景观”问题、多点成核的拓扑完备性问题、时间箭头与宇宙循环的兼容性问题、以及导航波诠释在宇宙尺度的普适性问题。所有解决方案均严格基于经典十六维爱因斯坦-嘉当动力学，不依赖引力量子化。本文给出四个定理的完整严格证明，并澄清“经典几何动力学非微扰定义”的方法论含义。这些定理共同确立了理论在深层逻辑上的自洽性和完备性。

本文在高维时空几何统一理论的公理体系内，完成该理论所需的全部数学严格性证明。核心成果包括：严格证明集中紧致性原理在爱因斯坦-嘉当系统中向纤维化乘积空间的完整推广，利用挠率质量项在缩放极限下的发散排除气泡、通过阿格蒙指数衰减估计排除逃逸；完整证明薛定谔方程、导航方程和玻恩规则的涌现；严格证明量子势与纤维挠率延迟回应力之间的等价性，确立量子势的几何起源。所有证明均不依赖引力量子化，不依赖外部假设，所有引用均来自经过核实的标准参考文献。

本文在“高维时空几何统一理论”的公理体系内，给出宇宙循环——从大爆炸到热寂再到新宇宙诞生——的完整数学描述。宇宙被描述为一个十六维时空几何体经历周期性几何相变的过程。本文从十六维爱因斯坦-嘉当作用量出发，严格推导了宇宙膨胀、暴胀、物质创生、晚期加速膨胀的动力学方程。通过绝热约化定理，证明了纤维演化可由单一序参量描述；通过拓扑势垒存在定理，确立了纤维有效势的双阱结构及其量子不稳定性的必然性；从协变守恒律严格导出了宇宙终结时暗能量向物质能量的转化方程；通过拓扑非定域性定理，证明了纤维相变的全局同步翻转机制，严格确立了单点成核触发全局相变、多点成核造就同一新宇宙的结论；通过泡壁拓扑守恒定理和半经典幺正性定理，证明了拓扑不变量在相变中的守恒和信息遗传的幺正性。本文导出了旧宇宙物质分布向新宇宙原初扰动谱的遗传关系，并由传递函数定理严格确立了绝热极限的成立条件。本文给出了推广的惠勒-德维特方程及其循环边界条件，并明确了其作为半经典有效描述的地位。所有定理均附有完整证明，所有假设均从公理或场方程导出。待完成的计算任务已明确列出，不影响理论的逻辑完备性。

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灵境把信息编译在时空边界上，信息根据场方程动力学实时演化，每一帧都是一个统计结果。而Agent根据自身坐标和光锥几何求解该边界的因果事实=三维展开+时序帧，然后做出决策，决策实时反馈回边界信息场，信息场继续因果演化。系统闭环，这时候多智能体获取边界信息时都可同步共识，这就是灵境引擎算力革命。

Holographic Fiber Theory (HFT) models the vacuum as an informationally discrete cell complex governed by a weaving rule: a combinatorial law for how cell states couple and update. Its first part is a trivalent connectivity whose continuum limit realises the Hopf bundle $S^3 \to S^2$. Its four cohomology channels pair by Poincar\'e duality into the substrate's two fields, a tension field and a phase field, and its homotopy classes supply the topological matter charges. Its second part is a braiding constraint on this connectivity, two-strand on each edge and three-strand at each vertex; a symmetry-breaking event locks the edge braid's chirality, fixing the gauge structure and prestressing the vacuum. Coarse-grained across cells and ordered by causal dependence, its deterministic updates read statistically as a signed-measure Markov chain, reproducing the Tsirelson bound from local, real-valued weights and delivering the path-integral measure; the same operator's short-time heat kernel returns the form of the Einstein--Hilbert curvature term, and the Lorentzian metric is stitched in the infrared by a signal speed binding tick count to spatial span. The coarse-graining mechanism is checked by accompanying scripts rather than posited. The Standard-Model mass spectrum emerges as energy topologically trapped in the prestressed tension field. Two near-term falsifiable targets from the tension floor: the summed Majorana neutrino mass, $\sum m_\nu \approx 61.2$ meV, and the cosmic neutrino background temperature $T_{{\rm C}\nu{\rm B}} = T_{\rm CMB} \approx 2.725$ K, the two backgrounds sharing one thermal fluctuation --- an irreducible entropic cost rather than a frozen relic, read in the finite-information frame this paper adopts.

We present a purely kinematic construction in which the gauge groups, field content, and matter charges of the Standard Model arise together as multiplexed readouts of a single geometric object: the Hopf bundle S³ ─S¹→ S², carrying a trivalent connectivity with a braiding on its edges and vertices. A handedness choice on the edge braid breaks parity, selecting the left-handed sector the weak factor acts on. The abelian factor is the fibre's phase holonomy, the non-abelian factors the reconnection algebras of the two-strand edge braid and the three-strand vertex braid, the colour count N_c = 3 inherited from the trivalent connectivity. The rank-two edge frame splits into a symmetric spin-2 tensor mode and an antisymmetric spin-1 field strength; spin is the tangent direction, its values fixed by framing topology; the homotopy groups π₁, π₂, π₃ supply the matter charges. The construction fixes what the fields are and how they relate, not how they evolve.

大型语言模型(LLM)和自治Agent在动态环境中需要有效的知识更新以保持准确性，但现有的知识编辑方法存在灾难性的遗忘和主题主导性的干扰--修改一个事实--不经意地破坏了相关知识。同时，因果表示学习方法通常假设静态因果结构，而不能解决知识的时间演变。我们提出了因果蜘蛛网(CSW)，这是一种将知识表示为分层因果图的分层存储体系结构，它具有三个基本创新：(1)通过不覆盖现有节点的同级萌芽协议仅附加因果更新；(2)Bayesian张力模型，将节点可信度作为原则不确定性量化的Beta分布后验，以及(3)带有上下文覆盖的自动沉淀，该模型将稳定的知识提升到不可变的核心层，同时允许在不可辩驳的新证据到达时临时掩蔽过时的公理。证明了CSW是有界的、收敛的和因果一致的，大量的实验表明，CSW在保持近零的灾难性遗忘和完全有界的活动节点增长的同时，实现了最先进的多跳一致性，有效地消除了语义湮灭。

Abstract Current large language models (LLMs) generate text through autoregressive token prediction—a fundamentally bottom-up process in which the model samples from a probability distribution over the next token, relying on statistical patterns learned from training data. While this approach has achieved remarkable success, it suffers from a structural limitation: constraints (safety, factual accuracy, logical consistency, stylistic coherence) are applied after generation, through post-hoc filtering, RLHF, or inference-time prompting. This is not a matter of "stronger constraints"—it is a matter of when and where constraints are applied. We propose a fundamentally different architecture. Drawing on the "1=N" hierarchical framework, we invert the generation direction: the top-level semantic goal (the "1") defines the legitimate generation space a priori, and generation proceeds within this constrained space. A dynamic Constraint Strength parameter (λ) and a real-time Feedback Signal (δ) form a closed-loop control system that continuously monitors and adjusts the generation process. When novel, effective patterns emerge during generation, they are consolidated into new constraint layers—creating a recursive structure in which each generation step both produces output and updates the rules that govern subsequent output. This paper presents the architectural blueprint: the conceptual foundation, the core mechanisms, the formal definitions, and a preliminary implementation framework. We position this work against existing approaches (RLHF, Constitutional AI, constrained decoding) and outline a pathway toward empirical validation.

We present a systematic and deliberately conservative synthesis of a 258-gate research archive on four-dimensional gravity, Batalin–Vilkovisky (BV) quantization, Bondi–Metzner–Sachs (BMS) boundary symmetry, infrared domains, and operator-theoretic completion. The archive contains exact finite checkers for each gate, but a finite checker is not identified with a proof of an infinite-dimensional theorem. We therefore separate four epistemic classes: (i) theorem-level statements whose hypotheses and analytic domains are fully stated; (ii) conditional theorems; (iii) finite algebraic or numerical skeletons; and (iv) open or retracted extrapolations. The strongest audited results are: the two-helicity quotient of linearized Einstein gravity; positive free graviton quantization away from zero momentum; the classical BV master equation and a local, formal causal-renormalization chain; a detailed classical BV–BFV/BMS boundary construction in specified jet and edge categories; a massless radiation Hilbert complex; a sharp collinear Sobolev trace theorem; and several no-go results showing that the standard angular L2 Fock representation does not support the actual order-zero collinear nonlinear block as a closable operator. For the diagonal trace Dt:Ht(S2)⊗2Ht(S2)⟶Ht(S2) we prove the sharp trichotomy t≤12:nonclosable,12<t≤1:closable but unbounded,t>1:bounded. Pure supertranslation phases intertwine exactly on the intrinsic collinear incidence measure, while a scalar complementary-series norm leaves only the candidate window 1/2<τ<1; that window is not an actual nonlinear Einstein–BV module. The audit also finds decisive failures in a later imported chain. In particular, a Lagrangian Weyl subalgebra was incorrectly treated as central; a nonconstant supertranslation multiplier was incorrectly assumed to commute with angular smoothing; an order-minus-two resolvent was incorrectly declared trace class on S2; and a sharp-cutoff compression proposal, tail-probability bounds, and uncertainty upper bounds were overinterpreted as self-adjoint truncation, dynamical barriers, and operational discreteness. Consequently the archive does not prove a one-loop boundary QME, an interacting BMS charge algebra, infrared-complete scattering, asymptotic completeness, black-hole information recovery, or complete quantum gravity. The paper includes the proof routes, dependency graph, failure modes, and a gate-by-gate evidence atlas needed for independent reconstruction.

We present SOLA (Streaming Online Learning Architecture), a theoretically grounded framework for transformer-based language models that continuously learn from streaming data without catastrophic forgetting. SOLA integrates three key innovations: 1. MoE Transformer — Specialized feed-forward experts process distinct input patterns, enabling modular knowledge acquisition where different experts handle different types of content. 2. Fast-Weight Hypernetwork (FWH) — A small learned network that produces LoRA-style weight deltas for expert parameters in a single forward pass, replacing gradient descent for online adaptation. Think of it as an "amortized optimizer": instead of computing costly gradients, the FWH directly predicts useful weight changes from the hidden states it observes. 3. Expert Lifecycle Manager (ELM) — A theoretical mechanism that dynamically spawns, prunes, and merges experts in response to input novelty. When the model encounters persistently surprising data that existing experts can't handle well, the ELM spawns new experts — enabling the model to grow from 1B to 10B parameters as it accumulates knowledge. When experts become redundant or underutilized, they're merged or pruned. We provide theoretical regret bounds for online learning with expanding hypothesis classes, analyze the plasticity-stability trade-off in modular architectures, and derive capacity-growth scaling laws extending the Chinchilla framework to growing models. The FWH implements MAML-style meta-training via torch.func.functional_call , enabling gradient flow through weight updates without in-place parameter mutation. A proof-of-concept implementation at 30M parameters validates the core mechanisms on WikiText-2. Our analysis shows that modular expert growth combined with amortized weight updates provides a principled path toward truly continual language models that improve with every interaction — rather than remaining frozen snapshots of their training data.