# LLM Wiki

> 知识索引页面 — 自动生成
> 最后更新：2026-05-15 | 总页面数：335

## Concepts

- [[adaptive-computation-time]] — 根据输入难度动态调整计算量的技术族（ACT, PonderNet 等）
- [[additive-combinatorics]]
- [[agent-communication-stack]]
- [[agent-mediated-deception]]
- [[agent-network-memory-scope]]
- [[agent-network-taxonomy]]
- [[agent-network-topology]]
- [[agent-network-update-behavior]]
- [[agentic-systems]]
- [[ai-agent-security]]
- [[ai-alignment]]
- [[ai-mathematics]]
- [[ai-safety]]
- [[analytical-report-synthesizer]] — LLM 驱动的预测结果→分析报告自动生成器
- [[api-key-authentication]]
- [[attention-entropy-collapse]]
- [[attention-sinks]]
- [[automated-theorem-proving]]
- [[backtranslation-round-trip-relay]] — 回译接力：通过可逆编辑链评估 LLM 文档编辑保真度
- [[base-table-embedding]] — DIME 第一阶段：双路径编码捕获表内语义
- [[behrouz-memory-caching-rnn]]
- [[bidirectional-trajectory-evaluation]]
- [[bpf-syscall-interception]]
- [[cache-health-observability]]
- [[cache-hit-ratio]]
- [[cache-invalidation]]
- [[cache-safe-forking]]
- [[caddy-reverse-proxy-auth]]
- [[caddy-web-server]]
- [[cel-shading-style]]
- [[centralized-agent-architecture]]
- [[certainty-based-loss]] — 通过 argmin(loss) + argmax(certainty) 双 tick 选择实现原生自适应计算
- [[certainty-based-rewards]]
- [[chain-of-thought]]
- [[chaitin-algorithmic-information-theory]]
- [[chaitin-constant]]
- [[cl-bench-life]]
- [[classifier-free-guidance-language]] — CFG 在语言扩散模型中的应用
- [[clawless]]
- [[clawless-ai-agent-security]]
- [[coarse-grained-counting]]
- [[cognitive-architecture]]
- [[completeness-logic]]
- [[composable-base-model-architecture]] — 基础模型池 + 共享组件的可组合建模框架
- [[compressed-sparse-attention]]
- [[computability-theory]]
- [[computerized-adaptive-testing]]
- [[conditional-model-dispatcher]] — ZCP + 历史 EMA 驱动的模型选择与条件增强调度器
- [[confidence-correctness-alignment]]
- [[consistency-logic]]
- [[context-blue-clique]]
- [[context-compression]]
- [[context-learning]]
- [[context-misuse]]
- [[continuous-diffusion-language-models]] — 连续嵌入空间中的扩散语言模型
- [[continuous-thought-machine]] — CTM：以神经时序动态和同步为核心计算原理的新架构
- [[continuum-hypothesis]]
- [[cramer-rao-lower-bound]]
- [[crawl4ai]]
- [[crawl4ai-open-source-web-crawler]]
- [[critical-failures]] — 关键失败：稀疏但严重的错误解释了约80%的文档退化
- [[curvine-distributed-cache]]
- [[darlow-ctm-2025]] — CTM: 以神经同步为表示的持续思考机器 (NeurIPS 2025)
- [[darwin-godel-machine]]
- [[data-slice]] — 任务特定的关系数据库子集，DIME 的核心数据对象
- [[decentralized-agent-architecture]]
- [[deepseek-v4-flash]]
- [[deepseek-v4-million-token-context]]
- [[deepseek-vit]]
- [[delegate-52]] — Microsoft 基准：310工作环境 × 52专业领域，评估LLM委托工作就绪性
- [[delegated-work]] — 委托工作：新兴LLM交互范式，用户监督模型代其完成任务
- [[depth-scaling-signal-degradation]]
- [[diagonal-ramsey-number]]
- [[diagonalization-method]]
- [[dime-dynamic-in-database-modeling-engine]] — DIME：NeurIDA 的核心动态建模引擎
- [[discrete-diffusion-language-models]] — 离散 token 空间中的扩散语言模型
- [[distractor-context]] — 干扰上下文：话题相关但无需编辑的文档，模拟不完美检索精度
- [[document-degradation]] — 文档退化：LLM在长委托工作流中静默破坏文档内容的现象
- [[domain-knowledge-reasoning]]
- [[domain-specific-evaluation]] — 领域特定评估：每个领域自定义解析器和语义等价评分的评估方法
- [[dou-cl-bench]]
- [[duo-attention]]
- [[dynamic-in-database-modeling]] — 从共享组件在查询时装配定制模型的新范式
- [[dynamic-mode-decomposition]]
- [[dynamic-model-fusion]] — 上下文感知的选择性关系融合模块
- [[dynamic-relation-modeling]] — 跨表关系结构感知的消息传递
- [[elf-embedded-language-flows]] — ELF: 连续嵌入空间中的 Flow Matching 语言扩散模型 (2026)
- [[embedded-language-flows]] — ELF: 连续嵌入流匹配语言模型
- [[eml-operator]]
- [[empirical-discovery-simulation]]
- [[ensemble-based-rewards]]
- [[evolutionary-algorithms]]
- [[exponential-decay-reward]]
- [[few-shot-learning]]
- [[fine-grained-counting]]
- [[flash-attention]]
- [[flash-attention-3]]
- [[flow-matching]] — 连续时间流匹配生成框架
- [[formal-security-model]]
- [[formal-systems]]
- [[formal-verification]]
- [[forward-authentication]]
- [[fourier-filter-dynamics]]
- [[fp4-quantization-training]]
- [[furstenberg-correspondence]]
- [[generation-verification-asymmetry]]
- [[generative-perplexity]] — 基于第三方模型评估生成质量的指标
- [[genetic-programming]]
- [[geometric-ramsey-theory]]
- [[glitch-art-style]]
- [[godel-incompleteness-theorems]]
- [[godel-incompleteness-tutorial]]
- [[godel-numbering]]
- [[goodsteins-theorem]]
- [[gpt-image2]]
- [[gpt-image2-prompt-collection]]
- [[gravitino-unified-metadata]]
- [[greedy-context-screening]]
- [[green-tao-theorem]]
- [[group-relative-policy-optimization]]
- [[grouped-query-attention]]
- [[halftone-print-style]]
- [[halting-problem]]
- [[he-urlvr-sharpening-2026]]
- [[heavily-compressed-attention]]
- [[hilberts-program]]
- [[human-agent-trust]]
- [[human-centered-ai]]
- [[hunyuan-team-cl-bench-life]]
- [[hybrid-attention-architecture]]
- [[hyperagents]]
- [[hypergraph-ramsey-number]]
- [[identity-reference-resolution]]
- [[image-generation-prompt-design]]
- [[in-database-analytics]] — 在 DBMS 内部直接执行 ML/分析任务的方法论
- [[internal-ticks]] — 与数据维度解耦的内部时序，CTM 的「思考步骤」展开维度
- [[internal-world-model]] — agent 内部构建的环境表征，在 CTM 迷宫任务中涌现
- [[intrinsic-rewards-sharpening]]
- [[jagged-frontier]] — 锯齿前沿：AI模型能力在不同领域间不均衡、不可预测的分布
- [[klein-blue]]
- [[knowledge-bank]]
- [[kolmogorov-complexity]]
- [[koopman-autoencoder]]
- [[koopman-predictor]]
- [[koopman-theory]]
- [[kv-cache-bottleneck]]
- [[kvcache-transfer]]
- [[laban-llms-corrupt-documents-delegate]] — "LLMs Corrupt Your Documents When You Delegate" — DELEGATE-52
- [[length-extrapolation]] — 长度外推：让 LLM 处理超出预训练窗口的序列长度
- [[li-amd-human-perception]]
- [[linear-attention-methods]]
- [[liu-koopa-2023]]
- [[llm-applications]]
- [[llm-attention-survey-2026]]
- [[llm-evaluation-benchmarks]]
- [[log]] — 变更日志
- [[long-context-understanding]]
- [[long-horizon-evaluation]] — 长视界评估：通过延长交互揭示短评估中不可见的退化模式
- [[lost-in-the-middle]]
- [[lovasz-local-lemma]]
- [[lucas-penrose-argument]]
- [[mamba-ssm]]
- [[manifold-constrained-hyper-connections]]
- [[mathematical-pluralism]]
- [[maze-navigation]]
- [[memory-caching-rnn]]
- [[messy-context-reasoning]]
- [[meta-jctrader]]
- [[meta-learning]]
- [[metacognitive-self-modification]]
- [[metamathematics]]
- [[million-token-context]]
- [[mixture-of-attention-schemes]]
- [[mixture-of-depths-attention]]
- [[mixture-of-experts]]
- [[model-collapse-step]]
- [[multi-head-attention]]
- [[multi-head-latent-attention]]
- [[multi-query-attention]]
- [[multi-token-prediction]]
- [[multimodal-large-language-model]]
- [[muon-optimizer]]
- [[native-sparse-attention]]
- [[neural-synchronization]] — 将神经元激活历史的时序相关性直接用作潜在表示
- [[neurida]] — Neural In-Database Analytics：自主端到端库内分析系统
- [[neuron-level-models]] — 每个神经元拥有私有参数的 MLP，替代统一激活函数
- [[neuron-pairing]] — 对 O(D²) 同步矩阵的子采样策略，用于效率与表达力平衡
- [[neuroscience]]
- [[nikolopoulos-spurious-predictability]]
- [[non-stationary-time-series]]
- [[ntk-aware-interpolation]]
- [[odrzywolek-eml-single-operator]]
- [[on-policy-distillation]]
- [[oppo-multimodal-data-lake]]
- [[paley-graph]]
- [[paris-harrington-theorem]]
- [[path-tracing]]
- [[peano-arithmetic]]
- [[perception-gap]]
- [[pre-activation-history]] — 每个神经元维护的滚动前激活缓冲区，NLM 的输入
- [[prefill-as-a-service]]
- [[prefill-decode-disaggregation]]
- [[prefix-matching]]
- [[primitive-recursive-functions]]
- [[probabilistic-method]]
- [[procedural-task-execution]]
- [[program-synthesis]]
- [[prompt-caching]]
- [[prompt-caching-architecture]]
- [[prompt-layering]]
- [[prompt-reverse-engineering]]
- [[qin-prfaas-cross-datacenter]]
- [[query-intent-analyzer]] — LLM 驱动的 NLQ 解析器，输出结构化任务/数据画像
- [[rag-systems]]
- [[ramsey-context-cache]]
- [[ramsey-context-construction]]
- [[ramsey-context-graph]]
- [[ramsey-context-template]]
- [[ramsey-numbers]]
- [[ramsey-numbers-survey]]
- [[ramsey-theory]]
- [[ramsey-theory-applications]]
- [[random-graph-theory]]
- [[README]] — Wiki 说明
- [[real-life-context-learning]]
- [[rectified-flows]] — Flow Matching 中的直线插值路径
- [[recursive-self-improvement]]
- [[reference-gap]]
- [[reinforcement-learning-trading]]
- [[relational-graph]] — 以 FK-PK 为边的元组图，关系建模的数据结构基础
- [[reverse-proxy-authentication]]
- [[reward-hacking-llm]]
- [[reward-model]]
- [[risograph-print-style]]
- [[rlvr-unified-framework]]
- [[rolling-kv-cache]] — 滚动 KV 缓存：StreamingLLM 的两段式固定大小缓存机制
- [[rotary-position-embedding]]
- [[round-trip-reconstruction-score]] — RS@k：衡量k次交互后文档重建质量的评估指标
- [[rule-system-application]]
- [[russells-paradox]]
- [[russian-constructivism]]
- [[SCHEMA]] — Wiki 结构规范
- [[sde-sampler-language]] — 语言扩散中的随机微分方程采样器
- [[secure-containers]]
- [[seer-attention]]
- [[self-conditioning]] — 用自身中间预测作为条件的扩散技术
- [[self-improving-ai]]
- [[self-reference]]
- [[self-verification-rewards]]
- [[semantic-equivalence]] — 语义等价：通过领域特定解析器衡量文档间语义等价程度的方法
- [[shared-weight-discretization]] — ELF 的共享权重去噪-解码机制
- [[singularity]]
- [[sink-token]] — 可学习汇 Token：预训练时添加专用 Token 作为唯一注意力汇
- [[softmax-off-by-one]] — SoftMax₁：允许丢弃多余注意力的 SoftMax 变体
- [[song-agent-network-taxonomy]]
- [[sparse-attention-patterns]]
- [[specialist-training-pipeline]]
- [[specialized-rl]]
- [[specialized-sft]]
- [[spiking-neural-networks]] — 使用离散脉冲和事件驱动计算的生物启发神经网络
- [[spurious-predictability]]
- [[streaming-llm]] — StreamingLLM: 基于注意力汇的无限长流式语言模型推理框架 (ICLR 2024)
- [[stub-pattern]]
- [[subquadratic-transformer-alternatives]]
- [[symbolic-regression]]
- [[synapse-model]] — CTM 的 U-Net 风格循环突触结构，神经元间信息共享引擎
- [[system-2-thinking]]
- [[system-message-abuse]]
- [[szemerédi-regularity-lemma]]
- [[tabular-foundation-models]] — 大规模表格数据预训练的基础模型（TabPFN, TabICL 等）
- [[tao-klowden-ai-mathematical-methods]]
- [[temporal-decay-neural]] — 每对神经元可学习的指数衰减参数，控制同步的时间尺度
- [[test-time-scaling]]
- [[test-time-training-rl]]
- [[thinking-with-visual-primitives]]
- [[time-variant-dynamics]]
- [[token-efficiency]]
- [[tool-registry]]
- [[transfer-learning]]
- [[unified-rft]]
- [[unsupervised-rlvr]]
- [[userspace-kernel]]
- [[van-der-waerden-theorem]]
- [[visual-primitives]]
- [[window-attention]] — 窗口注意力：仅缓存最近 Token 的朴素方案，因驱逐注意力汇而崩溃
- [[worst-case-threat-model]]
- [[x-prediction-parameterization]] — Flow Matching 中直接预测干净数据的参数化
- [[xing-trails-2024]] — Trails: 数据库原生的深度神经网络模型选择 (VLDB 2024)
- [[zeng-dynamic-model-slicing-2024]] — 数据库内的动态模型切片技术 (VLDB 2024)
- [[zeng-neurida-2025]] — NeurIDA: 动态库内建模实现有效的关系数据库分析
- [[zero-cost-proxies]] — 无需完整训练即可估计模型性能的 NAS 技术
- [[zhang-hyperagents]]
- [[zhao-neurdb-2025]] — NeurDB: AI 驱动的自主数据库 (CIDR 2025)
- [[zhu-moda-mixture-of-depths]]

## Papers

- [[behrouz-memory-caching-rnn]]
- [[clawless-ai-agent-security]]
- [[darlow-ctm-2025]] — CTM: 以神经同步为表示的持续思考机器 (NeurIPS 2025)
- [[deepseek-v4-million-token-context]]
- [[dou-cl-bench]]
- [[elf-embedded-language-flows]] — ELF: 连续嵌入空间中的 Flow Matching 语言扩散模型 (2026)
- [[godel-incompleteness-tutorial]]
- [[he-urlvr-sharpening-2026]]
- [[hunyuan-team-cl-bench-life]]
- [[laban-llms-corrupt-documents-delegate]] — "LLMs Corrupt Your Documents When You Delegate" — DELEGATE-52
- [[li-amd-human-perception]]
- [[liu-koopa-2023]]
- [[llm-attention-survey-2026]]
- [[nikolopoulos-spurious-predictability]]
- [[odrzywolek-eml-single-operator]]
- [[qin-prfaas-cross-datacenter]]
- [[ramsey-numbers-survey]]
- [[song-agent-network-taxonomy]]
- [[streaming-llm]] — StreamingLLM: 基于注意力汇的无限长流式语言模型推理框架 (ICLR 2024)
- [[tao-klowden-ai-mathematical-methods]]
- [[thinking-with-visual-primitives]]
- [[xing-trails-2024]] — Trails: 数据库原生的深度神经网络模型选择 (VLDB 2024)
- [[zeng-dynamic-model-slicing-2024]] — 数据库内的动态模型切片技术 (VLDB 2024)
- [[zeng-neurida-2025]] — NeurIDA: 动态库内建模实现有效的关系数据库分析
- [[zhang-hyperagents]]
- [[zhao-neurdb-2025]] — NeurDB: AI 驱动的自主数据库 (CIDR 2025)
- [[zhu-moda-mixture-of-depths]]

## Articles


- [[caddy-reverse-proxy-auth]]
- [[crawl4ai-open-source-web-crawler]]
- [[gpt-image2-prompt-collection]]
- [[oppo-multimodal-data-lake]]
- [[prompt-caching-architecture]]
- [[ramsey-context-construction]]
## Special Pages

- [[SCHEMA]] — Wiki 结构规范
- [[log]] — 变更日志
- [[README]] — Wiki 说明

## Reviews

- [[ctm-review-20260515]] — CTM 论文集成 Review (2026-05-15)