350 lines
14 KiB
Markdown
350 lines
14 KiB
Markdown
# LLM Wiki
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> 知识索引页面 — 自动生成
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> 最后更新：2026-05-15 | 总页面数：335
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## Concepts
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- [[adaptive-computation-time]] — 根据输入难度动态调整计算量的技术族（ACT, PonderNet 等）
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- [[additive-combinatorics]]
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- [[agent-communication-stack]]
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- [[agent-mediated-deception]]
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- [[agent-network-memory-scope]]
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- [[agent-network-taxonomy]]
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- [[agent-network-topology]]
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- [[agent-network-update-behavior]]
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- [[agentic-systems]]
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- [[ai-agent-security]]
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- [[ai-alignment]]
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- [[ai-mathematics]]
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- [[ai-safety]]
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- [[analytical-report-synthesizer]] — LLM 驱动的预测结果→分析报告自动生成器
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- [[api-key-authentication]]
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- [[attention-entropy-collapse]]
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- [[attention-sinks]]
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- [[automated-theorem-proving]]
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- [[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
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- [[base-table-embedding]] — DIME 第一阶段：双路径编码捕获表内语义
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- [[behrouz-memory-caching-rnn]]
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- [[bidirectional-trajectory-evaluation]]
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- [[bpf-syscall-interception]]
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- [[cache-health-observability]]
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- [[cache-hit-ratio]]
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- [[cache-invalidation]]
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- [[cache-safe-forking]]
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- [[caddy-reverse-proxy-auth]]
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- [[caddy-web-server]]
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- [[cel-shading-style]]
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- [[centralized-agent-architecture]]
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- [[certainty-based-loss]] — 通过 argmin(loss) + argmax(certainty) 双 tick 选择实现原生自适应计算
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- [[certainty-based-rewards]]
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- [[chain-of-thought]]
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- [[chaitin-algorithmic-information-theory]]
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- [[chaitin-constant]]
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- [[cl-bench-life]]
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- [[classifier-free-guidance-language]] — CFG 在语言扩散模型中的应用
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- [[clawless]]
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- [[clawless-ai-agent-security]]
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- [[coarse-grained-counting]]
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- [[cognitive-architecture]]
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- [[completeness-logic]]
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- [[composable-base-model-architecture]] — 基础模型池 + 共享组件的可组合建模框架
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- [[compressed-sparse-attention]]
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- [[computability-theory]]
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- [[computerized-adaptive-testing]]
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- [[conditional-model-dispatcher]] — ZCP + 历史 EMA 驱动的模型选择与条件增强调度器
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- [[confidence-correctness-alignment]]
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- [[consistency-logic]]
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- [[context-blue-clique]]
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- [[context-compression]]
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- [[context-learning]]
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- [[context-misuse]]
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- [[continuous-diffusion-language-models]] — 连续嵌入空间中的扩散语言模型
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- [[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
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- [[continuum-hypothesis]]
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- [[cramer-rao-lower-bound]]
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- [[crawl4ai]]
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- [[crawl4ai-open-source-web-crawler]]
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- [[critical-failures]] — 关键失败：稀疏但严重的错误解释了约80%的文档退化
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- [[curvine-distributed-cache]]
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- [[darlow-ctm-2025]] — CTM: 以神经同步为表示的持续思考机器 (NeurIPS 2025)
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- [[darwin-godel-machine]]
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- [[data-slice]] — 任务特定的关系数据库子集，DIME 的核心数据对象
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- [[decentralized-agent-architecture]]
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- [[deepseek-v4-flash]]
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- [[deepseek-v4-million-token-context]]
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- [[deepseek-vit]]
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- [[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
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- [[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
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- [[depth-scaling-signal-degradation]]
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- [[diagonal-ramsey-number]]
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- [[diagonalization-method]]
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- [[dime-dynamic-in-database-modeling-engine]] — DIME：NeurIDA 的核心动态建模引擎
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- [[discrete-diffusion-language-models]] — 离散 token 空间中的扩散语言模型
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- [[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
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- [[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
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- [[domain-knowledge-reasoning]]
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- [[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
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- [[dou-cl-bench]]
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- [[duo-attention]]
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- [[dynamic-in-database-modeling]] — 从共享组件在查询时装配定制模型的新范式
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- [[dynamic-mode-decomposition]]
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- [[dynamic-model-fusion]] — 上下文感知的选择性关系融合模块
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- [[dynamic-relation-modeling]] — 跨表关系结构感知的消息传递
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- [[elf-embedded-language-flows]] — ELF: 连续嵌入空间中的 Flow Matching 语言扩散模型 (2026)
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- [[embedded-language-flows]] — ELF: 连续嵌入流匹配语言模型
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- [[eml-operator]]
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- [[empirical-discovery-simulation]]
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- [[ensemble-based-rewards]]
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- [[evolutionary-algorithms]]
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- [[exponential-decay-reward]]
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- [[few-shot-learning]]
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- [[fine-grained-counting]]
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- [[flash-attention]]
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- [[flash-attention-3]]
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- [[flow-matching]] — 连续时间流匹配生成框架
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- [[formal-security-model]]
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- [[formal-systems]]
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- [[formal-verification]]
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- [[forward-authentication]]
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- [[fourier-filter-dynamics]]
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- [[fp4-quantization-training]]
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- [[furstenberg-correspondence]]
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- [[generation-verification-asymmetry]]
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- [[generative-perplexity]] — 基于第三方模型评估生成质量的指标
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- [[genetic-programming]]
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- [[geometric-ramsey-theory]]
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- [[glitch-art-style]]
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- [[godel-incompleteness-theorems]]
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- [[godel-incompleteness-tutorial]]
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- [[godel-numbering]]
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- [[goodsteins-theorem]]
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- [[gpt-image2]]
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- [[gpt-image2-prompt-collection]]
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- [[gravitino-unified-metadata]]
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- [[greedy-context-screening]]
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- [[green-tao-theorem]]
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- [[group-relative-policy-optimization]]
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- [[grouped-query-attention]]
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- [[halftone-print-style]]
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- [[halting-problem]]
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- [[he-urlvr-sharpening-2026]]
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- [[heavily-compressed-attention]]
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- [[hilberts-program]]
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- [[human-agent-trust]]
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- [[human-centered-ai]]
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- [[hunyuan-team-cl-bench-life]]
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- [[hybrid-attention-architecture]]
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- [[hyperagents]]
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- [[hypergraph-ramsey-number]]
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- [[identity-reference-resolution]]
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- [[image-generation-prompt-design]]
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- [[in-database-analytics]] — 在 DBMS 内部直接执行 ML/分析任务的方法论
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- [[internal-ticks]] — 与数据维度解耦的内部时序，CTM 的「思考步骤」展开维度
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- [[internal-world-model]] — agent 内部构建的环境表征，在 CTM 迷宫任务中涌现
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- [[intrinsic-rewards-sharpening]]
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- [[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
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- [[klein-blue]]
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- [[knowledge-bank]]
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- [[kolmogorov-complexity]]
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- [[koopman-autoencoder]]
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- [[koopman-predictor]]
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- [[koopman-theory]]
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- [[kv-cache-bottleneck]]
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- [[kvcache-transfer]]
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- [[laban-llms-corrupt-documents-delegate]] — "LLMs Corrupt Your Documents When You Delegate" — DELEGATE-52
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- [[length-extrapolation]] — 长度外推：让 LLM 处理超出预训练窗口的序列长度
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- [[li-amd-human-perception]]
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- [[linear-attention-methods]]
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- [[liu-koopa-2023]]
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- [[llm-applications]]
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- [[llm-attention-survey-2026]]
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- [[llm-evaluation-benchmarks]]
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- [[log]] — 变更日志
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- [[long-context-understanding]]
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- [[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
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- [[lost-in-the-middle]]
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- [[lovasz-local-lemma]]
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- [[lucas-penrose-argument]]
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- [[mamba-ssm]]
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- [[manifold-constrained-hyper-connections]]
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- [[mathematical-pluralism]]
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- [[maze-navigation]]
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- [[memory-caching-rnn]]
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- [[messy-context-reasoning]]
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- [[meta-jctrader]]
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- [[meta-learning]]
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- [[metacognitive-self-modification]]
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- [[metamathematics]]
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- [[million-token-context]]
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- [[mixture-of-attention-schemes]]
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- [[mixture-of-depths-attention]]
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- [[mixture-of-experts]]
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- [[model-collapse-step]]
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- [[multi-head-attention]]
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- [[multi-head-latent-attention]]
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- [[multi-query-attention]]
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- [[multi-token-prediction]]
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- [[multimodal-large-language-model]]
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- [[muon-optimizer]]
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- [[native-sparse-attention]]
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- [[neural-synchronization]] — 将神经元激活历史的时序相关性直接用作潜在表示
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- [[neurida]] — Neural In-Database Analytics：自主端到端库内分析系统
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- [[neuron-level-models]] — 每个神经元拥有私有参数的 MLP，替代统一激活函数
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- [[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
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- [[neuroscience]]
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- [[nikolopoulos-spurious-predictability]]
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- [[non-stationary-time-series]]
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- [[ntk-aware-interpolation]]
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- [[odrzywolek-eml-single-operator]]
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- [[on-policy-distillation]]
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- [[oppo-multimodal-data-lake]]
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- [[paley-graph]]
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- [[paris-harrington-theorem]]
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- [[path-tracing]]
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- [[peano-arithmetic]]
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- [[perception-gap]]
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- [[pre-activation-history]] — 每个神经元维护的滚动前激活缓冲区，NLM 的输入
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- [[prefill-as-a-service]]
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- [[prefill-decode-disaggregation]]
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- [[prefix-matching]]
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- [[primitive-recursive-functions]]
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- [[probabilistic-method]]
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- [[procedural-task-execution]]
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- [[program-synthesis]]
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- [[prompt-caching]]
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- [[prompt-caching-architecture]]
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- [[prompt-layering]]
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- [[prompt-reverse-engineering]]
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- [[qin-prfaas-cross-datacenter]]
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- [[query-intent-analyzer]] — LLM 驱动的 NLQ 解析器，输出结构化任务/数据画像
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- [[rag-systems]]
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- [[ramsey-context-cache]]
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- [[ramsey-context-construction]]
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- [[ramsey-context-graph]]
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- [[ramsey-context-template]]
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- [[ramsey-numbers]]
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- [[ramsey-numbers-survey]]
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- [[ramsey-theory]]
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- [[ramsey-theory-applications]]
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- [[random-graph-theory]]
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- [[README]] — Wiki 说明
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- [[real-life-context-learning]]
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- [[rectified-flows]] — Flow Matching 中的直线插值路径
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- [[recursive-self-improvement]]
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- [[reference-gap]]
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- [[reinforcement-learning-trading]]
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- [[relational-graph]] — 以 FK-PK 为边的元组图，关系建模的数据结构基础
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- [[reverse-proxy-authentication]]
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- [[reward-hacking-llm]]
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- [[reward-model]]
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- [[risograph-print-style]]
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- [[rlvr-unified-framework]]
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- [[rolling-kv-cache]] — 滚动 KV 缓存：StreamingLLM 的两段式固定大小缓存机制
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- [[rotary-position-embedding]]
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- [[round-trip-reconstruction-score]] — RS@k：衡量k次交互后文档重建质量的评估指标
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- [[rule-system-application]]
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- [[russells-paradox]]
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- [[russian-constructivism]]
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- [[SCHEMA]] — Wiki 结构规范
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- [[sde-sampler-language]] — 语言扩散中的随机微分方程采样器
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- [[secure-containers]]
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- [[seer-attention]]
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- [[self-conditioning]] — 用自身中间预测作为条件的扩散技术
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- [[self-improving-ai]]
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- [[self-reference]]
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- [[self-verification-rewards]]
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- [[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
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- [[shared-weight-discretization]] — ELF 的共享权重去噪-解码机制
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- [[singularity]]
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- [[sink-token]] — 可学习汇 Token：预训练时添加专用 Token 作为唯一注意力汇
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- [[softmax-off-by-one]] — SoftMax₁：允许丢弃多余注意力的 SoftMax 变体
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- [[song-agent-network-taxonomy]]
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- [[sparse-attention-patterns]]
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- [[specialist-training-pipeline]]
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- [[specialized-rl]]
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- [[specialized-sft]]
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- [[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
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- [[spurious-predictability]]
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- [[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)
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- [[stub-pattern]]
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- [[subquadratic-transformer-alternatives]]
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- [[symbolic-regression]]
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- [[synapse-model]] — CTM 的 U-Net 风格循环突触结构，神经元间信息共享引擎
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- [[system-2-thinking]]
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- [[system-message-abuse]]
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- [[szemerédi-regularity-lemma]]
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- [[tabular-foundation-models]] — 大规模表格数据预训练的基础模型（TabPFN, TabICL 等）
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- [[tao-klowden-ai-mathematical-methods]]
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- [[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
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- [[test-time-scaling]]
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- [[test-time-training-rl]]
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- [[thinking-with-visual-primitives]]
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- [[time-variant-dynamics]]
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- [[token-efficiency]]
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- [[tool-registry]]
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- [[transfer-learning]]
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- [[unified-rft]]
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- [[unsupervised-rlvr]]
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- [[userspace-kernel]]
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- [[van-der-waerden-theorem]]
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- [[visual-primitives]]
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- [[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
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- [[worst-case-threat-model]]
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- [[x-prediction-parameterization]] — Flow Matching 中直接预测干净数据的参数化
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- [[xing-trails-2024]] — Trails: 数据库原生的深度神经网络模型选择 (VLDB 2024)
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- [[zeng-dynamic-model-slicing-2024]] — 数据库内的动态模型切片技术 (VLDB 2024)
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- [[zeng-neurida-2025]] — NeurIDA: 动态库内建模实现有效的关系数据库分析
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- [[zero-cost-proxies]] — 无需完整训练即可估计模型性能的 NAS 技术
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- [[zhang-hyperagents]]
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- [[zhao-neurdb-2025]] — NeurDB: AI 驱动的自主数据库 (CIDR 2025)
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- [[zhu-moda-mixture-of-depths]]
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## Papers
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- [[behrouz-memory-caching-rnn]]
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- [[clawless-ai-agent-security]]
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- [[darlow-ctm-2025]] — CTM: 以神经同步为表示的持续思考机器 (NeurIPS 2025)
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- [[deepseek-v4-million-token-context]]
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- [[dou-cl-bench]]
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- [[elf-embedded-language-flows]] — ELF: 连续嵌入空间中的 Flow Matching 语言扩散模型 (2026)
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- [[godel-incompleteness-tutorial]]
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- [[he-urlvr-sharpening-2026]]
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- [[hunyuan-team-cl-bench-life]]
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- [[laban-llms-corrupt-documents-delegate]] — "LLMs Corrupt Your Documents When You Delegate" — DELEGATE-52
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- [[li-amd-human-perception]]
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- [[liu-koopa-2023]]
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- [[llm-attention-survey-2026]]
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- [[nikolopoulos-spurious-predictability]]
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- [[odrzywolek-eml-single-operator]]
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- [[qin-prfaas-cross-datacenter]]
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- [[ramsey-numbers-survey]]
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- [[song-agent-network-taxonomy]]
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- [[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)
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- [[tao-klowden-ai-mathematical-methods]]
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- [[thinking-with-visual-primitives]]
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- [[xing-trails-2024]] — Trails: 数据库原生的深度神经网络模型选择 (VLDB 2024)
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- [[zeng-dynamic-model-slicing-2024]] — 数据库内的动态模型切片技术 (VLDB 2024)
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- [[zeng-neurida-2025]] — NeurIDA: 动态库内建模实现有效的关系数据库分析
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- [[zhang-hyperagents]]
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- [[zhao-neurdb-2025]] — NeurDB: AI 驱动的自主数据库 (CIDR 2025)
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- [[zhu-moda-mixture-of-depths]]
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## Articles
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- [[caddy-reverse-proxy-auth]]
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- [[crawl4ai-open-source-web-crawler]]
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- [[gpt-image2-prompt-collection]]
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- [[oppo-multimodal-data-lake]]
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- [[prompt-caching-architecture]]
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- [[ramsey-context-construction]]
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## Special Pages
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- [[SCHEMA]] — Wiki 结构规范
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- [[log]] — 变更日志
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- [[README]] — Wiki 说明
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## Reviews
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- [[ctm-review-20260515]] — CTM 论文集成 Review (2026-05-15) |