myWiki/index.md

# LLM Wiki

> 知识索引页面 — 自动生成
> 最后更新：2026-05-31 | 总页面数：528

## Concepts

- [[action-applicability]] — Action Applicability (动作合法性判定)
- [[active-cache-warmup]] — Active Cache Warm-up (主动缓存预热)
- [[adaptive-computation-time]] — Adaptive Computation Time (ACT)
- [[adaptive-harness-simplification]] — Adaptive Harness Simplification（自适应 Harness 简化）
- [[additive-combinatorics]] — Additive Combinatorics（加法组合学）
- [[agent-capability-stability-gap]] — Agent Capability-Stability Gap（能力-稳定性差距）
- [[agent-communication-stack]] — Agent通信协议栈
- [[agent-completion-evaluation]] — Agent Completion Evaluation（Agent 完成度评测）
- [[agent-computer-interface]] — Agent-Computer Interface (ACI)
- [[agent-eval-case-design]] — Agent Eval Case Design
- [[agent-eval-grader]] — Agent Eval Grader
- [[agent-eval-trace]] — Agent Eval Trace
- [[agent-evaluation-paradigm-shift]] — Agent 评测范式转变（Paradigm Shift in Agent Evaluation）
- [[agent-frameworks-to-platforms]] — Agent Frameworks to Platforms（从 Agent 框架到 Agent 平台）
- [[agent-governance]] — Agent Governance（Agent 治理与安全）
- [[agent-harness-engineering]] — Agent Harness Engineering（Agent 执行骨架工程）
- [[agent-harness-mini]] — Mini Agent Harness
- [[agent-mediated-deception]] — 代理中介欺骗 (Agent-Mediated Deception)
- [[agent-multidimensional-capability]] — Agent Multidimensional Capability（Agent 多维能力）
- [[agent-network-memory-scope]] — Agent网络记忆范围
- [[agent-network-taxonomy]] — Agent网络三层分类法
- [[agent-network-topology]] — Agent网络拓扑
- [[agent-network-update-behavior]] — Agent网络更新行为
- [[agent-observability]] — Agent Observability（Agent 可观测性）
- [[agent-process-evaluation]] — Agent Process Evaluation（过程评测）
- [[agent-robustness-evaluation]] — Agent Robustness Evaluation（Agent 鲁棒性评测）
- [[agent-safety-evaluation]] — Agent Safety Evaluation（Agent 安全评测）
- [[agent-sandbox]] — Agent Sandbox（Agent 沙箱）
- [[agent-symbolic-learning]] — Agent Symbolic Learning (Agent 符号学习)
- [[agent-verification]] — Agent Verification（Agent 验证与评估）
- [[agentic-systems]] — Agentic Systems（智能体系统）
- [[ai-agent-security]] — AI代理安全
- [[ai-alignment]] — AI Alignment (AI对齐)
- [[ai-mathematics]] — AI and Mathematics (AI 与数学)
- [[ai-safety]] — AI Safety (AI安全)
- [[amortized-variational-inference]] — Amortized Variational Inference（摊销变分推断）
- [[analytical-report-synthesizer]] — Analytical Report Synthesizer
- [[anthropic-agent-evals]] — Anthropic Agent Evals
- [[api-key-authentication]] — API Key 认证 (API Key Authentication)
- [[asynchronous-rl-llm]] — 异步强化学习与大语言模型后训练
- [[attention-entropy-collapse]] — 注意力熵崩溃 (Attention Entropy Collapse)
- [[attention-sinks]] — 注意力汇 (Attention Sinks)
- [[autoharness]] — AutoHarness
- [[automated-theorem-proving]] — 自动定理证明 (Automated Theorem Proving, ATP)
- [[backtranslation-round-trip-relay]] — Backtranslation Round-Trip Relay
- [[base-table-embedding]] — Base Table Embedding
- [[bayesian-attention-geometry]] — Bayesian Attention Geometry (贝叶斯注意力几何)
- [[bayesian-attention-trilogy]] — Bayesian Attention Trilogy
- [[bayesian-wind-tunnels]] — Bayesian Wind Tunnels
- [[belief-accumulation]] — Belief Accumulation (信念累积)
- [[belief-transport]] — Belief Transport (信念传输)
- [[bidirectional-trajectory-evaluation]] — 双向轨迹评估 (Bidirectional Trajectory Evaluation)
- [[binding-constraint-thesis]] — Binding-Constraint Thesis（约束瓶颈论）
- [[bpf-syscall-interception]] — BPF系统调用拦截
- [[bypass-network-handle-distribution]] — Bypass Network Handle Distribution (旁路网络句柄分发)
- [[cache-cold-start]] — Cache Cold-Start (缓存冷启动)
- [[cache-health-observability]] — Cache Health Observability（缓存健康度可观测性）
- [[cache-hit-ratio]] — Cache Hit Ratio (CHR)
- [[cache-invalidation]] — Cache Invalidation（缓存失效）
- [[cache-safe-forking]] — Cache-Safe Forking（缓存安全分叉）
- [[caddy-web-server]] — Caddy Web Server
- [[capability-control-tradeoff]] — Capability-Control Tradeoff（能力-控制权衡）
- [[capability-degradation]] — 能力退化 (Capability Degradation)
- [[cel-shading-style]] — 赛璐璐风格 (Cel-Shading)
- [[centralized-agent-architecture]] — 集中式Agent架构
- [[certainty-based-loss]] — Certainty-Based Loss
- [[certainty-based-rewards]] — 确定性奖励 (Certainty-Based Rewards)
- [[chain-of-thought]] — 思维链 (Chain-of-Thought, CoT)
- [[chaitin-algorithmic-information-theory]] — 算法信息论 (Algorithmic Information Theory, AIT)
- [[chaitin-constant]] — 蔡廷常数 Ω (Chaitin's Constant)
- [[cl-bench-life]] — CL-Bench Life
- [[classifier-free-guidance-language]] — Classifier-Free Guidance for Language
- [[clawless]] — ClawLess
- [[coarse-grained-counting]] — 粗粒度计数 (Coarse-grained Counting)
- [[coarse-to-fine-granularity]] — Coarse-to-Fine Granularity
- [[code-as-harness]] — Code as Harness
- [[cognitive-architecture]] — Cognitive Architecture (认知架构)
- [[compiled-ai-paradigm]] — Compiled AI Paradigm (编译型 AI 范式)
- [[completeness-logic]] — 完备性 (Completeness, 逻辑学)
- [[composable-base-model-architecture]] — Composable Base Model Architecture
- [[compressed-sparse-attention]] — Compressed Sparse Attention (CSA)
- [[computability-theory]] — 可计算性理论 (Computability Theory)
- [[computer-use-agents]] — Computer Use Agents (CUAs)
- [[computerized-adaptive-testing]] — Computerized Adaptive Testing (CAT)
- [[conditional-model-dispatcher]] — Conditional Model Dispatcher
- [[confidence-correctness-alignment]] — 置信度-正确性对齐 (Confidence-Correctness Alignment)
- [[consistency-logic]] — 一致性 (Consistency, 逻辑学)
- [[context-blue-clique]] — Context Blue Clique（上下文蓝色团）
- [[context-compression]] — Context Compression（上下文压缩）
- [[context-drift]] — Context Drift（上下文漂移）
- [[context-engineering]] — Context Engineering（上下文工程）
- [[context-learning]] — 上下文学习 (Context Learning)
- [[context-management]] — Context Management（上下文管理）
- [[context-misuse]] — 上下文误用 (Context Misuse)
- [[context-pruning]] — Context Pruning (上下文剪枝)
- [[context-state-estimation]] — Context as State Estimation（上下文作为状态估计）
- [[continuous-diffusion-language-models]] — Continuous Diffusion Language Models
- [[continuous-thought-machine]] — Continuous Thought Machine (CTM)
- [[continuum-hypothesis]] — 连续统假设 (Continuum Hypothesis, CH)
- [[controlled-autonomy]] — Controlled Autonomy (受控的自主性)
- [[cost-quality-speed-trilemma]] — Cost-Quality-Speed Trilemma（成本-质量-速度三元悖论）
- [[covariance-matrix]] — 协方差矩阵 (Covariance Matrix)
- [[covariance-matrix-knowledge]] — 协方差矩阵知识存储 (Covariance Matrix Knowledge Storage)
- [[cramer-rao-lower-bound]] — Cramér-Rao Lower Bound (CRLB)
- [[crawl4ai]] — Crawl4AI
- [[critical-failures]] — Critical Failures / 关键失败
- [[curvine-distributed-cache]] — Curvine 云原生分布式缓存
- [[darwin-godel-machine]] — Darwin Gödel Machine (达尔文·哥德尔机)
- [[data-hierarchical-governance]] — Data Hierarchical Governance (L0-L4 数据分级治理)
- [[data-label-consistency]] — Data-Label Consistency (数据-标签一致性)
- [[data-quality-over-scale]] — Data Quality over Scale (数据质量重于规模)
- [[data-replay]] — 数据回放 (Data Replay)
- [[data-slice]] — Data Slice
- [[decentralized-agent-architecture]] — 去中心化Agent架构
- [[deep-and-wide-reasoning]] — Deep-and-Wide Reasoning（深度且宽广的推理）
- [[deep-thinking-sft]] — Deep-Thinking SFT (深思考SFT数据)
- [[deepseek-v4-flash]] — DeepSeek-V4-Flash
- [[deepseek-vit]] — DeepSeek-ViT
- [[delegate-52]] — DELEGATE-52
- [[delegated-work]] — Delegated Work / 委托工作
- [[depth-scaling-signal-degradation]] — LLM 深度扩展与信号退化
- [[dgae]] — Difficulty-Balanced Group Advantage Estimation (DGAE)
- [[dgpo]] — Difficulty-Aware Group Policy Optimization (DGPO)
- [[diagonal-ramsey-number]] — Diagonal Ramsey Number（对角拉姆齐数）
- [[diagonalization-method]] — 对角线方法 (Diagonalization Method)
- [[dime-dynamic-in-database-modeling-engine]] — DIME (Dynamic In-Database Modeling Engine)
- [[discrete-diffusion-language-models]] — discrete-diffusion-language-models
- [[distractor-context]] — Distractor Context / 干扰上下文
- [[distributed-cache-routing]] — Distributed Cache Routing (分布式缓存路由)
- [[distributed-optimistic-locking]] — Distributed Optimistic Locking (分布式乐观锁)
- [[distributed-prompt-caching]] — Distributed Prompt Caching (分布式提示词缓存)
- [[distribution-shift]] — Distribution Shift（分布偏移）
- [[document-degradation]] — Document Degradation / 文档退化
- [[domain-knowledge-reasoning]] — 领域知识推理 (Domain Knowledge Reasoning)
- [[domain-specific-evaluation]] — Domain-Specific Evaluation / 领域特定评估
- [[dominant-shuffle]] — Dominant Shuffle
- [[dqw]] — Difficulty-Aware Question-Level Weighting (DQW)
- [[dual-layer-rl]] — Dual-Layer RL (双层强化学习)
- [[dual-space-rl]] — Dual Space RL (DSRL)
- [[duo-attention]] — DuoAttention
- [[dynamic-in-database-modeling]] — Dynamic In-Database Modeling
- [[dynamic-mode-decomposition]] — Dynamic Mode Decomposition (DMD)
- [[dynamic-model-fusion]] — Dynamic Model Fusion
- [[dynamic-relation-modeling]] — Dynamic Relation Modeling
- [[embedded-language-flows]] — Embedded Language Flows (ELF)
- [[eml-operator]] — EML 算子 (Exp-Minus-Log)
- [[empirical-discovery-simulation]] — 经验发现与模拟 (Empirical Discovery & Simulation)
- [[endogenous-reasoning]] — Endogenous Reasoning（内生推理）
- [[ensemble-based-rewards]] — 集成奖励 (Ensemble-Based Rewards)
- [[etclovg-taxonomy]] — ETCLOVG 七层分类法
- [[evolutionary-algorithms]] — Evolutionary Algorithms (进化算法)
- [[evolving-knowledge-injection]] — 进化知识注入 (Evolving Knowledge Injection)
- [[execution-environment]] — Execution Environment（执行环境与沙箱）
- [[exponential-decay-reward]] — 指数衰减奖励 (Exponential Decay Reward)
- [[few-shot-learning]] — Few-Shot Learning (少样本学习)
- [[fine-grained-counting]] — 细粒度计数 (Fine-grained Counting)
- [[flash-attention]] — FlashAttention
- [[flash-attention-3]] — FlashAttention-3
- [[flow-matching]] — Flow Matching
- [[forecasting-augmentation-taxonomy]] — Forecasting Augmentation Taxonomy
- [[formal-security-model]] — 形式化安全模型
- [[formal-systems]] — 形式系统 (Formal System)
- [[formal-verification]] — Formal Verification (形式化验证)
- [[forward-authentication]] — 外部认证委托 (Forward Authentication)
- [[fourier-filter-dynamics]] — Fourier Filter for Dynamics（Fourier Filter 动力学分解）
- [[fp4-quantization-training]] — FP4 Quantization-Aware Training
- [[freqmask-freqmix]] — FreqMask / FreqMix
- [[furstenberg-correspondence]] — Furstenberg Correspondence Principle
- [[generation-verification-asymmetry]] — 生成-验证不对称性 (Generation-Verification Asymmetry)
- [[generative-general-unification]] — Generative-General-Unification (GenAI 三支柱)
- [[generative-perplexity]] — generative-perplexity
- [[genetic-programming]] — Genetic Programming (遗传编程)
- [[geometric-ramsey-theory]] — Geometric Ramsey Theory（几何拉姆齐理论）
- [[gflownet-fine-tuning]] — GFlowNet 微调
- [[glitch-art-style]] — 故障艺术 (Glitch Art)
- [[global-context-hash-tree]] — Global Context Hash Tree (全局上下文哈希树)
- [[godel-incompleteness-theorems]] — 哥德尔不完备定理 (Gödel's Incompleteness Theorems)
- [[godel-numbering]] — 哥德尔编码 (Gödel Numbering)
- [[goodsteins-theorem]] — 古德斯坦定理 (Goodstein's Theorem)
- [[governance-security]] — Governance & Security（治理与安全）
- [[gpt-image2]] — GPT-Image-2
- [[gradient-alignment]] — Gradient Alignment (PreRL)
- [[gram-generative-recursive-reasoning]] — GRAM（Generative Recursive reAsoning Models）
- [[gravitino-unified-metadata]] — Gravitino 统一元数据管理
- [[greedy-context-screening]] — Greedy Context Screening（贪心上下文筛选）
- [[green-tao-theorem]] — Green-Tao Theorem
- [[group-relative-policy-optimization]] — 群体相对策略优化 (GRPO)
- [[grouped-query-attention]] — Grouped-Query Attention (GQA)
- [[grpo]] — Group Relative Policy Optimization (GRPO)
- [[gui-tool-hybrid-action-space]] — GUI-Tool Hybrid Action Space
- [[halftone-print-style]] — 半调印刷风格 (Halftone Print Style)
- [[halting-problem]] — 停机问题 (Halting Problem)
- [[hardening-execution-environments]] — Hardening Execution Environments（硬化执行环境）
- [[harness-as-action-verifier]] — Harness-as-Action-Verifier
- [[harness-as-policy]] — Harness-as-Policy (Code as Policy)
- [[harness-coupling-problem]] — Harness Coupling Problem（Harness 耦合问题）
- [[harness-engineering]] — Harness Engineering
- [[hars]] — HARS（调和适应保留评分）
- [[heavily-compressed-attention]] — Heavily Compressed Attention (HCA)
- [[held-out-validation-gate]] — Held-Out Validation Gate (留出验证门)
- [[heuristic-learning]] — Heuristic Learning (启发式学习)
- [[hilberts-program]] — 希尔伯特计划 (Hilbert's Program)
- [[human-agent-trust]] — 人机信任 (Human-Agent Trust)
- [[human-centered-ai]] — Human-Centered AI (以人类为中心的 AI)
- [[hybrid-attention-architecture]] — Hybrid Attention Architecture
- [[hyperagents]] — Hyperagents (超智能体)
- [[hypergraph-ramsey-number]] — Hypergraph Ramsey Number（超图拉姆齐数）
- [[identity-reference-resolution]] — 身份指代消解 (Identity Reference Resolution)
- [[image-generation-prompt-design]] — 图像生成 Prompt 设计
- [[in-database-analytics]] — In-Database Analytics
- [[inference-primitives]] — Inference Primitives (推理原语)
- [[inference-time-scaling]] — Inference-Time Scaling（推理时扩展）
- [[input-superposition]] — Input Superposition
- [[interleaved-gui-tool-trajectory-scaling]] — Interleaved GUI-Tool Trajectory Scaling Pipeline
- [[internal-ticks]] — Internal Ticks
- [[internal-world-model]] — Internal World Model
- [[intrinsic-rewards-sharpening]] — 内在奖励锐化机制 (Intrinsic Rewards Sharpening)
- [[iterative-code-refinement]] — Iterative Code Refinement (迭代代码精炼)
- [[jagged-frontier]] — Jagged Frontier / 锯齿前沿
- [[klein-blue]] — 克莱因蓝 (Klein Blue / IKB)
- [[knowledge-adaptation]] — 知识适应 (Knowledge Adaptation)
- [[knowledge-agnostic-augmentation]] — 知识无关增强 (Knowledge-Agnostic Augmentation)
- [[knowledge-aware-augmentation]] — 知识感知增强 (Knowledge-Aware Augmentation)
- [[knowledge-bank]] — Knowledge Bank — AI 辅助开发时代的知识管理系统
- [[knowledge-internalization]] — 知识内化 (Knowledge Internalization)
- [[knowledge-retention]] — 知识保留 (Knowledge Retention)
- [[knowledge-tree]] — 知识树 (Knowledge Tree)
- [[kolmogorov-complexity]] — 柯尔莫哥洛夫复杂度 (Kolmogorov Complexity)
- [[koopman-autoencoder]] — Koopman Autoencoder (KAE)
- [[koopman-predictor]] — Koopman Predictor（Koopman 预测器）
- [[koopman-theory]] — Koopman Theory（Koopman 理论）
- [[kore-augmentation]] — KORE-AUGMENTATION（知识导向增强）
- [[kore-constraint]] — KORE-CONSTRAINT（知识导向约束）
- [[kv-cache-bottleneck]] — KV 缓存内存瓶颈
- [[kvcache-transfer]] — KVCache 传输与优化
- [[language-gradient]] — Language Gradient (语言梯度)
- [[language-loss]] — Language Loss (语言损失)
- [[latent-variable-generative-model]] — Latent-Variable Generative Model（潜在变量生成模型）
- [[length-extrapolation]] — 长度外推 (Length Extrapolation)
- [[lifecycle-orchestration]] — Lifecycle & Orchestration（生命周期与编排）
- [[linear-attention-methods]] — 线性注意力方法 (Linear Attention Methods)
- [[llm-applications]] — LLM 应用
- [[llm-evaluation-benchmarks]] — LLM 评测基准体系
- [[long-context-understanding]] — 长上下文理解 (Long-Context Understanding)
- [[long-horizon-evaluation]] — Long-Horizon Evaluation / 长视界评估
- [[lost-in-the-middle]] — Lost in the Middle
- [[lovasz-local-lemma]] — Lovász Local Lemma
- [[lucas-penrose-argument]] — 卢卡斯-彭罗斯论证 (Lucas-Penrose Argument)
- [[mamba-ssm]] — Mamba (State Space Model)
- [[manifold-constrained-hyper-connections]] — Manifold-Constrained Hyper-Connections (mHC)
- [[math-question-reformulation]] — 数学问题多维度改写
- [[mathematical-pluralism]] — 数学多元主义 (Mathematical Pluralism)
- [[mathforge]] — MathForge 框架
- [[maze-navigation]] — 迷宫导航 (Maze Navigation)
- [[memory-caching-rnn]] — Memory Caching (MC)
- [[messy-context-reasoning]] — 混乱上下文推理 (Messy Context Reasoning)
- [[meta-jctrader]] — Meta-JCTrader
- [[meta-learning]] — Meta-Learning (元学习)
- [[metacognitive-self-modification]] — Metacognitive Self-Modification (元认知自我修改)
- [[metamathematics]] — 元数学 (Metamathematics)
- [[million-token-context]] — Million-Token Context
- [[mixture-of-attention-schemes]] — Mixture of Attention Schemes (MoAS)
- [[mixture-of-depths-attention]] — Mixture-of-Depths Attention (MoDA)
- [[mixture-of-experts]] — Mixture of Experts (MoE)
- [[mme-voke]] — MMEVOKE
- [[model-collapse-step]] — 模型崩溃步 (Model Collapse Step, MCS)
- [[model-harness-relationship]] — Model-Harness Relationship (模型与Harness关系)
- [[moe-lora]] — MoELoRA
- [[mqr]] — Multi-Aspect Question Reformulation (MQR)
- [[multi-agent-orchestration]] — Multi-Agent Orchestration（多 Agent 编排）
- [[multi-head-attention]] — Multi-Head Attention (MHA)
- [[multi-head-latent-attention]] — Multi-head Latent Attention (MLA)
- [[multi-hot-cross-entropy]] — Multi-hot Cross-Entropy (MCE)
- [[multi-query-attention]] — Multi-Query Attention (MQA)
- [[multi-solution-recovery]] — Multi-Solution Recovery（多解恢复）
- [[multi-token-prediction]] — Multi-Token Prediction (MTP)
- [[multi-trajectory-inference]] — Multi-Trajectory Inference（多轨迹推理）
- [[multimodal-large-language-model]] — 多模态大语言模型 (MLLM)
- [[multimodal-rag]] — 多模态 RAG (Multimodal RAG)
- [[muon-optimizer]] — Muon Optimizer
- [[native-sparse-attention]] — Native Sparse Attention (NSA)
- [[negative-sample-reinforcement]] — Negative Sample Reinforcement (NSR)
- [[neural-synchronization]] — Neural Synchronization as Representation
- [[neurida]] — NeurIDA
- [[neuron-level-models]] — Neuron-Level Models (NLMs)
- [[neuron-pairing]] — Neuron Pairing
- [[neuroscience]] — Neuroscience (神经科学)
- [[next-state-grounding]] — Next-State Grounding
- [[non-stationary-time-series]] — Non-stationary Time Series（非平稳时间序列）
- [[ntk-aware-interpolation]] — NTK-aware 位置编码插值
- [[null-space]] — 零空间 (Null Space)
- [[null-space-projection-knowledge]] — 零空间投影知识保留 (Null Space Projection for Knowledge Retention)
- [[observability]] — Observability & Operations（可观测性与运维）
- [[off-policy-llm-post-training]] — Off-Policy LLM 后训练
- [[on-policy-distillation]] — On-Policy Distillation (OPD)
- [[on-policy-learning-collapse]] — On-policy Learning Collapse
- [[optimal-gui-tool-path-selection]] — Optimal GUI-Tool Path Selection
- [[osworld-mcp]] — OSWorld-MCP Benchmark
- [[paley-graph]] — Paley Graph
- [[paris-harrington-theorem]] — Paris-Harrington Theorem（巴黎-哈灵顿定理）
- [[pass-at-k-vs-pass-k]] — Pass@k vs Pass^k（能力上限 vs 可靠性下限）
- [[path-tracing]] — 路径追踪 (Path Tracing)
- [[peano-arithmetic]] — 皮亚诺算术 (Peano Arithmetic, PA)
- [[perception-gap]] — 感知鸿沟 (Perception Gap)
- [[policy-reincarnation]] — Policy Reincarnation
- [[positive-sample-reinforcement]] — Positive Sample Reinforcement (PSR)
- [[post-train-space-rl]] — Post-train Space Reinforcement Learning
- [[practitioner-research-gap]] — Practitioner-Research Gap（从业者-研究鸿沟）
- [[pre-activation-history]] — Pre-Activation History
- [[pre-train-space-reinforcement-learning]] — Pre-train Space Reinforcement Learning (PreRL)
- [[prefill-as-a-service]] — Prefill-as-a-Service (PrfaaS)
- [[prefill-decode-disaggregation]] — Prefill-Decode 分离架构 (PD Disaggregation)
- [[prefix-matching]] — Prefix Matching（前缀匹配）
- [[primitive-completeness]] — Primitive Completeness (原语完备性)
- [[primitive-recursive-functions]] — 原始递归函数 (Primitive Recursive Functions)
- [[probabilistic-method]] — Probabilistic Method（概率方法）
- [[procedural-task-execution]] — 程序性任务执行 (Procedural Task Execution)
- [[program-synthesis]] — Program Synthesis (程序合成)
- [[prompt-caching]] — Prompt Caching
- [[prompt-layering]] — Prompt Layering（提示分层）
- [[prompt-reverse-engineering]] — 图片反推 Prompt (Prompt Reverse Engineering)
- [[prompt-to-harness-evolution]] — Prompt-to-Harness Evolution（三阶段工程演进）
- [[query-intent-analyzer]] — Query Intent Analyzer
- [[question-quality-vs-quantity]] — Question Quality vs. Quantity（问题质量 vs 数量）
- [[rag-systems]] — RAG 系统
- [[ramsey-context-cache]] — Ramsey Context Cache（拉姆齐上下文缓存）
- [[ramsey-context-graph]] — Ramsey Context Graph（拉姆齐上下文图）
- [[ramsey-context-template]] — Ramsey Context Template（拉姆齐上下文模板）
- [[ramsey-numbers]] — Ramsey Numbers（拉姆齐数）
- [[ramsey-theory]] — Ramsey Theory（拉姆齐理论）
- [[ramsey-theory-applications]] — Ramsey Theory Applications（拉姆齐理论应用）
- [[random-access-binding]] — Random-Access Binding (随机访问绑定)
- [[random-graph-theory]] — Random Graph Theory（随机图理论）
- [[real-life-context-learning]] — 真实生活上下文学习 (Real-Life Context Learning)
- [[rectified-flows]] — Rectified Flows
- [[recursive-reasoning-models]] — Recursive Reasoning Models（递归推理模型）
- [[recursive-self-improvement]] — Recursive Self-Improvement (递归自我改进)
- [[reference-gap]] — 引用鸿沟 (Reference Gap)
- [[reinforcement-learning-trading]] — Reinforcement Learning Trading（强化学习交易）
- [[rejected-edit-buffer]] — Rejected-Edit Buffer (拒绝编辑缓冲)
- [[relational-graph]] — Relational Graph
- [[reliable-state-long-running-agents]] — Reliable State in Long-Running Agents（长期运行中的可靠状态）
- [[replay-buffer-rl-llm]] — Replay Buffer 在 LLM RL 中的应用
- [[representation-alignment]] — Representation Alignment
- [[reverse-proxy-authentication]] — 反向代理认证 (Reverse Proxy Authentication)
- [[reward-hacking-llm]] — LLM 奖励黑客 (Reward Hacking in LLMs)
- [[reward-model]] — 奖励模型 (Reward Model, RM)
- [[reward-recency-sampling]] — 奖励-最近度混合采样
- [[risograph-print-style]] — Riso 印刷风格 (Risograph Print Style)
- [[rlvr-unified-framework]] — RLVR 统一理论框架
- [[rolling-kv-cache]] — 滚动 KV 缓存 (Rolling KV Cache)
- [[rotary-position-embedding]] — 旋转位置编码 (RoPE)
- [[round-trip-reconstruction-score]] — Round-Trip Reconstruction Score (RS@k)
- [[rule-system-application]] — 规则系统应用 (Rule System Application)
- [[russells-paradox]] — 罗素悖论 (Russell's Paradox)
- [[russian-constructivism]] — 俄国构成主义 (Russian Constructivism)
- [[s-token]] — S-Token (Superposed Token)
- [[sde-sampler-language]] — SDE Sampler for Language Diffusion
- [[searcher-trainer-decoupling]] — Searcher-Trainer 解耦架构
- [[secure-containers]] — 安全容器
- [[seer-attention]] — SeerAttention
- [[self-conditioning]] — Self-Conditioning
- [[self-evolving-agents]] — Self-Evolving Agents (自进化 Agent)
- [[self-evolving-benchmark]] — 自进化基准 (Self-Evolving Benchmark)
- [[self-improving-ai]] — Self-Improving AI (自我改进人工智能)
- [[self-reference]] — 自指 (Self-Reference)
- [[self-verification-rewards]] — 自我验证奖励 (Self-Verification Rewards)
- [[semantic-equivalence]] — Semantic Equivalence / 语义等价
- [[shadow-calling]] — Shadow Calling (影子调用)
- [[shared-parameter-influence]] — Shared Parameter Influence
- [[shared-weight-discretization]] — Shared-Weight Discretization
- [[singularity]] — Singularity (奇点)
- [[sink-token]] — 汇 Token (Sink Token)
- [[skill-as-external-state]] — Skill as External State (Skill 作为外部状态)
- [[skill-data-flywheel]] — Skill Data Flywheel (Skill 数据飞轮)
- [[skill-ecosystem]] — Skill Ecosystem (Skill 生态系统)
- [[skillopt]] — SkillOpt
- [[slow-meta-update]] — Slow/Meta Update (慢/元更新)
- [[softmax-off-by-one]] — SoftMax-off-by-One
- [[sparse-attention-patterns]] — 稀疏注意力模式 (Sparse Attention Patterns)
- [[specialist-training-pipeline]] — Specialist Training Pipeline
- [[specialized-rl]] — 专项强化学习 (Specialized RL)
- [[specialized-sft]] — 专项监督微调 (Specialized SFT)
- [[spiking-neural-networks]] — Spiking Neural Networks (SNN)
- [[spurious-predictability]] — Spurious Predictability
- [[stage-matched-data-config]] — Stage-Matched Data Configuration (分阶段数据配置)
- [[standard-agent-handoffs]] — Standard Agent Handoffs（标准化 Agent 交接）
- [[staug]] — STAug (EMD-based Augmentation)
- [[stochastic-latent-trajectory]] — Stochastic Latent Trajectory（随机潜在轨迹）
- [[strategy-engineering-unification]] — Strategy-Engineering Unification (策略与工程统一)
- [[structured-knowledge]] — 结构化知识 (Structured Knowledge)
- [[stub-pattern]] — Stub Pattern（轻量化桩模式）
- [[subquadratic-transformer-alternatives]] — 次二次 Transformer 替代方案
- [[sufficient-context-paradox]] — 充分上下文悖论 (Sufficient Context Paradox)
- [[swe-bench]] — SWE-bench
- [[symbolic-backpropagation]] — Symbolic Back-Propagation (符号反向传播)
- [[symbolic-network]] — Symbolic Network (符号网络)
- [[symbolic-regression]] — Symbolic Regression
- [[synapse-model]] — Synapse Model
- [[synthetic-data-qa-generation]] — Synthetic Data QA Generation (合成数据Q&A生成)
- [[system-2-thinking]] — System 2 思维
- [[system-message-abuse]] — System Message Abuse（系统消息滥用）
- [[szemerédi-regularity-lemma]] — Szemerédi Regularity Lemma
- [[tabular-foundation-models]] — Tabular Foundation Models
- [[tba]] — Trajectory Balance with Asynchrony (TBA)
- [[temporal-decay-neural]] — Temporal Decay (Neural)
- [[temporal-patch-shuffle]] — Temporal Patch Shuffle (TPS)
- [[terminal-bench]] — Terminal-Bench
- [[test-time-scaling]] — Test-Time Scaling
- [[test-time-training-rl]] — 测试时训练 RL (Test-Time Training with RL)
- [[text-space-optimizer]] — Text-Space Optimizer (文本空间优化器)
- [[text-vs-weight-optimization]] — Text vs Weight Optimization (文本 vs 权重优化)
- [[textual-learning-rate]] — Textual Learning Rate (文本学习率)
- [[thompson-sampling-code-search]] — Thompson Sampling Code Search
- [[three-engineering-phases]] — Three Engineering Phases（三阶段工程演进）
- [[throughput-hypothesis]] — Throughput Hypothesis (吞吐量假说)
- [[time-series-forecasting-augmentation]] — Time Series Forecasting Augmentation
- [[time-variant-dynamics]] — Time-variant Dynamics（时变动力学）
- [[token-efficiency]] — Token 效率 (Token Efficiency)
- [[token-superposition-training]] — Token Superposition Training (TST)
- [[tool-bootstrapped-rft]] — Tool-Bootstrapped GUI RFT
- [[tool-efficient-path-reward]] — Tool-Efficient Path Reward
- [[tool-interface]] — Tool Interface & Protocol Layer（工具接口与协议层）
- [[tool-registry]] — ToolRegistry
- [[trace-native-evaluation]] — Trace-Native Evaluation（踪迹原生评估）
- [[trading-lifecycle-driven-eviction]] — Trading-Lifecycle Driven Eviction (交易生命周期驱动淘汰)
- [[trajectory-balance-objective]] — Trajectory Balance (TB) 目标
- [[transfer-learning]] — Transfer Learning (迁移学习)
- [[two-phase-pretraining]] — Two-Phase Pre-Training
- [[ultradata]] — UltraData
- [[unconditional-generation-latent]] — Unconditional Generation via Latent Reasoning
- [[unified-rft]] — 统一拒绝采样微调 (Unified RFT)
- [[unsupervised-rlvr]] — 无监督可验证奖励强化学习 (URLVR)
- [[update-magnitude-imbalance]] — GRPO 更新幅度不平衡
- [[userspace-kernel]] — 用户空间内核
- [[van-der-waerden-theorem]] — van der Waerden Theorem
- [[verification-evaluation]] — Verification & Evaluation（验证与评估）
- [[visual-primitives]] — 视觉原语 (Visual Primitives)
- [[wavemask-wavemix]] — WaveMask / WaveMix
- [[width-based-scaling]] — Width-Based Scaling（宽度扩展）
- [[window-attention]] — 窗口注意力 (Window Attention)
- [[worst-case-threat-model]] — 最坏情况威胁模型
- [[x-prediction-parameterization]] — x-Prediction Parameterization
- [[zero-cost-proxies]] — Zero-Cost Proxies (ZCP)

## Papers

- [[agarwal-bayesian-attention-geometry]] — The Bayesian Geometry of Transformer Attention
- [[agent-harness-engineering-survey]] — Agent Harness Engineering: A Survey
- [[bartoldson-tba-2025]] — TBA: 异步轨迹平衡 — 解耦探索与学习以实现快速可扩展的 LLM 后训练
- [[behrouz-memory-caching-rnn]] — Memory Caching: RNNs with Growing Memory
- [[clawless-ai-agent-security]] — ClawLess: AI 代理安全模型
- [[dai-mathforge-2026]] — MathForge: Harder Is Better — 难度感知GRPO与多维度问题改写
- [[darlow-ctm-2025]] — Continuous Thought Machines (CTM)
- [[deepseek-v4-million-token-context]] — DeepSeek-V4: 迈向高效百万 Token 上下文智能
- [[dou-cl-bench]] — CL-bench: 上下文学习基准——首篇定义context learning范式的论文
- [[elf-embedded-language-flows]] — ELF: Embedded Language Flows
- [[godel-incompleteness-tutorial]] — 哥德尔不完备定理教程
- [[gram-generative-recursive-reasoning-paper]] — Generative Recursive Reasoning (GRAM)
- [[he-urlvr-sharpening-2026]] — How Far Can Unsupervised RLVR Scale LLM Training?
- [[hunyuan-team-cl-bench-life]] — CL-Bench Life: 真实生活上下文学习基准
- [[kore-knowledge-injection]] — KORE: Knowledge-Oriented Controls for Knowledge Injection
- [[laban-llms-corrupt-documents-delegate]] — LLMs Corrupt Your Documents When You Delegate
- [[li-amd-human-perception]] — "Are You Sure?": Human Perception Vulnerability in LLM Agents
- [[liu-koopa-2023]] — Koopa: Koopman 预测器驱动的非平稳时间序列学习
- [[llm-attention-survey-2026]] — 大语言模型注意力机制全面分析
- [[lou-autoharness-2026]] — AutoHarness: LLM Agent 的自动代码 Harness 合成
- [[nikolopoulos-spurious-predictability]] — Spurious Predictability in Financial Machine Learning
- [[odrzywolek-eml-single-operator]] — All elementary functions from a single binary operator
- [[peng-tst-2026]] — Token Superposition Training: 高效 LLM 预训练的 Token 叠加方法
- [[pre-train-space-reinforcement-learning]] — Pre-train Space Reinforcement Learning (PreRL/DSRL)
- [[qin-prfaas-cross-datacenter]] — Prefill-as-a-Service: KVCache Goes Cross-Datacenter
- [[ramsey-numbers-survey]] — 拉姆齐数的数学综述
- [[song-agent-network-taxonomy]] — Complex networks of AI agentic systems: 拓扑-记忆-更新三层分类法
- [[streaming-llm]] — StreamingLLM: 基于注意力汇的高效流式语言模型
- [[tao-klowden-ai-mathematical-methods]] — Mathematical methods and human thought in the age of AI
- [[thinking-with-visual-primitives]] — Thinking with Visual Primitives — 以视觉原语思考
- [[toolcua-optimal-gui-tool-orchestration]] — ToolCUA: Optimal GUI-Tool Path Orchestration for Computer Use Agents
- [[when-large-multimodal-models-confront-evolving-knowledge]] — When Large Multimodal Models Confront Evolving Knowledge
- [[xing-trails-2024]] — Trails: Database Native Model Selection (VLDB 2024)
- [[yang-skillopt-2026]] — SkillOpt: Agent Skill 的文本空间优化器
- [[zeng-dynamic-model-slicing-2024]] — Powering In-Database Dynamic Model Slicing for Structured Data Analytics (VLDB 2
- [[zeng-neurida-2025]] — NeurIDA: Dynamic Modeling for Effective In-Database Analytics
- [[zhang-hyperagents]] — Hyperagents: Self-Referential Agents with Metacognitive Self-Modification
- [[zhao-neurdb-2025]] — NeurDB: On the Design and Implementation of an AI-powered Autonomous Database (C
- [[zhou-agent-symbolic-learning-2024]] — Agent Symbolic Learning: 用符号学习实现自进化 Agent
- [[zhu-moda-mixture-of-depths]] — Mixture-of-Depths Attention (MoDA)

## Articles

- [[caddy-reverse-proxy-auth]] — Caddy 反向代理认证方案
- [[claw-eval]] — Claw-Eval：面向自主Agent的端到端评测框架
- [[crawl4ai-open-source-web-crawler]] — Crawl4AI：赋能AI用户的开源智能网页爬虫与数据提取工具
- [[distributed-agent-cache-sync-2026]] — 分布式Agent缓存同步：从单机到多机的Prompt Caching架构升级
- [[gpt-image2-prompt-collection]] — GPT-Image-2 绘图 Prompt 方法论与风格合集
- [[lyu-model-harness-evolution-2026]] — Model与Harness的关系演进：从AutoHarness到Heuristic Learning
- [[lyu-skillopt-deep-dive-2026]] — SkillOpt深度解读：自进化Agent技能的'反向传播'与工程化Continued Evolve
- [[mini-agent-harness]] — 从零搭建 Mini Agent Harness
- [[oppo-multimodal-data-lake]] — OPPO 多模态数据湖架构实践
- [[prompt-caching-architecture]] — Prompt Caching 架构工程手册
- [[ramsey-context-construction]] — 上下文构造与拉姆齐数
- [[temporal-patch-shuffle-tps]] — 时序预测增强方法综述：从频域到 TPS
- [[ultradata-l3-open-source-2026]] — UltraData：面壁智能L3数据开源与数据分级治理体系

## Special Pages

- [[index]] — 
- [[log]] — 
- [[README]] — 
- [[SCHEMA]] — 

## Reviews

- [[toolcua-review-20260531]] — ToolCUA Review: GUI-Tool路径编排的概念网络分析


- [[agent-harness-engineering-review-20260523]] — Review: Agent Harness Engineering Survey
- [[agent-network-taxonomy-review-20260501]] — Agent网络三层分类法 — Review 报告
- [[cl-bench-life-review-20260501]] — CL-Bench Life 论文集成 Review
- [[cl-bench-review-20260501]] — CL-bench 论文集成 Review
- [[clawless-review-20260422]] — ClawLess: AI 代理安全模型 - Review 报告
- [[ctm-review-20260515]] — Continuous Thought Machines 论文集成 Review
- [[delegate52-review-20260514]] — DELEGATE-52 Review
- [[distributed-agent-cache-sync-review]] — Review: 分布式Agent缓存同步
- [[elf-embedded-language-flows-review-20260513]] — Review: ELF — Embedded Language Flows
- [[godel-tutorial-review-20260428]] — 哥德尔不完备定理教程 — Review 报告
- [[hyperagents-review-20260420]] — 📚 Wiki 添加 Review 报告 - Hyperagents 论文
- [[koopa-review-20260511]] — Review: Koopa — Koopman 预测器驱动的非平稳时序学习
- [[kore-review-20260521]] — KORE Review
- [[llm-attention-survey-review-20260429]] — Review: 大语言模型注意力机制全面分析
- [[lou-autoharness-review]] — Review: AutoHarness — 自动合成代码 Harness 改进 LLM Agent
- [[lyu-model-harness-review]] — Review: Model与Harness的关系演进
- [[lyu-skillopt-deep-dive-review]] — Review: SkillOpt深度解读 — 自进化Agent的'反向传播'
- [[mathforge-review-20260512]] — MathForge Review — 2026-05-12
- [[neurida-review-20260515]] — NeurIDA 论文集成 Review
- [[peng-tst-2026-review]] — Review: Token Superposition Training
- [[pretrain-space-rl-review-20260518]] — Review: Pre-train Space Reinforcement Learning
- [[prompt-caching-architecture-review-20260511]] — Review: Prompt Caching 架构工程手册
- [[ramsey-context-construction-review-20260511]] — Review: 上下文构造与拉姆齐数
- [[ramsey-numbers-survey-review-20260511]] — Review: 拉姆齐数的数学综述
- [[streaming-llm-review-20260514]] — Review: StreamingLLM — 基于注意力汇的无限长流式语言模型
- [[tba-review-20260512]] — TBA Review — 2026-05-12
- [[thinking-with-visual-primitives-review-20260430]] — Review — Thinking with Visual Primitives
- [[ultradata-l3-review]] — Review: UltraData — 大模型数据分级治理的开源实践
- [[yang-skillopt-review]] — Review: SkillOpt — Agent Skill 的文本空间优化器
- [[zhou-agent-symbolic-learning-review]] — Review: Agent Symbolic Learning — 符号学习驱动的自进化Agent