1273 lines
86 KiB
Markdown
1273 lines
86 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：2026-06-25 | 总页面数：1249
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## Concepts
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- [[4d-gaussian-splatting]] — 4D 高斯泼溅 (4D Gaussian Splatting)
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- [[abductive-reasoning-recommendation]] — 溯因推理 (推荐) — Abductive Reasoning in Recommendation
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- [[absolute-gating]] — 绝对门控与相对门控 (Absolute vs Relative Gating)
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- [[abstract-representation-space]] — 抽象表征空间 (Abstract Representation Space)
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- [[ace-router]] — ACE-Router — 训练专用路由器
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- [[action-applicability]] — Action Applicability (动作合法性判定)
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- [[action-consequence-prediction]] — 预测行动后果 (Action Consequence Prediction)
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- [[action-decoder]] — 动作解码器 (Action Decoder)
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- [[action-head-router]] — 动作头路由器 (Action Head Router)
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- [[action-realization-layer]] — Action Realization Layer（动作实现层）
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- [[action-routing-policy]] — 动作路由策略 (Action-Routing Policy)
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- [[activation-manifold]] — Activation Manifold
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- [[activation-steering]] — Activation Steering
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- [[active-cache-warmup]] — Active Cache Warm-up (主动缓存预热)
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- [[active-tool-discovery]] — 主动工具发现 — Active Tool Discovery
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- [[active-tool-request]] — Active Tool Request — 结构化工具请求
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- [[adapter-protocol]] — 适配器协议 (Adapter Protocol)
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- [[adaptive-adversary]] — 自适应对手 (Adaptive Adversary)
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- [[adaptive-computation-time]] — Adaptive Computation Time (ACT)
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- [[adaptive-harness-simplification]] — Adaptive Harness Simplification（自适应 Harness 简化）
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- [[additive-combinatorics]] — Additive Combinatorics（加法组合学）
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- [[adkv]] — AdaKV
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- [[agent-capability-stability-gap]] — Agent Capability-Stability Gap（能力-稳定性差距）
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- [[agent-communication-stack]] — Agent通信协议栈
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- [[agent-completion-evaluation]] — Agent Completion Evaluation（Agent 完成度评测）
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- [[agent-computer-interface]] — Agent-Computer Interface (ACI)
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- [[agent-eval-case-design]] — Agent Eval Case Design
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- [[agent-eval-grader]] — Agent Eval Grader
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- [[agent-eval-trace]] — Agent Eval Trace
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- [[agent-evaluation-paradigm-shift]] — Agent 评测范式转变（Paradigm Shift in Agent Evaluation）
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- [[agent-frameworks-to-platforms]] — Agent Frameworks to Platforms（从 Agent 框架到 Agent 平台）
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- [[agent-governance]] — Agent Governance（Agent 治理与安全）
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- [[agent-harness]] — Agent Harness (Claw)
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- [[agent-harness-engineering]] — Agent Harness Engineering（Agent 执行骨架工程）
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- [[agent-harness-mini]] — Mini Agent Harness
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- [[agent-harness-safety]] — Agent Harness Safety
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- [[agent-mediated-deception]] — 代理中介欺骗 (Agent-Mediated Deception)
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- [[agent-memory-five-category-model]] — Agent Memory Five-Category Model (sz 设计)
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- [[agent-memory-lifecycle]] — Agent 记忆生命周期
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- [[agent-memory-system]] — Agent 记忆系统
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- [[agent-memory-taxonomy]] — Agent Memory Taxonomy (三索引分型)
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- [[agent-multidimensional-capability]] — Agent Multidimensional Capability（Agent 多维能力）
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- [[agent-network-memory-scope]] — Agent网络记忆范围
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- [[agent-network-taxonomy]] — Agent网络三层分类法
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- [[agent-network-topology]] — Agent网络拓扑
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- [[agent-network-update-behavior]] — Agent网络更新行为
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- [[agent-observability]] — Agent Observability（Agent 可观测性）
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- [[agent-process-evaluation]] — Agent Process Evaluation（过程评测）
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- [[agent-robustness-evaluation]] — Agent Robustness Evaluation（Agent 鲁棒性评测）
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- [[agent-safety-evaluation]] — Agent Safety Evaluation（Agent 安全评测）
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- [[agent-sandbox]] — Agent Sandbox（Agent 沙箱）
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- [[agent-skill]] — Agent Skill — 可复用过程性构件
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- [[agent-skill-atomization]] — Agent Skill 原子化
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- [[agent-skill-ecosystem]] — Agent Skill 生态系统
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- [[agent-symbolic-learning]] — Agent Symbolic Learning (Agent 符号学习)
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- [[agent-token-budget-optimization]] — Agent Token Budget Optimization
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- [[agent-verification]] — Agent Verification（Agent 验证与评估）
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- [[agent-web]] — Agent Web — 开放协作智能体网络
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- [[agentic-cache-manager]] — Agentic Cache Manager
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- [[agentic-rag]] — Agentic RAG
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- [[agentic-streaming-inference]] — Agentic Streaming Inference
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- [[agentic-systems]] — Agentic Systems（智能体系统）
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- [[agi-critique]] — AGI 批判（AGI Critique）
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- [[ai-agent-security]] — AI代理安全
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- [[ai-alignment]] — AI Alignment (AI对齐)
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- [[ai-mathematics]] — AI and Mathematics (AI 与数学)
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- [[ai-production-tradeoffs]] — AI 生产权衡 — 六大维度
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- [[ai-safety]] — AI Safety (AI安全)
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- [[aidb]] — AIDB（大模型友好数据层）
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- [[aleatoric-uncertainty]] — 随机不确定性 (Aleatoric Uncertainty)
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- [[algebraic-numbers-countability]] — 代数数的可数性
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- [[algorithmic-equity]] — 算法公平性 (Algorithmic Equity)
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- [[amortized-variational-inference]] — Amortized Variational Inference（摊销变分推断）
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- [[analytical-report-synthesizer]] — Analytical Report Synthesizer
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- [[and-or-interactions]] — AND-OR 交互 (AND-OR Interactions)
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- [[anthropic-agent-evals]] — Anthropic Agent Evals
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- [[anthropomorphization-critique]] — 人类化机器批判（Anthropomorphization Critique）
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- [[api-key-authentication]] — API Key 认证 (API Key Authentication)
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- [[appearance-bias-vla]] — Appearance Bias in VLA
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- [[arxiv]] — arXiv
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- [[asymmetric-grounding-adherence-loss]] — Asymmetric Grounding Adherence Loss (L_AGA)
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- [[asynchronous-rl-llm]] — 异步强化学习与大语言模型后训练
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- [[atlas-memory-system]] — Atlas Memory System
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- [[attention-entropy-collapse]] — 注意力熵崩溃 (Attention Entropy Collapse)
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- [[attention-mechanism]] — Attention Mechanism
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- [[attention-sinks]] — 注意力汇 (Attention Sinks)
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- [[attractor-dynamics]] — 吸引子动力学 (Attractor Dynamics)
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- [[audio-visual-generation]] — Audio-Visual Generation
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- [[audio-visual-representation-alignment]] — Audio-Visual Representation Alignment
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- [[autoharness]] — AutoHarness
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- [[automated-theorem-proving]] — 自动定理证明 (Automated Theorem Proving, ATP)
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- [[automatic-prompt-optimization]] — APO 自动提示工程 (Automatic Prompt Optimization)
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- [[autonomous-optimization-ao]] — Autonomous Optimization (AO)
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- [[autoregressive-unrolling]] — 自回归展开 (Autoregressive Unrolling)
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- [[autoregressive-video-generation]] — Autoregressive Video Generation
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- [[auxiliary-predictive-objectives]] — 辅助预测目标 (Auxiliary Predictive Objectives)
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- [[backtranslation-round-trip-relay]] — Backtranslation Round-Trip Relay
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- [[banach-space]] — Banach 空间 (Banach Space)
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- [[bare-adapter]] — Bare Adapter
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- [[barker-gibbs]] — Barker Gibbs
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- [[base-table-embedding]] — Base Table Embedding
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- [[bastiani-calculus]] — Bastiani 微积分 (Bastiani Calculus)
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- [[batch-vs-real-time-inference]] — 批处理推理 vs 实时推理
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- [[bayesian-attention-geometry]] — Bayesian Attention Geometry (贝叶斯注意力几何)
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- [[bayesian-attention-trilogy]] — Bayesian Attention Trilogy
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- [[bayesian-deep-learning]] — 贝叶斯深度学习 (Bayesian Deep Learning)
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- [[bayesian-filtering]] — 贝叶斯滤波
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- [[bayesian-nonparametric-tpp]] — 贝叶斯非参数 TPP (Bayesian Nonparametric TPP)
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- [[bayesian-wind-tunnels]] — Bayesian Wind Tunnels
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- [[belief-accumulation]] — Belief Accumulation (信念累积)
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- [[belief-state]] — 信念状态 (Belief State)
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- [[belief-transport]] — Belief Transport (信念传输)
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- [[bellman-taylor-score-decoding]] — Bellman-Taylor 得分解码 (BTSD)
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- [[bidirectional-trajectory-evaluation]] — 双向轨迹评估 (Bidirectional Trajectory Evaluation)
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- [[binding-constraint-thesis]] — Binding-Constraint Thesis（约束瓶颈论）
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- [[block-causal-attention]] — Block-Causal Attention
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- [[block-sparse-attention]] — Block-Sparse Attention Mask (分块稀疏注意力掩码)
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- [[bm25-financial-retrieval]] — BM25 金融检索
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- [[boundary-compliance]] — Boundary Compliance
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- [[bounded-reuse]] — 有界复用 (Bounded Reuse)
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- [[bpf-syscall-interception]] — BPF系统调用拦截
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- [[btsd-ppo]] — BTSD-PPO
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- [[build-vs-buy-llm]] — 构建 vs 购买 — Build vs Buy (LLM)
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- [[bypass-network-handle-distribution]] — Bypass Network Handle Distribution (旁路网络句柄分发)
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- [[cace-principle]] — CACE 原理 — Change Anything Changes Everything
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- [[cache-cold-start]] — Cache Cold-Start (缓存冷启动)
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- [[cache-health-observability]] — Cache Health Observability（缓存健康度可观测性）
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- [[cache-hit-ratio]] — Cache Hit Ratio (CHR)
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- [[cache-invalidation]] — Cache Invalidation（缓存失效）
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- [[cache-safe-forking]] — Cache-Safe Forking（缓存安全分叉）
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- [[caddy-web-server]] — Caddy Web Server
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- [[candidate-graph]] — 候选图 — Candidate Graph
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- [[capability-control-tradeoff]] — Capability-Control Tradeoff（能力-控制权衡）
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- [[capability-degradation]] — 能力退化 (Capability Degradation)
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- [[catastrophic-forgetting]] — 灾难性遗忘 (Catastrophic Forgetting)
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- [[causal-decomposition-pomg]] — 因果分解 (Causal Decomposition in POMG)
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- [[causal-generation]] — Causal Generation
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- [[causal-information-flow]] — Causal Information Flow
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- [[causal-multimodal-vae]] — Causal Multimodal VAE
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- [[cel-shading-style]] — 赛璐璐风格 (Cel-Shading)
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- [[center-manifold-theorem]] — Center Manifold Theorem (中心流形定理)
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- [[centralized-agent-architecture]] — 集中式Agent架构
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- [[certainty-based-loss]] — Certainty-Based Loss
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- [[certainty-based-rewards]] — 确定性奖励 (Certainty-Based Rewards)
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- [[chain-of-thought]] — 思维链 (Chain-of-Thought, CoT)
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- [[chaitin-algorithmic-information-theory]] — 算法信息论 (Algorithmic Information Theory, AIT)
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- [[chaitin-constant]] — 蔡廷常数 Ω (Chaitin's Constant)
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- [[cl-bench-life]] — CL-Bench Life
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- [[classifier-free-guidance-language]] — Classifier-Free Guidance for Language
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- [[claw-swe-bench-lite]] — Claw-SWE-Bench Lite
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- [[clawforce]] — ClawForce — 企业 AI Agent 方案
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- [[clawless]] — ClawLess
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- [[clean-conditioning-mask]] — 清洁条件掩码 (Clean-Conditioning Mask)
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- [[clinical-ai]] — 临床人工智能 (Clinical AI)
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- [[coarse-grained-counting]] — 粗粒度计数 (Coarse-grained Counting)
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- [[coarse-grained-recurrence]] — 粗粒度循环 (Coarse-Grained Recurrence)
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- [[coarse-to-fine-granularity]] — Coarse-to-Fine Granularity
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- [[coconut]] — COCONUT: 连续潜空间推理
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- [[code-as-harness]] — Code as Harness
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- [[cognitive-architecture]] — Cognitive Architecture (认知架构)
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- [[collectivist-ai]] — 集体主义 AI（Collectivist AI）
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- [[compiled-ai-paradigm]] — Compiled AI Paradigm (编译型 AI 范式)
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- [[completeness-logic]] — 完备性 (Completeness, 逻辑学)
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- [[composable-base-model-architecture]] — Composable Base Model Architecture
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- [[compressed-sparse-attention]] — Compressed Sparse Attention (CSA)
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- [[computability-theory]] — 可计算性理论 (Computability Theory)
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- [[computer-use-agents]] — Computer Use Agents (CUAs)
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- [[computerized-adaptive-testing]] — Computerized Adaptive Testing (CAT)
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- [[concept-lattice]] — 概念格 (Concept Lattice)
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- [[concept-learning]] — 概念学习：几何视角 (Concept Learning: Geometric View)
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- [[conditional-intensity-function]] — 条件强度函数 (Conditional Intensity Function)
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- [[conditional-memory]] — Conditional Memory
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- [[conditional-model-dispatcher]] — Conditional Model Dispatcher
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- [[confidence-correctness-alignment]] — 置信度-正确性对齐 (Confidence-Correctness Alignment)
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- [[consistency-logic]] — 一致性 (Consistency, 逻辑学)
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- [[constant-kv-cache]] — Constant KV Cache
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- [[content-based-reasoning]] — Content-Based Reasoning
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- [[content-diversity-decay]] — 内容多样性衰减（Content Diversity Decay）
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- [[content-grounded-retrieval]] — Content-Grounded Retrieval — Faithfulness as First Principle
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- [[content-homogenization]] — 内容同质化（Content Homogenization）
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- [[content-question-answering]] — Content Question Answering (CQA)
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- [[context-anchoring]] — 历史上下文锚定（Context Anchoring）
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- [[context-blue-clique]] — Context Blue Clique（上下文蓝色团）
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- [[context-compression]] — Context Compression（上下文压缩）
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- [[context-drift]] — Context Drift（上下文漂移）
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- [[context-engineering]] — Context Engineering（上下文工程）
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- [[context-enriched-embeddings]] — 上下文增强嵌入 — Context Enriched Embeddings
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- [[context-learning]] — 上下文学习 (Context Learning)
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- [[context-management]] — Context Management（上下文管理）
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- [[context-misuse]] — 上下文误用 (Context Misuse)
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- [[context-pruning]] — Context Pruning (上下文剪枝)
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- [[context-state-estimation]] — Context as State Estimation（上下文作为状态估计）
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- [[continual-learning]] — 持续学习 (Continual Learning)
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- [[continuation-value-function]] — 延续价值函数 (Continuation Value Function)
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- [[continuous-diffusion-language-models]] — Continuous Diffusion Language Models
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- [[continuous-representation]] — 连续表征 (Continuous Representation)
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- [[continuous-thought-machine]] — Continuous Thought Machine (CTM)
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- [[continuous-time-rl]] — 连续时间强化学习 (Continuous-Time RL)
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- [[continuum-hypothesis]] — 连续统假设 (Continuum Hypothesis, CH)
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- [[control-affine-mdp]] — 控制仿射 MDP (Control-Affine MDP)
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- [[controlled-autonomy]] — Controlled Autonomy (受控的自主性)
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- [[controlled-text-generation]] — Controlled Text Generation
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- [[convex-hull-relaxation]] — Convex-Hull Relaxation (KV Cache)
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- [[coordinator-executor-architecture]] — Coordinator-Executor Architecture
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- [[cost-aware-benchmarking]] — 代价感知基准评测 (Cost-Aware Benchmarking)
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- [[cost-quality-speed-trilemma]] — Cost-Quality-Speed Trilemma（成本-质量-速度三元悖论）
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- [[countable-uncountable-infinity]] — 可数与不可数无穷
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- [[covariance-matrix]] — 协方差矩阵 (Covariance Matrix)
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- [[covariance-matrix-knowledge]] — 协方差矩阵知识存储 (Covariance Matrix Knowledge Storage)
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- [[cramer-rao-lower-bound]] — Cramér-Rao Lower Bound (CRLB)
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- [[crawl4ai]] — Crawl4AI
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- [[critical-failures]] — Critical Failures / 关键失败
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- [[critpt]] — CritPt (Critical Point Benchmark)
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- [[cross-head-budget-allocation]] — Cross-Head Budget Allocation
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- [[cross-model-harness-transfer]] — Cross-Model Harness Transfer（跨模型 Harness 迁移）
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- [[cross-section-synthesis]] — Cross-Section Synthesis — Information Integration Across Document Parts
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- [[curvine-distributed-cache]] — Curvine 云原生分布式缓存
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- [[dag-reasoning-evaluation]] — DAG-based Reasoning Evaluation
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- [[darwin-godel-machine]] — Darwin Gödel Machine (达尔文·哥德尔机)
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- [[data-augmentation]] — 数据增强 (Data Augmentation)
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- [[data-hierarchical-governance]] — Data Hierarchical Governance (L0-L4 数据分级治理)
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- [[data-label-consistency]] — Data-Label Consistency (数据-标签一致性)
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- [[data-markets]] — 数据市场（Data Markets）
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- [[data-quality-over-scale]] — Data Quality over Scale (数据质量重于规模)
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- [[data-quality-vs-quantity]] — 数据数量 vs 数据质量
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- [[data-replay]] — 数据回放 (Data Replay)
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- [[data-slice]] — Data Slice
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- [[data-swamp]] — 数据沼泽 — Data Swamp
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- [[data-wall]] — 数据墙 (Data Wall)
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- [[dcgwm]] — DCGWM (双通道接地世界建模)
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- [[ddcadam]] — DDCAdam (Dead-Direction-Calibrated Adam)
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- [[dead-direction]] — 死方向 (Dead Direction)
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- [[decentralized-agent-architecture]] — 去中心化Agent架构
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- [[deep-and-wide-reasoning]] — Deep-and-Wide Reasoning（深度且宽广的推理）
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- [[deep-gaussian-process]] — 深度高斯过程 (Deep Gaussian Process)
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- [[deep-rl-scaling]] — 扩展深度强化学习 (Scaling Deep RL)
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- [[deep-thinking-sft]] — Deep-Thinking SFT (深思考SFT数据)
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- [[deep-variational-implicit-process]] — 深度变分隐式过程 (DVIP)
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- [[deepencoder]] — DeepEncoder
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- [[deepseek-ocr]] — DeepSeek OCR
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- [[deepseek-r1]] — DeepSeek-R1
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- [[deepseek-v4-flash]] — DeepSeek-V4-Flash
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- [[deepseek-vit]] — DeepSeek-ViT
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- [[default-tools]] — Default Tools — 始终可用的通用工具
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- [[delegate-52]] — DELEGATE-52
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- [[delegated-work]] — Delegated Work / 委托工作
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- [[delta-rule]] — Delta Rule
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- [[depth-dilemma]] — 深度困境 (Depth Dilemma)
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- [[depth-recurrence]] — 深度循环 (Depth Recurrence)
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- [[depth-scaling-signal-degradation]] — LLM 深度扩展与信号退化
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- [[deterministic-agent-failures]] — Deterministic Agent Failures（确定性 Agent 失败分类）
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- [[dgae]] — Difficulty-Balanced Group Advantage Estimation (DGAE)
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- [[dgpo]] — Difficulty-Aware Group Policy Optimization (DGPO)
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- [[diagonal-ramsey-number]] — Diagonal Ramsey Number（对角拉姆齐数）
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- [[diagonalization-method]] — 对角线方法 (Diagonalization Method)
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- [[differentiable-token-budgeting]] — Differentiable Token Budgeting
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- [[diffusion-based-tpp]] — 扩散时间点过程 (Diffusion-based TPP)
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- [[diffusion-transformer]] — Diffusion Transformer (DiT)
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- [[dime-dynamic-in-database-modeling-engine]] — DIME (Dynamic In-Database Modeling Engine)
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- [[discrete-diffusion-language-models]] — discrete-diffusion-language-models
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- [[distractor-context]] — Distractor Context / 干扰上下文
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- [[distributed-cache-routing]] — Distributed Cache Routing (分布式缓存路由)
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- [[distributed-optimistic-locking]] — Distributed Optimistic Locking (分布式乐观锁)
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- [[distributed-prompt-caching]] — Distributed Prompt Caching (分布式提示词缓存)
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- [[distribution-shift]] — Distribution Shift（分布偏移）
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- [[document-degradation]] — Document Degradation / 文档退化
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- [[domain-aware-preference-optimization]] — Domain-Aware Preference Optimization
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- [[domain-knowledge-reasoning]] — 领域知识推理 (Domain Knowledge Reasoning)
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- [[domain-specific-evaluation]] — Domain-Specific Evaluation / 领域特定评估
|
||
- [[dominant-shuffle]] — Dominant Shuffle
|
||
- [[double-descent]] — 双下降 (Double Descent)
|
||
- [[dpo]] — DPO (Direct Preference Optimization)
|
||
- [[dpo-bias-mitigation]] — DPO Bias Mitigation
|
||
- [[dqw]] — Difficulty-Aware Question-Level Weighting (DQW)
|
||
- [[drift-detection]] — 漂移检测 (Drift Detection)
|
||
- [[drifting]] — Temporal Drift (时序漂移)
|
||
- [[dual-collapse]] — Dual Collapse in Latent CoT
|
||
- [[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-react]] — Dynamic ReAct — 动态工具选择
|
||
- [[dynamic-relation-modeling]] — Dynamic Relation Modeling
|
||
- [[dynamic-state-evolution]] — Dynamic State Evolution
|
||
- [[dynamic-token-limit]] — 动态 Token 限制 (Dynamic Token Limit)
|
||
- [[dynamic-weight-updates]] — Dynamic Weight Updates
|
||
- [[e-values]] — E-values（证据值）
|
||
- [[edge-of-stability]] — Edge of Stability (EoS)
|
||
- [[ellipsis-prompt]] — 省略号提示 (Ellipsis Prompt)
|
||
- [[eluder-dimension]] — Eluder 维度 (Eluder Dimension)
|
||
- [[embedded-language-flows]] — Embedded Language Flows (ELF)
|
||
- [[eml-operator]] — EML 算子 (Exp-Minus-Log)
|
||
- [[emmy-noether]] — 埃米·诺特 (Emmy Noether)
|
||
- [[emotional-reasoning-bias]] — Emotional Reasoning Bias
|
||
- [[emotional-value-evaluation]] — 情绪价值评估 (Emotional Value Evaluation)
|
||
- [[empirical-discovery-simulation]] — 经验发现与模拟 (Empirical Discovery & Simulation)
|
||
- [[empirical-fisher]] — Empirical Fisher (经验 Fisher 信息)
|
||
- [[end-to-end-ocr]] — End-to-End OCR
|
||
- [[end-to-end-streaming-interaction]] — End-to-End Streaming Interaction
|
||
- [[endogenous-reasoning]] — Endogenous Reasoning（内生推理）
|
||
- [[engram]] — Engram (Conditional Memory Module)
|
||
- [[enhanced-state-space-models]] — 增强状态空间模型 (Enhanced State-Space Models)
|
||
- [[ensemble-based-rewards]] — 集成奖励 (Ensemble-Based Rewards)
|
||
- [[environment-contract-layer]] — Environment Contract Layer（环境契约层）
|
||
- [[epistemic-uncertainty]] — 认知不确定性 (Epistemic Uncertainty)
|
||
- [[epoch-based-optimistic-mle]] — Epoch-based 乐观 MLE (Epoch-based Optimistic MLE)
|
||
- [[etclovg-taxonomy]] — ETCLOVG 七层分类法
|
||
- [[evolution-probe]] — 进化探针 (Evolution Probe)
|
||
- [[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）
|
||
- [[execution-fidelity]] — Execution Fidelity
|
||
- [[execution-harness]] — Execution Harness
|
||
- [[expected-calibration-error]] — 预期校准误差 (Expected Calibration Error, ECE)
|
||
- [[experience-distillation]] — 经验蒸馏 (Experience Distillation)
|
||
- [[experience-representation]] — 经验表示 (Experience Representation)
|
||
- [[exploratory-dynamics]] — 探索动力学 (Exploratory Dynamics)
|
||
- [[exponential-decay-reward]] — 指数衰减奖励 (Exponential Decay Reward)
|
||
- [[extended-kalman-filter]] — 扩展 Kalman 滤波
|
||
- [[fact-augmented-key-expansion]] — Fact-Augmented Key Expansion
|
||
- [[fading-memory]] — 衰减记忆 (Fading Memory)
|
||
- [[faithfulness-in-ai]] — Faithfulness in AI
|
||
- [[feature-absorption]] — 特征吸收 (Feature Absorption)
|
||
- [[feature-family]] — 特征家族 (Feature Family)
|
||
- [[feature-splitting]] — 特征分裂 (Feature Splitting)
|
||
- [[feedforward-depth-limitation]] — 前馈深度局限 (Feedforward Depth Limitation)
|
||
- [[few-shot-learning]] — Few-Shot Learning (少样本学习)
|
||
- [[fiber-of-parametrization]] — 参数化纤维 (Fiber of Parametrization)
|
||
- [[financial-agent-permission]] — 金融 Agent 权限管控
|
||
- [[financial-llm-deployment]] — 金融行业大模型部署约束
|
||
- [[financial-llm-model-selection]] — 金融大模型选型
|
||
- [[financial-llm-requirements]] — 金融行业好需求工程
|
||
- [[fine-grained-counting]] — 细粒度计数 (Fine-grained Counting)
|
||
- [[first-lyapunov-coefficient]] — First Lyapunov Coefficient (第一Lyapunov系数)
|
||
- [[fisher-information-metric]] — Fisher 信息度量 (Fisher Information Metric)
|
||
- [[fisher-lipschitz]] — Fisher-Lipschitz 假设类
|
||
- [[fisher-width]] — Fisher Width (Fisher 宽度)
|
||
- [[five-axis-positional-encoding]] — 五轴位置编码 (Five-Axis Positional Encoding)
|
||
- [[fixed-mean-gaussian-process]] — 固定均值高斯过程 (Fixed-Mean Gaussian Process)
|
||
- [[flash-attention]] — FlashAttention
|
||
- [[flash-attention-3]] — FlashAttention-3
|
||
- [[flex-attention]] — FlexAttention
|
||
- [[flip-bifurcation]] — Flip Bifurcation (翻转分岔)
|
||
- [[flow-matching]] — Flow Matching
|
||
- [[forecasting-augmentation-taxonomy]] — Forecasting Augmentation Taxonomy
|
||
- [[formal-concept-analysis]] — 形式概念分析 (Formal Concept Analysis)
|
||
- [[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)
|
||
- [[forward-repair-ladder]] — Forward-Repair Ladder
|
||
- [[foundation-model-frontier-bias]] — 基础模型前沿偏倚（Foundation Model Frontier Bias）
|
||
- [[fourier-filter-dynamics]] — Fourier Filter for Dynamics（Fourier Filter 动力学分解）
|
||
- [[fp4-quantization-training]] — FP4 Quantization-Aware Training
|
||
- [[freetimegs]] — FreeTimeGS
|
||
- [[freqmask-freqmix]] — FreqMask / FreqMix
|
||
- [[full-duplex-interaction]] — Full-Duplex Interaction
|
||
- [[function-space-modeling]] — 函数空间建模 (Function-Space Modeling)
|
||
- [[functional-input-neural-networks]] — 函数输入神经网络 (Functional Input Neural Network)
|
||
- [[furstenberg-correspondence]] — Furstenberg Correspondence Principle
|
||
- [[future-commit-cleanup]] — Future-Commit 清理 (Future-Commit Cleanup)
|
||
- [[gambling-gibbs]] — Gambling Gibbs
|
||
- [[gaussian-filtering]] — 高斯滤波
|
||
- [[gaussian-manifold]] — 高斯流形
|
||
- [[gaussian-process]] — 高斯过程 (Gaussian Process)
|
||
- [[gaussian-width]] — Gaussian Width (高斯宽度)
|
||
- [[gbrain-memory]] — GBrain Memory System
|
||
- [[gene-bench]] — Gene-Bench
|
||
- [[gene-evolution-protocol]] — 基因进化协议 (GEP)
|
||
- [[gene-probe]] — 基因探针 (Gene Probe)
|
||
- [[generalization-bounds]] — 泛化界 (Generalization Bounds)
|
||
- [[generalized-delta-rule]] — Generalized Delta Rule
|
||
- [[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
|
||
- [[generative-recommendation]] — 生成式推荐 (Generative Recommendation)
|
||
- [[generative-reconstruction-latent]] — Generative Reconstruction (Latent)
|
||
- [[genetic-programming]] — Genetic Programming (遗传编程)
|
||
- [[geometric-compression-latent]] — Geometric Compression (Latent CoT)
|
||
- [[geometric-ramsey-theory]] — Geometric Ramsey Theory（几何拉姆齐理论）
|
||
- [[georg-cantor]] — 格奥尔格·康托尔 (Georg Cantor)
|
||
- [[gflownet-fine-tuning]] — GFlowNet 微调
|
||
- [[gibbs-posterior]] — Gibbs 后验
|
||
- [[glitch-art-style]] — 故障艺术 (Glitch Art)
|
||
- [[global-combinatorial-optimization]] — Global Combinatorial Optimization (KV Cache)
|
||
- [[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）
|
||
- [[granger-causality-tpp]] — Granger 因果发现 (Granger Causality in TPP)
|
||
- [[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
|
||
- [[gumbel-softmax]] — Gumbel-Softmax 重参数化
|
||
- [[halftone-print-style]] — 半调印刷风格 (Halftone Print Style)
|
||
- [[hallucination-mitigation]] — Hallucination Mitigation in LLM Systems
|
||
- [[halting-problem]] — 停机问题 (Halting Problem)
|
||
- [[hard-token]] — Hard Token
|
||
- [[hardening-execution-environments]] — Hardening Execution Environments（硬化执行环境）
|
||
- [[hardware-aware-algorithm]] — Hardware-Aware Algorithm (Mamba)
|
||
- [[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
|
||
- [[harness-evolution]] — Harness Evolution（轨迹驱动的 Harness 进化）
|
||
- [[harness-model-interaction]] — Harness × Model 交互效应
|
||
- [[harnessaudit]] — HarnessAudit
|
||
- [[hars]] — HARS（调和适应保留评分）
|
||
- [[hawkes-process]] — Hawkes 过程 (Hawkes Process)
|
||
- [[head-level-budget-allocation]] — Head-Level Budget Allocation
|
||
- [[head-structure-ssm]] — SSM 多头结构 (Head Structure for SSMs)
|
||
- [[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)
|
||
- [[heuristic-metric]] — Heuristic Metric (KV Cache)
|
||
- [[hidden-audit-channel]] — Hidden Audit Channel
|
||
- [[hidden-symmetries-neural]] — 隐藏对称性 (Hidden Symmetries)
|
||
- [[hierarchical-semantic-routing]] — 层次语义路由 — Hierarchical Semantic Routing
|
||
- [[hierarchy-preservation]] — Hierarchy Preservation — Structural Knowledge for Literature Ranking
|
||
- [[hilberts-program]] — 希尔伯特计划 (Hilbert's Program)
|
||
- [[hippo]] — HiPPO (High-order Polynomial Projection Operators)
|
||
- [[history-aware-routing]] — 历史感知路由 — History-Aware Routing
|
||
- [[honest-open-subset]] — Honest 开子集 (Honest Open Subset)
|
||
- [[hrpo]] — HRPO: Hybrid Reasoning Policy Optimization
|
||
- [[human-agent-trust]] — 人机信任 (Human-Agent Trust)
|
||
- [[human-centered-ai]] — Human-Centered AI (以人类为中心的 AI)
|
||
- [[human-in-the-loop]] — Human-in-the-Loop — 人机协同
|
||
- [[hybrid-attention-architecture]] — Hybrid Attention Architecture
|
||
- [[hybrid-reasoning]] — 混合推理 (Hybrid Reasoning)
|
||
- [[hybrid-reasoning-models]] — 混合推理模型 (Hybrid Reasoning Models)
|
||
- [[hybrid-recall-pipeline]] — Hybrid Recall Pipeline (BM25 + Dense)
|
||
- [[hyperagents]] — Hyperagents (超智能体)
|
||
- [[hypergraph-ramsey-number]] — Hypergraph Ramsey Number（超图拉姆齐数）
|
||
- [[hyperplane-arrangements]] — 超平面排列 (Hyperplane Arrangements)
|
||
- [[hypothesis-tree-refinement]] — Hypothesis Tree Refinement (HTR)
|
||
- [[identity-reference-resolution]] — 身份指代消解 (Identity Reference Resolution)
|
||
- [[image-generation-prompt-design]] — 图像生成 Prompt 设计
|
||
- [[implicit-processes]] — 隐式过程 (Implicit Processes)
|
||
- [[in-context-learning]] — 上下文学习 (In-Context Learning)
|
||
- [[in-context-learning-rate]] — In-Context Learning Rate
|
||
- [[in-database-analytics]] — In-Database Analytics
|
||
- [[induction-heads]] — Induction Heads
|
||
- [[inference-primitives]] — Inference Primitives (推理原语)
|
||
- [[inference-time-scaling]] — Inference-Time Scaling（推理时扩展）
|
||
- [[infinite-dimensional-manifolds]] — 无限维流形 (Infinite-Dimensional Manifolds)
|
||
- [[infinite-width-limit]] — 无限宽度极限 (Infinite-Width Limit)
|
||
- [[infinity-hierarchy]] — 无穷层级体系 (Infinity Hierarchy)
|
||
- [[information-cocoons]] — 信息茧房（Information Cocoons）
|
||
- [[information-flow-control]] — Information Flow Control
|
||
- [[information-geometry]] — 信息几何 (Information Geometry)
|
||
- [[information-leakage-vla]] — Information Leakage in VLA
|
||
- [[information-performance-binding]] — Information-Performance Binding
|
||
- [[input-superposition]] — Input Superposition
|
||
- [[insight-backpropagation]] — Insight Backpropagation
|
||
- [[intensity-free-modeling]] — Intensity-free 建模
|
||
- [[interaction-based-explanation]] — 交互基解释 (Interaction-Based Explanation)
|
||
- [[interaction-generalizability]] — 交互泛化性 (Interaction Generalizability)
|
||
- [[interaction-order]] — 交互阶数 (Interaction Order)
|
||
- [[interaction-types-sft]] — SFT 中的三类交互 (Removed, Preserved, Newly Emerged)
|
||
- [[interleaved-gui-tool-trajectory-scaling]] — Interleaved GUI-Tool Trajectory Scaling Pipeline
|
||
- [[internal-ticks]] — Internal Ticks
|
||
- [[internal-world-model]] — Internal World Model
|
||
- [[intersectional-persona-evaluation]] — Intersectional Persona Evaluation
|
||
- [[intervention-multiplier]] — Intervention Multiplier
|
||
- [[intra-head-eviction]] — Intra-Head Eviction
|
||
- [[intrabench]] — IntraBench — Benchmark for Content-Grounded Literature QA
|
||
- [[intragent]] — IntrAgent — Structural-Aware Literature Reading Agent
|
||
- [[intraview]] — IntraView — Content-Grounded Literature Information Retrieval
|
||
- [[intrinsic-rewards-sharpening]] — 内在奖励锐化机制 (Intrinsic Rewards Sharpening)
|
||
- [[inward-only-gradient-flow]] — Inward-Only Gradient Flow (内向梯度流)
|
||
- [[isolation-necessity-theorem]] — Isolation Necessity Theorem (隔离必要性定理)
|
||
- [[isotonic-regression]] — Isotonic Regression
|
||
- [[itemic-text-alignment]] — Itemic-Text 对齐 (Itemic-Text Alignment)
|
||
- [[itemic-tokens]] — Itemic Token
|
||
- [[iterative-capability-extension]] — 迭代能力扩展 — Iterative Capability Extension
|
||
- [[iterative-code-refinement]] — Iterative Code Refinement (迭代代码精炼)
|
||
- [[iterative-reading]] — Iterative Reading — Progressive Information Extraction from Literature
|
||
- [[ito-calculus]] — Itô 微积分 (Itô Calculus)
|
||
- [[jagged-frontier]] — Jagged Frontier / 锯齿前沿
|
||
- [[jepa]] — JEPA (Joint Embedding Predictive Architecture)
|
||
- [[jepa-for-robotics]] — JEPA for Robotics
|
||
- [[k-pass-training]] — K-Pass Training (K 遍训练)
|
||
- [[kalman-filter]] — Kalman 滤波
|
||
- [[keydiff]] — KeyDiff
|
||
- [[kl-order]] — KL 阶 (KL Order)
|
||
- [[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-injection]] — 知识注入 (Knowledge Injection)
|
||
- [[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]] — KV Cache
|
||
- [[kv-cache-bottleneck]] — KV 缓存内存瓶颈
|
||
- [[kv-cache-eviction]] — KV Cache Eviction
|
||
- [[kvcache-transfer]] — KVCache 传输与优化
|
||
- [[language-gradient]] — Language Gradient (语言梯度)
|
||
- [[language-loss]] — Language Loss (语言损失)
|
||
- [[large-reasoning-models]] — 大推理模型 (Large Reasoning Models)
|
||
- [[latent-action-pretraining]] — Latent-Action Pretraining
|
||
- [[latent-reasoning]] — 潜在推理 (Latent Reasoning)
|
||
- [[latent-score-mdp]] — 潜在得分 MDP (Latent-Score MDP)
|
||
- [[latent-thought-models]] — 隐式思考模型 (Latent Thought Models)
|
||
- [[latent-variable-generative-model]] — Latent-Variable Generative Model（潜在变量生成模型）
|
||
- [[latent-world-model]] — Latent World Model (Robotics)
|
||
- [[layered-memory-architecture]] — 三层记忆架构
|
||
- [[leakage-free-state-prediction]] — Leakage-Free State Prediction
|
||
- [[length-extrapolation]] — 长度外推 (Length Extrapolation)
|
||
- [[leopold-kronecker]] — 利奥波德·克罗内克尔 (Leopold Kronecker)
|
||
- [[leworldmodel]] — LeWorldModel
|
||
- [[lifecycle-aware-harness]] — Lifecycle-Aware Harness（生命周期感知 Harness）
|
||
- [[lifecycle-orchestration]] — Lifecycle & Orchestration（生命周期与编排）
|
||
- [[lifting-identity]] — Lifting Identity (提升恒等式)
|
||
- [[light-routing-agent]] — 轻量路由 Agent — Light Routing Agent
|
||
- [[linear-attention]] — 线性注意力 (Linear Attention)
|
||
- [[linear-attention-methods]] — 线性注意力方法 (Linear Attention Methods)
|
||
- [[linear-quadratic-regulator]] — 线性二次调节器 (Linear Quadratic Regulator)
|
||
- [[linear-representation-hypothesis]] — Linear Representation Hypothesis
|
||
- [[linearized-neural-network]] — 线性化神经网络 (Linearized Neural Network)
|
||
- [[llama-factory]] — LLaMA-Factory
|
||
- [[llm-applications]] — LLM 应用
|
||
- [[llm-based-temporal-point-process]] — LLM 时间点过程 (LLM-based TPP)
|
||
- [[llm-consistent-reasoning]] — LLM Consistent Reasoning
|
||
- [[llm-evaluation-benchmarks]] — LLM 评测基准体系
|
||
- [[llm-mcmc]] — LLM-MCMC
|
||
- [[logfire]] — Logfire
|
||
- [[logical-model-interaction]] — 交互逻辑模型 (Logical Model of Interactions)
|
||
- [[long-context-understanding]] — 长上下文理解 (Long-Context Understanding)
|
||
- [[long-horizon-evaluation]] — Long-Horizon Evaluation / 长视界评估
|
||
- [[long-horizon-parsing]] — Long-Horizon Parsing
|
||
- [[long-horizon-utility]] — Long-Horizon Utility
|
||
- [[long-range-dependency]] — Long-Range Dependency
|
||
- [[long-term-interactive-memory]] — Long-Term Interactive Memory
|
||
- [[longmem-eval]] — LongMemEval Benchmark
|
||
- [[look-ahead-buffer-controller]] — Look-Ahead Buffer Controller
|
||
- [[lora]] — LoRA (Low-Rank Adaptation)
|
||
- [[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)
|
||
- [[lukv]] — LU-KV (Long-horizon Utility KV)
|
||
- [[macro-level-token-economics]] — Macro-Level Token Economics
|
||
- [[mamba-2]] — Mamba-2
|
||
- [[mamba-ssm]] — Mamba (State Space Model)
|
||
- [[manifold-constrained-hyper-connections]] — Manifold-Constrained Hyper-Connections (mHC)
|
||
- [[manifold-of-minimizers]] — Manifold of Minimizers (极小值流形)
|
||
- [[marginal-utility]] — Marginal Utility (KV Cache)
|
||
- [[marked-temporal-point-process]] — 标记时间点过程 (Marked TPP)
|
||
- [[martingale-clt]] — 鞅中心极限定理 (Martingale CLT)
|
||
- [[math-question-reformulation]] — 数学问题多维度改写
|
||
- [[mathchatsync-reasoning]] — MathChatSync Reasoning
|
||
- [[mathematical-pluralism]] — 数学多元主义 (Mathematical Pluralism)
|
||
- [[mathematical-priority-disputes]] — 数学优先权争议
|
||
- [[mathforge]] — MathForge 框架
|
||
- [[maze-navigation]] — 迷宫导航 (Maze Navigation)
|
||
- [[mc-dropout]] — MC Dropout (Monte Carlo Dropout)
|
||
- [[mcp-protocol]] — MCP 协议 — Model Context Protocol
|
||
- [[mcp-tools-dataset]] — MCP-tools 数据集
|
||
- [[me2-principle]] — ME² Principle
|
||
- [[mechanistic-interpretability]] — 机制可解释性 (Mechanistic Interpretability)
|
||
- [[megatron-lm]] — Megatron-LM
|
||
- [[mem2skill]] — Mem2Skill — 记忆到技能转化
|
||
- [[memcube]] — MemCube — 最小记忆单元
|
||
- [[memory-caching-rnn]] — Memory Caching (MC)
|
||
- [[memory-compute-decoupling]] — Memory-Compute Decoupling
|
||
- [[memory-consolidation]] — Memory Consolidation（写后提炼）
|
||
- [[memory-dedup-pipeline]] — 记忆去重管线
|
||
- [[memory-governance]] — 记忆治理 — Memory Governance
|
||
- [[memory-indexing-retrieval-reading]] — Memory Indexing-Retrieval-Reading Framework
|
||
- [[meso-level-token-economics]] — Meso-Level Token Economics
|
||
- [[messy-context-reasoning]] — 混乱上下文推理 (Messy Context Reasoning)
|
||
- [[meta-jctrader]] — Meta-JCTrader
|
||
- [[meta-learning]] — Meta-Learning (元学习)
|
||
- [[meta-tools]] — Meta Tools — 管理工具的工具
|
||
- [[metacognitive-self-modification]] — Metacognitive Self-Modification (元认知自我修改)
|
||
- [[metamathematics]] — 元数学 (Metamathematics)
|
||
- [[micro-level-token-economics]] — Micro-Level Token Economics
|
||
- [[million-token-context]] — Million-Token Context
|
||
- [[mineru]] — minerU — PDF-to-Markdown for Scientific Literature
|
||
- [[minimax-optimality]] — Minimax 最优性 (Minimax Optimality)
|
||
- [[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)
|
||
- [[ml-technical-debt]] — ML 技术债务
|
||
- [[mme-voke]] — MMEVOKE
|
||
- [[model-collapse-step]] — 模型崩溃步 (Model Collapse Step, MCS)
|
||
- [[model-driven-vs-app-driven-memory]] — 模型驱动 vs 应用驱动记忆
|
||
- [[model-free-rl]] — Model-Free 强化学习 (Model-Free RL)
|
||
- [[model-harness-relationship]] — Model-Harness Relationship (模型与Harness关系)
|
||
- [[model-steering]] — Model Steering
|
||
- [[moe-lora]] — MoELoRA
|
||
- [[moe-lora-toolchain-conflict]] — MOE + LoRA 工具链冲突
|
||
- [[moment-matching-filter]] — 矩匹配滤波
|
||
- [[monocular-video-to-4d]] — 单目视频到 4D (Monocular Video to 4D)
|
||
- [[mqr]] — Multi-Aspect Question Reformulation (MQR)
|
||
- [[mrq-algorithm]] — MR.Q 算法 (MR.Q Algorithm)
|
||
- [[multi-agent-orchestration]] — Multi-Agent Orchestration（多 Agent 编排）
|
||
- [[multi-agent-safety]] — Multi-Agent Safety
|
||
- [[multi-agent-spiral]] — 多智能体螺旋（Multi-Agent Spiral）
|
||
- [[multi-dimensional-synthetic-data]] — 多维合成数据 (Multi-Dimensional Synthetic Data)
|
||
- [[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-step-planning]] — 多步规划 (Multi-Step Planning)
|
||
- [[multi-teacher-on-policy-distillation]] — Multi-Teacher On-Policy Distillation (MODPO)
|
||
- [[multi-token-prediction]] — Multi-Token Prediction (MTP)
|
||
- [[multi-trajectory-inference]] — Multi-Trajectory Inference（多轨迹推理）
|
||
- [[multi-turn-reasoning]] — Multi-Turn Reasoning Training (多轮推理训练)
|
||
- [[multi-view-captioning]] — 多视角字幕 (Multi-View Captioning)
|
||
- [[multimodal-large-language-model]] — 多模态大语言模型 (MLLM)
|
||
- [[multimodal-rag]] — 多模态 RAG (Multimodal RAG)
|
||
- [[multitask-rl]] — 多任务强化学习 (Multitask RL)
|
||
- [[muon-optimizer]] — Muon Optimizer
|
||
- [[nachbin-theorem]] — Nachbin 定理
|
||
- [[native-sparse-attention]] — Native Sparse Attention (NSA)
|
||
- [[native-streaming-ar-training]] — Native Streaming AR Training
|
||
- [[natural-gradient-descent]] — 自然梯度下降
|
||
- [[negative-sample-reinforcement]] — Negative Sample Reinforcement (NSR)
|
||
- [[neural-synchronization]] — Neural Synchronization as Representation
|
||
- [[neural-tangent-kernel]] — 神经正切核 (Neural Tangent Kernel)
|
||
- [[neural-temporal-point-process]] — 神经时间点过程 (Neural TPP)
|
||
- [[neurida]] — NeurIDA
|
||
- [[neuroalgebraic-geometry]] — 神经代数几何 (Neuroalgebraic Geometry)
|
||
- [[neuromanifold]] — 神经流形 (Neuromanifold)
|
||
- [[neuron-level-models]] — Neuron-Level Models (NLMs)
|
||
- [[neuron-pairing]] — Neuron Pairing
|
||
- [[neuroscience]] — Neuroscience (神经科学)
|
||
- [[next-state-grounding]] — Next-State Grounding
|
||
- [[ngram-embedding]] — N-gram Embedding (in LLMs)
|
||
- [[non-anticipative-functionals]] — 非预期泛函 (Non-Anticipative Functionals)
|
||
- [[non-stationary-time-series]] — Non-stationary Time Series（非平稳时间序列）
|
||
- [[non-thinking-mode]] — 非思考模式 (Non-Thinking Mode)
|
||
- [[normal-tangent-decomposition]] — Normal-Tangent Decomposition (法向-切向分解)
|
||
- [[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)
|
||
- [[objective-driven-ai]] — 目标驱动AI (Objective-Driven AI)
|
||
- [[objective-interference-collapse]] — Objective Interference Collapse (目标干扰坍缩)
|
||
- [[observability]] — Observability & Operations（可观测性与运维）
|
||
- [[observable-operator-model]] — 可观测算子模型 (Observable Operator Model, OOM)
|
||
- [[off-policy-llm-post-training]] — Off-Policy LLM 后训练
|
||
- [[offline-profiling]] — Offline Profiling (LU-KV)
|
||
- [[omnidocbench]] — OmniDocBench
|
||
- [[on-policy-distillation]] — On-Policy Distillation (OPD)
|
||
- [[on-policy-learning-collapse]] — On-policy Learning Collapse
|
||
- [[one-pass-fine-tuning]] — One-Pass Fine-Tuning (单遍微调)
|
||
- [[onereason-bench]] — OneReason-Bench
|
||
- [[onerec]] — OneRec 生成式推荐模型族
|
||
- [[open-telemetry]] — OpenTelemetry (OTel)
|
||
- [[openclaw]] — OpenClaw
|
||
- [[opinion-polarization]] — 观点极化（Opinion Polarization）
|
||
- [[optimal-gui-tool-path-selection]] — Optimal GUI-Tool Path Selection
|
||
- [[optimality-gap]] — Optimality Gap
|
||
- [[oracle-importance]] — Oracle Importance
|
||
- [[order-bias-removal]] — Order Bias Removal
|
||
- [[osworld-mcp]] — OSWorld-MCP Benchmark
|
||
- [[output-aware-metric]] — Output-Aware Metric (OAM)
|
||
- [[overthinking]] — 过度思考 (Overthinking)
|
||
- [[pac-bayesian-bounds]] — PAC-Bayesian 泛化界 (PAC-Bayesian Bounds)
|
||
- [[pageindex]] — PageIndex
|
||
- [[paley-graph]] — Paley Graph
|
||
- [[parallel-scan]] — Parallel Scan (Parallel Associative Scan)
|
||
- [[parametrization-map]] — 参数化映射 (Parametrization Map)
|
||
- [[pareto-frontier-evaluation]] — Pareto 前沿评测 (Pareto Frontier Evaluation)
|
||
- [[paris-harrington-theorem]] — Paris-Harrington Theorem（巴黎-哈灵顿定理）
|
||
- [[partially-observable-markov-game]] — 部分可观测马尔可夫博弈 (Partially Observable Markov Game, POMG)
|
||
- [[pass-at-k-vs-pass-k]] — Pass@k vs Pass^k（能力上限 vs 可靠性下限）
|
||
- [[passive-vs-active-knowledge]] — 被动知识 vs 主动知识
|
||
- [[patch-based-evaluation]] — Patch-Based Evaluation (基于 Patch 的评测合约)
|
||
- [[path-tracing]] — 路径追踪 (Path Tracing)
|
||
- [[pdf-processing]] — PDF Processing
|
||
- [[peano-arithmetic]] — 皮亚诺算术 (Peano Arithmetic, PA)
|
||
- [[per-index-time-decay]] — Per-Index Time Decay
|
||
- [[perception-cognition-recommendation]] — 感知-认知推荐层次 (R0-R3)
|
||
- [[perception-gap]] — 感知鸿沟 (Perception Gap)
|
||
- [[persona-invariant-reasoning]] — Persona-Invariant Reasoning
|
||
- [[personalization-trap]] — 个性化陷阱 (Personalization Trap)
|
||
- [[pldm]] — PLDM (Pretrained Latent Dynamics Model)
|
||
- [[poisson-process]] — 泊松过程 (Poisson Process)
|
||
- [[policy-constrained-execution]] — Policy-Constrained Execution
|
||
- [[policy-regret]] — 策略后悔 (Policy Regret)
|
||
- [[policy-reincarnation]] — Policy Reincarnation
|
||
- [[polysemanticity]] — 多义性与单义性 (Polysemanticity & Monosemanticity)
|
||
- [[pomdp]] — 部分可观测马尔可夫决策过程 (POMDP)
|
||
- [[position-encoding]] — Position Encoding (位置编码)
|
||
- [[position-id-discrepancy]] — Position ID Discrepancy (位置 ID 偏差)
|
||
- [[positive-sample-reinforcement]] — Positive Sample Reinforcement (PSR)
|
||
- [[post-action-configuration]] — 后动作配置 (Post-Action Configuration)
|
||
- [[post-hoc-reasoning-rl]] — 后置推理 RL (Post-Hoc Reasoning RL)
|
||
- [[post-train-space-rl]] — Post-train Space Reinforcement Learning
|
||
- [[posterior-linearization-filter]] — 后验线性化滤波
|
||
- [[posterior-lipschitz-adversary]] — 后验李普希茨对手 (Posterior-Lipschitz Adversary)
|
||
- [[practitioner-research-gap]] — Practitioner-Research Gap（从业者-研究鸿沟）
|
||
- [[pre-activation-history]] — Pre-Activation History
|
||
- [[pre-hoc-reasoning-rl]] — 前置推理 RL (Pre-Hoc Reasoning RL)
|
||
- [[pre-train-space-reinforcement-learning]] — Pre-train Space Reinforcement Learning (PreRL)
|
||
- [[precision-weighted-fusion]] — 精度加权融合 (Precision-Weighted Fusion)
|
||
- [[prediction-driven-inference]] — 预测驱动推断（Prediction-Driven Inference）
|
||
- [[predictive-representation-learning]] — 预测表征学习 (Predictive Representation Learning)
|
||
- [[preference-log-odds]] — Preference Log-Odds
|
||
- [[preference-utility-analysis]] — Preference–Utility Analysis
|
||
- [[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）
|
||
- [[preserved-interactions-backbone]] — 保留交互作为推理支柱 (Preserved Interactions as Inference Backbone)
|
||
- [[pretraining-statistical-bias]] — 预训练统计偏好（Pretraining Statistical Bias）
|
||
- [[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）
|
||
- [[probability-matching]] — 概率匹配（Probability Matching）
|
||
- [[procedural-gap]] — 过程性鸿沟 — Procedural Gap
|
||
- [[procedural-skill]] — 过程技能 (Procedural Skill)
|
||
- [[procedural-skill-layer]] — Procedural Skill Layer（程序技能层）
|
||
- [[procedural-task-execution]] — 程序性任务执行 (Procedural Task Execution)
|
||
- [[product-stability]] — Product-Stability (乘积稳定性)
|
||
- [[program-synthesis]] — Program Synthesis (程序合成)
|
||
- [[prompt-caching]] — Prompt Caching
|
||
- [[prompt-engineering-vs-fine-tuning]] — 提示词工程 vs 微调
|
||
- [[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）
|
||
- [[prope]] — PRoPE (Projective Rotary Position Encoding)
|
||
- [[prospective-memory-index]] — Prospective Memory Index (前瞻记忆索引)
|
||
- [[pseudo-huber-loss]] — Pseudo-Huber 损失
|
||
- [[pydantic]] — Pydantic
|
||
- [[pydantic-ai]] — Pydantic AI
|
||
- [[pydantic-core]] — pydantic-core
|
||
- [[pyramidkv]] — PyramidKV
|
||
- [[qlora]] — QLoRA (量化低秩适配)
|
||
- [[quadrotor-trajectory-following]] — 四旋翼轨迹跟踪 (Quadrotor Trajectory Following)
|
||
- [[query-intent-analyzer]] — Query Intent Analyzer
|
||
- [[question-quality-vs-quantity]] — Question Quality vs. Quantity（问题质量 vs 数量）
|
||
- [[queueing-network-control]] — 排队网络控制 (Queueing Network Control)
|
||
- [[rademacher-complexity]] — Rademacher Complexity
|
||
- [[rag]] — RAG (检索增强生成)
|
||
- [[rag-closed-loop]] — RAG 闭环迭代（RAG Closed-Loop Iteration）
|
||
- [[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)
|
||
- [[real-log-canonical-threshold]] — 实对数典范阈值 (Real Log Canonical Threshold, RLCT)
|
||
- [[reasoning-quality-optimization]] — Reasoning Quality Optimization
|
||
- [[recommendation-cot]] — 推荐思维链 (Recommendation CoT)
|
||
- [[recommendation-reasoning]] — 推荐推理 (Recommendation Reasoning)
|
||
- [[rectified-flows]] — Rectified Flows
|
||
- [[recurrence-taxonomy]] — 循环分类法 (Recurrence Taxonomy)
|
||
- [[recurrent-transformer-architectures]] — 循环Transformer架构 (Recurrent Transformer Architectures)
|
||
- [[recursive-reasoning-models]] — Recursive Reasoning Models（递归推理模型）
|
||
- [[recursive-self-improvement]] — Recursive Self-Improvement (递归自我改进)
|
||
- [[reer-reverse-knowledge-extraction]] — REER 逆向知识提炼
|
||
- [[reference-gap]] — 引用鸿沟 (Reference Gap)
|
||
- [[reference-sliding-window-attention]] — Reference Sliding Window Attention (R-SWA)
|
||
- [[regular-language-recognition]] — Regular Language Recognition
|
||
- [[reinforced-online-policy-distillation]] — Reinforced Online-Policy Distillation (ROPD)
|
||
- [[reinforcement-learning]] — 强化学习 (Reinforcement Learning)
|
||
- [[reinforcement-learning-trading]] — Reinforcement Learning Trading（强化学习交易）
|
||
- [[rejected-edit-buffer]] — Rejected-Edit Buffer (拒绝编辑缓冲)
|
||
- [[rejection-sampling-fine-tuning]] — Rejection Sampling Fine-tuning (RSFT)
|
||
- [[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）
|
||
- [[rep-mt-sac]] — RepMT-SAC
|
||
- [[reparameterization-exploration]] — 重参数化探索 (Reparameterization Exploration)
|
||
- [[replay-buffer-rl-llm]] — Replay Buffer 在 LLM RL 中的应用
|
||
- [[representation-alignment]] — Representation Alignment
|
||
- [[representation-collapse]] — 表征坍缩 (Representation Collapse)
|
||
- [[representation-learning-rl]] — RL中的表征学习 (Representation Learning in RL)
|
||
- [[representation-space]] — Representation Space
|
||
- [[representation-validity]] — Representation Validity
|
||
- [[representational-alignment]] — 表征对齐 (Representational Alignment)
|
||
- [[research-hypothesis-tree]] — Research Hypothesis Tree
|
||
- [[resource-access-control]] — Resource Access Control
|
||
- [[reverse-proxy-authentication]] — 反向代理认证 (Reverse Proxy Authentication)
|
||
- [[reward-hacking]] — Reward Hacking（奖励黑客）
|
||
- [[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
|
||
- [[richard-dedekind]] — 里夏德·狄德金 (Richard Dedekind)
|
||
- [[risograph-print-style]] — Riso 印刷风格 (Risograph Print Style)
|
||
- [[rlhf]] — RLHF (Reinforcement Learning from Human Feedback)
|
||
- [[rlhf-alignment-amplification]] — RLHF 对齐放大（RLHF Alignment Amplification）
|
||
- [[rlvr-unified-framework]] — RLVR 统一理论框架
|
||
- [[role-setting-entrenchment]] — 角色设定固化（Role-Setting Entrenchment）
|
||
- [[rolling-kv-cache]] — 滚动 KV 缓存 (Rolling KV Cache)
|
||
- [[rollout-drift]] — Rollout Drift (推演漂移)
|
||
- [[rotary-position-embedding]] — 旋转位置编码 (RoPE)
|
||
- [[rough-path-theory]] — 粗糙路径理论 (Rough Path Theory)
|
||
- [[round-trip-reconstruction-score]] — Round-Trip Reconstruction Score (RS@k)
|
||
- [[rule-system-application]] — 规则系统应用 (Rule System Application)
|
||
- [[runtime-governance]] — 运行时治理 — Skill Governance
|
||
- [[runtime-harness-adaptation]] — Runtime Harness Adaptation（运行时骨架适配）
|
||
- [[runtime-interface-adaptation]] — Runtime Interface Adaptation（运行时接口适配）
|
||
- [[russells-paradox]] — 罗素悖论 (Russell's Paradox)
|
||
- [[russian-constructivism]] — 俄国构成主义 (Russian Constructivism)
|
||
- [[rwkv]] — RWKV (Receptance Weighted Key Value)
|
||
- [[s-token]] — S-Token (Superposed Token)
|
||
- [[safety-adherence-rate]] — Safety Adherence Rate
|
||
- [[scaling-permutation-symmetry]] — 缩放与置换对称性 (Scaling & Permutation Symmetries)
|
||
- [[scientific-literature-qa]] — Scientific Literature QA — Question Answering over Research Papers
|
||
- [[sde-sampler-language]] — SDE Sampler for Language Diffusion
|
||
- [[se3-relative-camera-encoding]] — SE(3) 相对相机编码
|
||
- [[search-and-load]] — Search and Load — 精选工具加载
|
||
- [[searcher-trainer-decoupling]] — Searcher-Trainer 解耦架构
|
||
- [[section-ranking]] — Section Ranking — Structure-Aware Literature Section Prioritization
|
||
- [[secure-containers]] — 安全容器
|
||
- [[seer-attention]] — SeerAttention
|
||
- [[selective-copy]] — Selective Copying
|
||
- [[selective-hitl]] — 选择性 HITL — Selective Human-in-the-Loop
|
||
- [[selective-state-space]] — Selective State Space (S6)
|
||
- [[selective-state-space-models]] — 选择性状态空间模型 (Selective State Space Models)
|
||
- [[self-conditioning]] — Self-Conditioning
|
||
- [[self-evolutionary-mutation]] — 自进化变异 — Self-Evolutionary Mutation
|
||
- [[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-resampling]] — Self-Resampling
|
||
- [[self-verification-rewards]] — 自我验证奖励 (Self-Verification Rewards)
|
||
- [[semantic-equivalence]] — Semantic Equivalence / 语义等价
|
||
- [[semi-algebraic-set]] — 半代数集 (Semi-algebraic Set)
|
||
- [[semiseparable-matrices]] — 半可分矩阵 (Semiseparable Matrices)
|
||
- [[sequence-packing]] — Sequence Packing (序列打包)
|
||
- [[sequential-dependency]] — 顺序依赖 (Sequential Dependency)
|
||
- [[set-theory-history]] — 集合论史
|
||
- [[sft-denoising-stage]] — SFT 去噪阶段 (SFT Denoising Stage)
|
||
- [[sft-early-stopping]] — SFT 早停策略 (SFT Early Stopping)
|
||
- [[sglang]] — SGLang
|
||
- [[shadow-calling]] — Shadow Calling (影子调用)
|
||
- [[shapley-values]] — Shapley 值 (Shapley Values)
|
||
- [[shared-parameter-influence]] — Shared Parameter Influence
|
||
- [[shared-weight-discretization]] — Shared-Weight Discretization
|
||
- [[sharpness]] — Sharpness (锐度)
|
||
- [[signature]] — 签名 (Signature of Paths)
|
||
- [[sigreg]] — SIGReg (Sketch Isotropic Gaussian Regularization)
|
||
- [[singular-learning-theory]] — 奇异学习理论 (Singular Learning Theory)
|
||
- [[singularity]] — Singularity (奇点)
|
||
- [[sink-token]] — 汇 Token (Sink Token)
|
||
- [[situational-test-emotional-understanding]] — Situational Test of Emotional Understanding (STEU)
|
||
- [[skill-acquisition]] — Skill 获取 — 四种路径
|
||
- [[skill-as-external-state]] — Skill as External State (Skill 作为外部状态)
|
||
- [[skill-composition]] — Skill 组合 — 多技能编排
|
||
- [[skill-data-flywheel]] — Skill Data Flywheel (Skill 数据飞轮)
|
||
- [[skill-ecosystem]] — Skill Ecosystem (Skill 生态系统)
|
||
- [[skill-evolution]] — Skill 演化 — 修订→验证→治理
|
||
- [[skill-lifecycle]] — Skill 生命周期
|
||
- [[skill-probe]] — 技能探针 (Skill Probe)
|
||
- [[skill-representation]] — Skill 表示 — 文本/代码/混合
|
||
- [[skill-retrieval]] — Skill 检索 — 稠密/稀疏/生成/结构
|
||
- [[skill-selection]] — Skill 选择 — 上下文/组合/效用/反馈
|
||
- [[skillopt]] — SkillOpt
|
||
- [[slow-meta-update]] — Slow/Meta Update (慢/元更新)
|
||
- [[snapkv]] — SnapKV
|
||
- [[social-capital-framework]] — Social Capital Framework (AI Bias)
|
||
- [[social-video]] — Social Video
|
||
- [[social-world-model]] — Social World Model
|
||
- [[socialvideo-bench]] — SocialVideo Bench
|
||
- [[soft-actor-critic]] — Soft Actor-Critic (SAC)
|
||
- [[soft-supersession]] — Soft-Supersession
|
||
- [[soft-token]] — Soft Token
|
||
- [[softmax-off-by-one]] — SoftMax-off-by-One
|
||
- [[sovereign-ai]] — 主权AI (Sovereign AI)
|
||
- [[space-supervision]] — Space Supervision
|
||
- [[sparse-attention-patterns]] — 稀疏注意力模式 (Sparse Attention Patterns)
|
||
- [[sparse-autoencoder]] — 稀疏自编码器 (Sparse Autoencoder)
|
||
- [[sparsity-allocation]] — Sparsity Allocation (U-shaped Law)
|
||
- [[specialist-training-pipeline]] — Specialist Training Pipeline
|
||
- [[specialize-then-unify-rl]] — Specialize-then-Unify RL
|
||
- [[specialized-rl]] — 专项强化学习 (Specialized RL)
|
||
- [[specialized-sft]] — 专项监督微调 (Specialized SFT)
|
||
- [[spectral-mdp-decomposition]] — 谱 MDP 分解 (Spectral MDP Decomposition)
|
||
- [[spiking-neural-networks]] — Spiking Neural Networks (SNN)
|
||
- [[spiral-of-silence]] — 沉默的螺旋（Spiral of Silence）
|
||
- [[split-steering]] — SPLIT Steering
|
||
- [[spurious-predictability]] — Spurious Predictability
|
||
- [[ssd-algorithm]] — SSD 算法 (Structured State Space Duality Algorithm)
|
||
- [[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）
|
||
- [[state-dependent-feasible-action-sets]] — 状态依赖可行动作集 (State-Dependent Feasible Action Sets)
|
||
- [[state-space-models]] — 状态空间模型 (State-Space Models)
|
||
- [[state-tracking]] — 状态追踪 (State Tracking)
|
||
- [[statistical-contract-theory]] — 统计合同理论（Statistical Contract Theory）
|
||
- [[statistical-manifold]] — Statistical Manifold (统计流形)
|
||
- [[staug]] — STAug (EMD-based Augmentation)
|
||
- [[steering-dynamics]] — Steering Dynamics
|
||
- [[steering-vector]] — Steering Vector
|
||
- [[stein-lemma]] — Stein 引理
|
||
- [[stem-sparse-attention]] — Stem Sparse Attention
|
||
- [[step-recurrence]] — 步级循环 (Step Recurrence)
|
||
- [[stochastic-differential-equation]] — 随机微分方程 (Stochastic Differential Equation)
|
||
- [[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)
|
||
- [[strategy-gene]] — 策略基因 (Strategy Gene)
|
||
- [[streaming-generation]] — Streaming Generation
|
||
- [[streaming-inference]] — Streaming Inference
|
||
- [[structured-knowledge]] — 结构化知识 (Structured Knowledge)
|
||
- [[structured-masked-attention]] — 结构化掩码注意力 (Structured Masked Attention)
|
||
- [[structured-output]] — 结构化输出 (Structured Output)
|
||
- [[structured-state-space-duality]] — 结构化状态空间对偶 (Structured State Space Duality)
|
||
- [[structured-state-space-models]] — Structured State Space Models (S4)
|
||
- [[stub-pattern]] — Stub Pattern（轻量化桩模式）
|
||
- [[subquadratic-transformer-alternatives]] — 次二次 Transformer 替代方案
|
||
- [[sufficiency-check]] — Sufficiency Check — Explicit Hallucination Gate in Literature QA
|
||
- [[sufficient-context-paradox]] — 充分上下文悖论 (Sufficient Context Paradox)
|
||
- [[superposition]] — 叠加 (Superposition)
|
||
- [[supervised-fine-tuning]] — 监督微调 (Supervised Fine-Tuning, SFT)
|
||
- [[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]] — 合成数据 (Synthetic Data)
|
||
- [[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）
|
||
- [[system-stability]] — System Stability
|
||
- [[szemerédi-regularity-lemma]] — Szemerédi Regularity Lemma
|
||
- [[tabular-foundation-models]] — Tabular Foundation Models
|
||
- [[tapestry-federated]] — Tapestry 联邦训练
|
||
- [[task-conditioned-policy]] — 任务条件策略 (Task-Conditioned Policy)
|
||
- [[task-distribution]] — 任务分布 (Task Distribution)
|
||
- [[task-invariant-representation]] — 任务不变表征 (Task-Invariant Representation)
|
||
- [[taylor-expansion-q-function]] — Q 函数 Taylor 展开 (Taylor Expansion of Q-Function)
|
||
- [[tba]] — Trajectory Balance with Asynchrony (TBA)
|
||
- [[teacher-forced-history]] — 教师强制历史 (Teacher-Forced History)
|
||
- [[temperature-sampling]] — 温度采样（Temperature Sampling）
|
||
- [[temporal-decay-neural]] — Temporal Decay (Neural)
|
||
- [[temporal-patch-shuffle]] — Temporal Patch Shuffle (TPS)
|
||
- [[temporal-point-process]] — 时间点过程 (Temporal Point Process)
|
||
- [[temporal-rollout]] — 时间滚动展开 (Temporal Rollout)
|
||
- [[tensor-contraction-duality]] — 张量收缩对偶 (Tensor Contraction Duality)
|
||
- [[terminal-bench]] — Terminal-Bench
|
||
- [[test-time-control]] — 测试时控制 (Test-Time Control)
|
||
- [[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)
|
||
- [[thinker-performer-pipeline]] — Thinker-Performer Pipeline
|
||
- [[thinking-based-non-thinking]] — TNT: 基于思考的非思考 (Thinking-Based Non-Thinking)
|
||
- [[thinking-mode]] — 思考模式 (Thinking Mode)
|
||
- [[thinking-reward-model]] — Thinking Reward Model (TRM)
|
||
- [[thinking-supervision-transfer]] — Thinking Supervision Transfer
|
||
- [[thompson-sampling-code-search]] — Thompson Sampling Code Search
|
||
- [[three-engineering-phases]] — Three Engineering Phases（三阶段工程演进）
|
||
- [[three-stage-curriculum-training]] — 三阶段课程训练 (Three-Stage Curriculum Training)
|
||
- [[throughput-hypothesis]] — Throughput Hypothesis (吞吐量假说)
|
||
- [[time-aware-query-expansion]] — Time-Aware Query Expansion
|
||
- [[time-series-forecasting-augmentation]] — Time Series Forecasting Augmentation
|
||
- [[time-variant-dynamics]] — Time-variant Dynamics（时变动力学）
|
||
- [[token-as-economic-primitive]] — Token as Economic Primitive
|
||
- [[token-duplication]] — Token Duplication (Token 复制)
|
||
- [[token-economics]] — Token Economics
|
||
- [[token-efficiency]] — Token 效率 (Token Efficiency)
|
||
- [[token-level-policy-gradient]] — Token 级策略梯度 (Token-Level Policy Gradient)
|
||
- [[token-market-dynamics]] — Token Market Dynamics
|
||
- [[token-position-decay]] — Token Position-Decay (TPD)
|
||
- [[token-security-economics]] — Token Security Economics
|
||
- [[token-shift]] — Token Shift
|
||
- [[token-superposition-training]] — Token Superposition Training (TST)
|
||
- [[token-wise-routing]] — 逐Token路由 (Token-Wise Routing)
|
||
- [[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]] — 工具注册表 — Tool Registry
|
||
- [[tpp-applications]] — TPP 应用场景
|
||
- [[tpp-training-methods]] — TPP 训练方法
|
||
- [[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-auditing]] — Trajectory Auditing
|
||
- [[trajectory-balance-objective]] — Trajectory Balance (TB) 目标
|
||
- [[trajectory-regulation-layer]] — Trajectory Regulation Layer（轨迹调控层）
|
||
- [[trajectory-supervision]] — Trajectory Supervision
|
||
- [[trajectory-synthesis]] — 轨迹合成 — Trajectory Synthesis
|
||
- [[transfer-learning]] — Transfer Learning (迁移学习)
|
||
- [[trm-preference-dataset]] — TRM-Preference Dataset
|
||
- [[two-phase-pretraining]] — Two-Phase Pre-Training
|
||
- [[two-time-scale-process]] — 双时间尺度过程 (Two Time-Scale Process)
|
||
- [[type-safety-in-agents]] — Agent 类型安全 (Type Safety in Agents)
|
||
- [[typeadapter]] — TypeAdapter
|
||
- [[ultradata]] — UltraData
|
||
- [[uncancelled-interaction-effects]] — 未抵消交互效应 (Uncancelled Interaction Effects)
|
||
- [[uncertainty-disparity-ratio]] — 不确定性差异比 (Uncertainty Disparity Ratio, UDR)
|
||
- [[uncertainty-equity-gap]] — 不确定性公平性差距 (Uncertainty Equity Gap, UEG)
|
||
- [[uncertainty-quantification]] — 不确定性量化 (Uncertainty Quantification)
|
||
- [[uncertainty-taxonomy]] — Jordan 不确定性分类法（Uncertainty Taxonomy）
|
||
- [[unconditional-generation-latent]] — Unconditional Generation via Latent Reasoning
|
||
- [[unified-latent-probe]] — Unified Latent Probe (ULP)
|
||
- [[unified-rft]] — 统一拒绝采样微调 (Unified RFT)
|
||
- [[universal-approximation-theorem]] — 通用逼近定理 (Universal Approximation Theorem)
|
||
- [[unlimited-ocr]] — Unlimited OCR 模型
|
||
- [[unscented-kalman-filter]] — 无迹 Kalman 滤波
|
||
- [[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
|
||
- [[upstream-downstream-learning]] — 上游-下游学习 (Upstream-Downstream Learning)
|
||
- [[user-memory-bias]] — User Memory Bias
|
||
- [[userspace-kernel]] — 用户空间内核
|
||
- [[validity-decay]] — Validity Decay
|
||
- [[van-der-waerden-theorem]] — van der Waerden Theorem
|
||
- [[variational-autoencoder]] — 变分自编码器 (Variational Autoencoder, VAE)
|
||
- [[variational-linearized-laplace-approximation]] — 变分线性化 Laplace 近似 (VaLLA)
|
||
- [[vector-valued-gating]] — Vector-Valued Gating
|
||
- [[verbatim-pre-recall]] — Verbatim Pre-Recall
|
||
- [[verification-evaluation]] — Verification & Evaluation（验证与评估）
|
||
- [[vertical-llm-knowledge-engineering]] — 垂域 LLM 知识工程 (Vertical LLM Knowledge Engineering)
|
||
- [[vicreg]] — VICReg (Variance-Invariance-Covariance Regularization)
|
||
- [[visibility-constraint]] — Visibility Constraint (可见性约束)
|
||
- [[visual-primitives]] — 视觉原语 (Visual Primitives)
|
||
- [[vla-jepa]] — VLA-JEPA (模型)
|
||
- [[vla-vision-language-action]] — VLA (Vision-Language-Action)
|
||
- [[watanabe-triple]] — Watanabe 三元组 (Watanabe's Triple)
|
||
- [[wavemask-wavemix]] — WaveMask / WaveMix
|
||
- [[weak-revealing-condition]] — 弱揭示条件 (Weak Revealing Condition)
|
||
- [[weighted-spaces]] — 加权空间 (Weighted Spaces)
|
||
- [[width-based-scaling]] — Width-Based Scaling（宽度扩展）
|
||
- [[wiener-process]] — 维纳过程 (Wiener Process)
|
||
- [[wikilinks]] — Wikilinks
|
||
- [[window-attention]] — 窗口注意力 (Window Attention)
|
||
- [[wkv-time-mixing]] — WKV Time Mixing
|
||
- [[world-model-lecun]] — LeCun 世界模型理论
|
||
- [[world-models-rl]] — World Models in RL
|
||
- [[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)
|
||
- [[zero-data-cold-start]] — 零数据冷启动 (Zero-Data Cold Start)
|
||
|
||
## Papers
|
||
|
||
- [[advances-temporal-point-processes-2026]] — Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches
|
||
- [[agarwal-bayesian-attention-geometry]] — The Bayesian Geometry of Transformer Attention
|
||
- [[agent-harness-engineering-survey]] — Agent Harness Engineering: A Survey
|
||
- [[arbor-htr-2026]] — Arbor: Hypothesis-Tree Refinement (Jin et al., RUC/MSR, 2026)
|
||
- [[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
|
||
- [[bellman-taylor-score-decoding]] — Bellman–Taylor Score Decoding for MDPs with State-Dependent Feasible Action Sets
|
||
- [[chen-token-economics-llm-agents]] — Token Economics for LLM Agents
|
||
- [[claw-swe-bench]] — Claw-SWE-Bench: OpenClaw 风格 Agent Harness 的代码任务基准评测
|
||
- [[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
|
||
- [[dao-transformers-are-ssms-2024]] — Transformers are SSMs: Generalized Models and Efficient Algorithms Through Struc
|
||
- [[darlow-ctm-2025]] — Continuous Thought Machines (CTM)
|
||
- [[dead-directions-geometric-singular-learning]] — Dead Directions: 几何奇异学习理论
|
||
- [[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
|
||
- [[engram-conditional-memory-2026]] — Engram: Conditional Memory via Scalable Lookup (Cheng et al., PKU/DeepSeek-AI, 2
|
||
- [[fei-mcp-zero-2025]] — MCP-Zero：主动工具发现
|
||
- [[flex4dhuman]] — Flex4DHuman: 灵活多视角视频扩散用于 4D 人体重建
|
||
- [[gan-bifurcation-eos]] — A Bifurcation Theory Framework for Gradient Descent on the Edge of Stability
|
||
- [[gan-thinking-based-non-thinking-2026]] — Thinking-Based Non-Thinking: Solving the Reward Hacking Problem in Training Hybr
|
||
- [[gaurav-dynamic-react-2025]] — Dynamic ReAct：大规模 MCP 工具选择
|
||
- [[geometric-sae-concepts]] — A Geometric View for Understanding Concept Learning and Neuron Interpretation in
|
||
- [[godel-incompleteness-tutorial]] — 哥德尔不完备定理教程
|
||
- [[goru-one-pass-to-reason-2025]] — One-Pass to Reason: 多轮推理的高效单遍微调
|
||
- [[gram-generative-recursive-reasoning-paper]] — Generative Recursive Reasoning (GRAM)
|
||
- [[gu-mamba]] — Mamba: Linear-Time Sequence Modeling with Selective State Spaces
|
||
- [[hazare-dcgwm-2026]] — DCGWM: 双通道接地世界建模 — 结构防止目标干扰坍缩
|
||
- [[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
|
||
- [[jordan-collectivist-ai-2025]] — AI 的集体主义经济学视角（Jordan, 2025）
|
||
- [[kore-knowledge-injection]] — KORE: Knowledge-Oriented Controls for Knowledge Injection
|
||
- [[laban-llms-corrupt-documents-delegate]] — LLMs Corrupt Your Documents When You Delegate
|
||
- [[large-language-gibbs]] — Structured Inference with Large Language Gibbs
|
||
- [[latent-cot-supervision]] — What Makes Effective Supervision in Latent Chain-of-Thought
|
||
- [[li-amd-human-perception]] — "Are You Sure?": Human Perception Vulnerability in LLM Agents
|
||
- [[liu-auditing-agent-harness-safety]] — Auditing Agent Harness Safety
|
||
- [[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
|
||
- [[longmem-eval-2025]] — LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory (Wu et
|
||
- [[lou-autoharness-2026]] — AutoHarness: LLM Agent 的自动代码 Harness 合成
|
||
- [[ma-intragent-2026]] — IntrAgent: Content-Grounded Literature Information Retrieval
|
||
- [[maes-leworldmodel-2026]] — LeWorldModel: Stable End-to-End JEPA from Pixels
|
||
- [[maineCoon]] — MaineCoon: Real-Time Audio-Visual Social World Model
|
||
- [[me2-trm-reasoning-2026]] — ME² + TRM: Complex Reasoning Optimization (Zhang et al., ICML 2026)
|
||
- [[minimax-policy-regret-pomg]] — Minimax-Optimal Policy Regret in Partially Observable Markov Games
|
||
- [[mozer-topological-trouble-transformers-2026]] — The Topological Trouble With Transformers
|
||
- [[nano-filter]] — NANO Filter: 非线性贝叶斯滤波的自然梯度高斯近似
|
||
- [[nikolopoulos-spurious-predictability]] — Spurious Predictability in Financial Machine Learning
|
||
- [[niu-stem-causal-sparse-attention]] — Stem: Rethinking Causal Information Flow in Sparse Attention
|
||
- [[odrzywolek-eml-single-operator]] — All elementary functions from a single binary operator
|
||
- [[onereason]] — OneReason: 生成式推荐中的推理能力解锁
|
||
- [[ortega-phd-thesis]] — Uncertainty Estimation and Generalization Bounds for Modern Deep Learning
|
||
- [[peng-rwkv7]] — RWKV-7 Goose: Expressive Dynamic State Evolution
|
||
- [[peng-tst-2026]] — Token Superposition Training: 高效 LLM 预训练的 Token 叠加方法
|
||
- [[personalization-trap-2025]] — The Personalization Trap (Fang et al., Amazon, 2025)
|
||
- [[pre-train-space-reinforcement-learning]] — Pre-train Space Reinforcement Learning (PreRL/DSRL)
|
||
- [[predictive-representations-scalable-mtrl]] — 预测表征驱动可扩展多任务深度强化学习
|
||
- [[principled-uncertainty-clinical-ai]] — Principled Uncertainty in Clinical AI: Bayesian Modelling and Equity Auditing
|
||
- [[procedural-skills-to-strategy-genes]] — From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Ev
|
||
- [[qin-prfaas-cross-datacenter]] — Prefill-as-a-Service: KVCache Goes Cross-Datacenter
|
||
- [[ramsey-numbers-survey]] — 拉姆齐数的数学综述
|
||
- [[relu-neuromanifolds-semi-algebraicity]] — ReLU 神经流形的纤维与半代数性
|
||
- [[repmt-sac]] — Learning to Adapt: Representation-Based RL for Multi-Task Skill Transfer
|
||
- [[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
|
||
- [[tang-lukv]] — LU-KV: Predicting Future Utility for KV Cache Eviction
|
||
- [[tao-klowden-ai-mathematical-methods]] — Mathematical methods and human thought in the age of AI
|
||
- [[tarpo]] — TARPO: Token-Wise Latent-Explicit Reasoning via Action-Routing Policy Optimizati
|
||
- [[thinking-with-visual-primitives]] — Thinking with Visual Primitives — 以视觉原语思考
|
||
- [[ticks-to-flows]] — From Ticks to Flows: Dynamics of Neural RL in Continuous Environments
|
||
- [[toolcua-optimal-gui-tool-orchestration]] — ToolCUA: Optimal GUI-Tool Path Orchestration for Computer Use Agents
|
||
- [[unlimited-ocr-works-2026]] — Unlimited OCR Works (Yin et al., Baidu, 2026)
|
||
- [[vla-jepa-2026]] — VLA-JEPA (Sun et al., 2026)
|
||
- [[vu-fisher-width-2026]] — Fisher Width: 统计流形上的几何复杂度度量
|
||
- [[wan-streamer]] — Wan-Streamer v0.1: End-to-end Real-time Interactive Foundation Models
|
||
- [[weighted-uat-manifolds]] — Weighted Universal Approximation of Differentiable Maps on Infinite-Dimensional
|
||
- [[when-large-multimodal-models-confront-evolving-knowledge]] — When Large Multimodal Models Confront Evolving Knowledge
|
||
- [[xing-trails-2024]] — Trails: Database Native Model Selection (VLDB 2024)
|
||
- [[xu-life-harness]] — Adapting the Interface, Not the Model: Runtime Harness Adaptation for Determinis
|
||
- [[xu-why-steering-works]] — Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics
|
||
- [[yang-skillopt-2026]] — SkillOpt: Agent Skill 的文本空间优化器
|
||
- [[yao-ace-router-2026]] — ACE-Router：历史感知路由
|
||
- [[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
|
||
- [[zhang-reconciling-sft-interaction-2026]] — Reconciling Contradictory Views on the Effectiveness of SFT in LLMs
|
||
- [[zhao-neurdb-2025]] — NeurDB: On the Design and Implementation of an AI-powered Autonomous Database (C
|
||
- [[zhou-agent-skills-survey-2026]] — A Comprehensive Survey on Agent Skills — 综述
|
||
- [[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
|
||
|
||
- [[atlas-agent-memory-architecture-2026]] — Atlas Agent 记忆系统架构（2026）
|
||
- [[caddy-reverse-proxy-auth]] — Caddy 反向代理认证方案
|
||
- [[cantor-stole-infinity]] — 窃取无穷的数学家 — 康托尔与狄德金的隐秘合作
|
||
- [[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
|
||
- [[financial-llm-practice-2026]] — 金融行业大模型落地实践（林金曙，2026）
|
||
- [[gpt-image2-prompt-collection]] — GPT-Image-2 绘图 Prompt 方法论与风格合集
|
||
- [[lecun-llm-boundary-future]] — LeCun 论 LLM 的边界与未来架构
|
||
- [[llm-spiral-of-silence-2026]] — LLM 沉默螺旋：算法催生的数字从众
|
||
- [[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
|
||
- [[memtensor-memos-agent-memory-2026]] — MemOS：Agent 记忆基础设施
|
||
- [[michael-jordan-mlst-collectivist-ai-2026]] — Michael I. Jordan：AI 的集体主义经济学与虚假的 AGI 二元论
|
||
- [[mini-agent-harness]] — 从零搭建 Mini Agent Harness
|
||
- [[nobrega-ai-production-tradeoffs-2026]] — AI 工程师的 6 种生产权衡
|
||
- [[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
|
||
- [[pydantic-three-piece-suite]] — Pydantic 三件套：从校验库到 AI 基础设施
|
||
- [[qifu-llm-finance-practice]] — 金融行业大模型落地实践：从知识工程到后训练部署
|
||
- [[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
|
||
|
||
- [[ace-router-review-20260619]] — ACE-Router Review
|
||
- [[advances-temporal-point-processes-review-20260616]] — Review: Advances in Temporal Point Processes
|
||
- [[agent-harness-engineering-review-20260523]] — Review: Agent Harness Engineering Survey
|
||
- [[agent-network-taxonomy-review-20260501]] — agent-network-taxonomy-review-20260501
|
||
- [[agent-skills-survey-review-20260619]] — Agent Skills Survey Review
|
||
- [[arbor-htr-20260624]] — Review: Arbor — Autonomous Research via Hypothesis-Tree Refinement
|
||
- [[auditing-agent-harness-safety-review-20260605]] — Auditing Agent Harness Safety — Review
|
||
- [[btsd-review-20260617]] — Bellman-Taylor Score Decoding 论文集成 Review
|
||
- [[cantor-stole-infinity-2026-06-07]] — 窃取无穷的数学家 — 康托尔与狄德金的历史真相
|
||
- [[cl-bench-life-review-20260501]] — CL-Bench Life 论文集成 Review
|
||
- [[cl-bench-review-20260501]] — cl-bench-review-20260501
|
||
- [[claw-swe-bench-review-20260615]] — Claw-SWE-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
|
||
- [[dao-transformers-are-ssms-review-20260618]] — Review: Transformers are SSMs (Mamba-2)
|
||
- [[dcgwm-2026-06-23]] — Review: DCGWM — 结构防止目标干扰坍缩的双通道接地世界建模
|
||
- [[dead-directions-20260610]] — Review: Dead Directions — Geometric Singular Learning
|
||
- [[delegate52-review-20260514]] — DELEGATE-52 Review
|
||
- [[distributed-agent-cache-sync-review]] — Review: 分布式Agent缓存同步
|
||
- [[dynamic-react-review-20260619]] — Dynamic ReAct Review
|
||
- [[elf-embedded-language-flows-review-20260513]] — Review: ELF — Embedded Language Flows
|
||
- [[engram-conditional-memory-20260625]] — Engram Review — 条件记忆作为 Transformer 的新稀疏轴
|
||
- [[fisher-width-2026-06-23]] — Review: Fisher Width — 统计流形上的几何复杂度
|
||
- [[flex4dhuman-review-20260613]] — Review: Flex4DHuman — 无几何先验的多视角视频扩散
|
||
- [[gan-bifurcation-eos-20260623]] — Review: Gan Bifurcation EoS
|
||
- [[gan-tnt-review-20260618]] — Review: Thinking-Based Non-Thinking (TNT)
|
||
- [[geometric-sae-review-20260617]] — Geometric SAE 论文集成 Review
|
||
- [[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
|
||
- [[jordan-collectivist-ai-review-20260621]] — Review: A Collectivist, Economic Perspective on AI
|
||
- [[koopa-review-20260511]] — Review: Koopa — Koopman 预测器驱动的非平稳时序学习
|
||
- [[kore-review-20260521]] — KORE Review
|
||
- [[large-language-gibbs-2026-06-25]] — Large Language Gibbs Review
|
||
- [[latent-cot-supervision-2026-06-25]] — Latent CoT Supervision Review
|
||
- [[lecun-llm-20260608]] — Review: LeCun 论 LLM 的边界与未来架构
|
||
- [[leworldmodel-20260608]] — Review: LeWorldModel (arXiv:2603.19312)
|
||
- [[life-harness-review-20260611]] — Life-Harness — Runtime Harness Adaptation 论文 Review
|
||
- [[llm-attention-survey-review-20260429]] — Review: 大语言模型注意力机制全面分析
|
||
- [[longmem-eval-20250625]] — LongMemEval Review — 长期交互记忆的系统性评测框架
|
||
- [[lou-autoharness-review]] — Review: AutoHarness — 自动合成代码 Harness 改进 LLM Agent
|
||
- [[lukv-review-20260618]] — Review: LU-KV — Global Combinatorial Optimization for KV Cache Eviction
|
||
- [[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'
|
||
- [[ma-intragent-review-20260604]] — IntrAgent — Content-Grounded Literature Retrieval Review
|
||
- [[mainecoon-review-20260620]] — MaineCoon Review
|
||
- [[mamba-review-20260618]] — Review: Mamba — Linear-Time Sequence Modeling with Selective State Spaces
|
||
- [[mathforge-review-20260512]] — MathForge Review — 2026-05-12
|
||
- [[mcp-zero-review-20260619]] — MCP-Zero Review
|
||
- [[me2-trm-reasoning-20260624]] — Review: ME² + TRM — Complex Reasoning Optimization
|
||
- [[minimax-policy-regret-pomg-20260610]] — Review: Minimax-Optimal Policy Regret in POMGs
|
||
- [[mozer-topological-trouble-review-20260618]] — Review: The Topological Trouble With Transformers
|
||
- [[nano-filter-20260622]] — NANO Filter Review
|
||
- [[neurida-review-20260515]] — NeurIDA 论文集成 Review
|
||
- [[one-pass-to-reason-review-20260602]] — Review: One-Pass to Reason — 多轮推理的高效单遍微调
|
||
- [[onereason-review-20260610]] — OneReason Review — 生成式推荐的推理能力解锁
|
||
- [[ortega-phd-review-20260617]] — Ortega PhD Thesis 集成 Review
|
||
- [[peng-tst-2026-review]] — Review: Token Superposition Training
|
||
- [[personalization-trap-20260624]] — Review: The Personalization Trap
|
||
- [[predictive-representations-mtrl-20260610]] — Review: Predictive Representations for Scalable Multitask Deep RL
|
||
- [[pretrain-space-rl-review-20260518]] — Review: Pre-train Space Reinforcement Learning
|
||
- [[principled-uncertainty-clinical-ai-20260610]] — Review: Principled Uncertainty in Clinical AI
|
||
- [[prompt-caching-architecture-review-20260511]] — Review: Prompt Caching 架构工程手册
|
||
- [[pydantic-three-piece-review-20260610]] — Pydantic 三件套 Review — 从校验库到 AI 基础设施
|
||
- [[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
|
||
- [[relu-neuromanifolds-20260610]] — Review: ReLU Neuromanifolds — Fibers and Semi-algebraicity
|
||
- [[repmt-sac-review-20260617]] — RepMT-SAC 论文集成 Review
|
||
- [[rwkv7-review-20260618]] — Review: RWKV-7 Goose — Expressive Dynamic State Evolution
|
||
- [[skills-to-genes-review-20260614]] — Skills to Strategy Genes — Review 报告
|
||
- [[stem-causal-sparse-attention-review-20260605]] — Stem: Rethinking Causal Information Flow in Sparse Attention — Review
|
||
- [[streaming-llm-review-20260514]] — Review: StreamingLLM — 基于注意力汇的无限长流式语言模型
|
||
- [[tarpo-review-20260617]] — TARPO 论文集成 Review
|
||
- [[tba-review-20260512]] — TBA Review — 2026-05-12
|
||
- [[thinking-with-visual-primitives-review-20260430]] — Review — Thinking with Visual Primitives
|
||
- [[ticks-to-flows-review-20260617]] — Ticks-to-Flows 论文集成 Review
|
||
- [[token-economics-review-20260605]] — Token Economics for LLM Agents — Review
|
||
- [[toolcua-review-20260531]] — ToolCUA Review: GUI-Tool路径编排的概念网络分析
|
||
- [[ultradata-l3-review]] — Review: UltraData — 大模型数据分级治理的开源实践
|
||
- [[unlimited-ocr-works-20260624]] — Review: Unlimited OCR Works
|
||
- [[vla-jepa-20260624]] — Review: VLA-JEPA
|
||
- [[wan-streamer-2026-06-25]] — Wan-Streamer v0.1 Review
|
||
- [[weighted-uat-review-20260617]] — Weighted UAT 论文集成 Review
|
||
- [[xu-why-steering-works-review-20260601]] — Review: Why Steering Works — 参数动态统一视角
|
||
- [[yang-skillopt-review]] — Review: SkillOpt — Agent Skill 的文本空间优化器
|
||
- [[zhang-sft-interaction-review-20260603]] — Review: Reconciling Contradictory Views on SFT in LLMs — 交互视角
|
||
- [[zhou-agent-symbolic-learning-review]] — Review: Agent Symbolic Learning — 符号学习驱动的自进化Agent
|