Tag: architecture
-
智能体 RL 与推理搜索全景：MCTS 蒙特卡洛树搜索、PRM 过程监督与 RLVR 可验证奖励
> **核心摘要**：随着大语言模型迈向 **Agentic 自主智能体** 与 **System 2 慢思考** 阶段，传统单步静态输出已被长链轨迹规划 (Trajectory Planning)、多步工具调用与试错反思所取代。**智能体强化学习 (Agentic RL)** 将环境反馈与决策树搜索结合，形成了以 *
-
推荐系统工业级架构设计：召回-精排-重排三阶段、双塔模型与离在线一致性 Feature Store
> **核心摘要**：推荐系统是电商（淘宝/Amazon）、短视频（抖音/TikTok）以及信息流（小红书/Pinterest）的核心商业引擎。面对千万级 Item 与亿级 User，任何单一模型都无法在 **50ms 延迟 SLA** 内对全量候选打分，因此工业界采用**漏斗式多阶段架构：召回 (Retrieval)
-
DS A/B Testing Case Studies: CUPED, SRM Checks & Attribution
> **Executive Summary**: A/B testing is the gold standard for data-driven product decisions. In real-world enterprise environments, data scientists face three c
-
MLE System Design Guide: Recommendation, Search & Risk Control
> **Executive Summary**: Machine Learning System Design separates senior Machine Learning Engineers (MLE) and AI Architects from junior modelers. Candidates mus
-
RAG Pipeline: From Naive RAG to Advanced RAG Architecture, Hybrid Search, RRF & Cross-Encoder
> **Core Executive Summary**: Large Language Models suffer from knowledge cutoffs and hallucinations. **RAG (Retrieval-Augmented Generation)** connects LLMs to
-
KV Cache Management: Exact Bounds Derivation, vLLM PagedAttention & Prefix Caching
> **Core Executive Summary**: Autoregressive LLM generation requires caching key-value states to eliminate $O(N^2)$ recomputation. However, **KV Cache** imposes
-
Graph Neural Networks (GNN) Taxonomy: Graph Laplacian, Message Passing (MPNN), GCN, GraphSAGE, GAT & Edge Feature Guide
> **Summary**: Representation learning on non-Euclidean graph-structured data is fundamental to modern recommender systems and molecular modeling. This 100% exh
-
Classical NLP Tasks: NER, Text Classification, seq2seq Translation & NLI Entailment
> **Core Executive Summary**: Classical NLP established the foundations of text sequence modeling prior to large language models. From **NER (Named Entity Recog
-
Linear & Logistic Regression: Mathematical Derivations, Log-Odds, MLE, VIF & Bias-Variance Full Guide
> **Summary**: This comprehensive guide systematically covers the complete mathematical framework for Linear and Logistic Regression. We detail the 5 classical
-
Sampling & Monte Carlo Methods: Inverse Transform, Rejection, Importance Sampling, MCMC & Bootstrap
> **Core Executive Summary**: Sampling theory answers a fundamental question — how do we draw random values from a target distribution when we can only cheaply