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#retrieval

23 articles

01 ·★ MEMBER·PAPER·9 min read Paper Walkthrough: The Design Fundamentals of Pixel Text Representation Learning An encoder that reads meaning straight off the pixels, never converting glyphs to character codes. This EMNLP 2026 paper argues that what decides its quality is not data volume but four design choices — explained from zero. 02 ·Paper Deep-Dives·★ MEMBER·PAPER·11 min read Paper Walkthrough — RoboTok: Mining the Web for Demonstrations That Move Like Yours A ground-up walkthrough of RoboTok (arXiv:2609.03199), a data engine that pulls manipulation-relevant human demonstrations out of web video. Covers how DTW defines 'similar motion', how that ranking is distilled into an embedding for inner-product search, and what the results do and don't show. 03 ·RAG & Retrieval·FREE·8 min read RAG vs Fine-Tuning — Which One, and When The two main ways to make an LLM better, compared on four axes: knowledge freshness, cost, hallucination, and data requirements. From metaphor to math to interactive demos to the mistakes that break production systems. 04 ·RAG & Retrieval·FREE·8 min read RAG vs Fine-Tuning — Which One, and When The two main ways to make an LLM better, compared on four axes: knowledge freshness, cost, hallucination, and data requirements. From metaphor to math to interactive demos to the mistakes that break production systems. 05 ·RAG & Retrieval·★ MEMBER·PAPER·13 min read Paper walkthrough: Hi-Q — splitting a question down to the granularity your corpus can actually retrieve The real bottleneck in multi-hop QA is a mismatch between the granularity of the question and the granularity of retrievable evidence. Hi-Q answers first, then expands only the nodes that failed — in dependency order. Here is the control rule, the tree, and the measured numbers, from zero background. 06 ·Training & Alignment·★ MEMBER·PAPER·13 min read Paper Walkthrough: It Takes Two to Match — Co-Evolving Both Sides of Retrieval with RL A paper that rebuilds the first stage of search by having two LLMs write keywords — one for queries, one for items — and training them in alternation until their vocabularies meet. Explained from scratch. 07 ·Audio & Speech·★ MEMBER·PAPER·14 min read Paper Walkthrough: VoiceMem — A Left Brain and a Right Brain for Voice Agents, at Zero Added Latency A from-scratch walkthrough of VoiceMem, a memory system for real-time speech interaction. A factual 'left brain' and an affective 'right brain' run in parallel, and the whole retrieval is hidden inside the silence a VAD already waits out — which is how it wins at a top-5 budget. 08 ·RAG & Retrieval·★ MEMBER·PAPER·12 min read Paper Walkthrough: WeMM-Embedding — Putting Text, Images and Video on One Ruler A walkthrough of WeMM-Embedding (2B/4B/9B), Tencent's universal multimodal embedding family. The unified pair format, the <embedding> token, Matryoshka dimensions, two-stage training and distillation — explained from scratch, using only the numbers the paper reports. 09 ·RAG & Retrieval·★ MEMBER·PAPER·13 min read Evaluating RAG in Practice — Turning “Seems Better” Into a Number RAG improvement work gets lost because quality is judged as one vague feeling. This article builds up the metrics that separate retrieval from generation, the definitions of faithfulness and relevance, how to build an eval set with synthetic QA and where that goes wrong, and how many questions you need before the number means anything. 10 ·RAG & Retrieval·★ MEMBER·PAPER·13 min read Build Your Own Vector DB — From Brute Force to HNSW Assemble a vector search engine step by step, starting from a 20-line brute-force scan. The curse of dimensionality, IVF partitioning, HNSW graph traversal and quantization, all viewed through one lens: the trade between recall and speed. 11 ·RAG & Retrieval·★ MEMBER·PAPER·13 min read Build Your Own Vector DB — From Brute Force to HNSW Assemble a vector search engine step by step, starting from a 20-line brute-force scan. The curse of dimensionality, IVF partitioning, HNSW graph traversal and quantization, all viewed through one lens: the trade between recall and speed. 12 ·RAG & Retrieval·★ MEMBER·PAPER·10 min read GraphRAG from Scratch — Where Knowledge Graphs Meet Retrieval Rebuild your documents as a web of entities and relationships and you can answer questions ordinary RAG cannot reach — the ones about the corpus as a whole. Extraction, entity resolution, community summarization, and local/global search from first principles, ending with an honest account of when it's overkill. 13 ·RAG & Retrieval·★ MEMBER·PAPER·10 min read GraphRAG from Scratch — Where Knowledge Graphs Meet Retrieval Rebuild your documents as a web of entities and relationships and you can answer questions ordinary RAG cannot reach — the ones about the corpus as a whole. Extraction, entity resolution, community summarization, and local/global search from first principles, ending with an honest account of when it's overkill. 14 ·RAG & Retrieval·★ MEMBER·10 min read Chunking Strategies — How You Split Decides What You Can Find Most of a RAG system's quality is decided by how you split documents. The arithmetic of fixed-size chunks and overlap, structural and semantic splitting, parent-child chunks, and the two things that break every splitter: tables and equations. 15 ·RAG & Retrieval·★ MEMBER·10 min read Chunking Strategies — How You Split Decides What You Can Find Most of a RAG system's quality is decided by how you split documents. The arithmetic of fixed-size chunks and overlap, structural and semantic splitting, parent-child chunks, and the two things that break every splitter: tables and equations. 16 ·RAG & Retrieval·★ MEMBER·PAPER·10 min read Recommenders and Embeddings — Same Math as RAG, Different Goal What sits behind "recommended for you" is very nearly the same math as RAG's vector search. A from-zero tour: matrix factorization, two-tower models, and how the ANN stack is reused — plus why the evaluation and the failure modes end up completely different. 17 ·RAG & Retrieval·★ MEMBER·PAPER·10 min read Recommenders and Embeddings — Same Math as RAG, Different Goal What sits behind "recommended for you" is very nearly the same math as RAG's vector search. A from-zero tour: matrix factorization, two-tower models, and how the ANN stack is reused — plus why the evaluation and the failure modes end up completely different. 18 ·Agents·★ MEMBER·PAPER·9 min read Paper Explainer: Why Agent Skills Work — and Where They Break Handing an agent a written procedure improves its scores. This paper dissects why, using 8,135 trial records and 528 matched trajectory triples. Skills work as procedural anchors rather than knowledge injection — and they break once the skill library grows. 19 ·RAG & Retrieval·FREE·PAPER·6 min read Embeddings from Scratch — from word2vec Intuition to Contextual Embeddings Why does 'king − man + woman ≈ queen' actually work? A from-zero tour of embeddings: the map metaphor, the distributional hypothesis, the word2vec math, numpy code, contextual embeddings after BERT, and the knobs you touch in real RAG systems. 20 ·RAG & Retrieval·FREE·PAPER·6 min read Embeddings from Scratch — from word2vec Intuition to Contextual Embeddings Why does 'king − man + woman ≈ queen' actually work? A from-zero tour of embeddings: the map metaphor, the distributional hypothesis, the word2vec math, numpy code, contextual embeddings after BERT, and the knobs you touch in real RAG systems. 21 ·Inference & Serving·★ MEMBER·PAPER·8 min read Paper Walkthrough: CodeNib — A Multi-View Data System That Serves Repository Context to Coding Agents Coding agents grep their way through the same repository over and over. CodeNib (UC San Diego et al.) reframes this as a database problem — lexical, dense, and structural views over an immutable commit — and measures the whole lifecycle, caveats included. 22 ·RAG & Retrieval·★ MEMBER·8 min read RAG Fundamentals and Design Patterns — Embeddings, Chunking, Reranking, and Evaluation from Scratch Retrieval-Augmented Generation explained from zero: the core intuition, chunking strategies, hybrid search and reranking, and the evaluation design that matters most. 23 ·RAG & Retrieval·★ MEMBER·8 min read RAG Fundamentals and Design Patterns — Embeddings, Chunking, Reranking, and Evaluation from Scratch Retrieval-Augmented Generation explained from zero: the core intuition, chunking strategies, hybrid search and reranking, and the evaluation design that matters most.