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RAG.md · 17 documents

RAG.md

Effect-Atom MCP Enhancement Research Report

**Date**: October 23, 2025

airageval
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2
kriegcloud
RAG.md

Deep Learning for AI

Yoshua Bengio, Yann LeCun, Geoffrey Hinton

ai
0
3
Mycenae
RAG.md

AI Scientist Lab Notebook — Architecture (V1)

**Frontend/BFF:** Next.js 14 on Vercel (SSR for viewers, ISR for public reports, Server Actions for signed uploads).

airagprompt
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0
derril-tech
RAG.md

Technical Book Rack RAG — Implementation Plan

> **Archived 2026-03-17** — Implementation exceeded plan (MCP server added beyond scope)

aillmrag
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0
rasha-hantash
RAG.md

四、随机化 SVD

本节的目的是用单词向量的具体例子，来说明随机投影保留结构的想法！

ai
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3
apachecn
RAG.md

draft

This repository summaries Transformer-based architectures in the Computer Vision aspect, from the very basic (classification) to complex (object detection, segmentation, few-shot learning) tasks.

aieval
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0
quanghuy0497
RAG.md

The Annotated Encoder-Decoder with Attention

<script src="https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML" type="text/javascript"></script>

ai
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0
bastings
RAG.md

RFC-001: Continuity

> *In fact, forget the park. And the blackjack.*

airagclaude
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0
lazypower
RAG.md

📚 RAG with Mistral - Technical Documentation

- [Overview](#overview)

aillmrag
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0
CLoaKY233
RAG.md

llm-hallucination-survey

![](https://img.shields.io/badge/PRs-welcome-brightgreen)

aillmeval
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5
HillZhang1999
RAG.md

NLP - Dialogue System

|Paper|Conference|Remarks

aieval
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0
zhongpeixiang
RAG.md

RALM_Survey

This is a repository of RALM surveys containing a summary of state-of-the-art RAG and other technologies according to according to our survey paper: [RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing](https://arxiv.org/abs/2404.19543v1) . In this repository, we will present the most central research approach of our thesis as well as keep up-to-date with work on RALM in the most accessible way possible. For more detailed information, please read our papers

aillmrag
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0
2471023025
RAG.md

REFRAG: Retrieval-Enhanced Fine-Grained Retrieval Augmented Generation

REFRAG is a breakthrough approach to Retrieval-Augmented Generation (RAG) from Meta's Superintelligence Labs that fundamentally reimagines how retrieved information flows into language models. Instead of converting retrieved vectors back to text for LLM processing, REFRAG passes the vectors directly to the language model, achieving dramatic performance improvements.

aillmrag
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0
marcusjihansson
RAG.md

Notes

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://arxiv.org/abs/2312.10997v1)

aillmrag
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0
yogeshhk
RAG.md

RAG-LLaMA3 AI Project

We utilize Retrieval Augmented Generation on the LLaMA3 model to create an AI agent that can answer questions about bioinformatics software DNALinux. It helps users navigate through a large range of bioinformatics tools. Additionally, you will be able to create a simple RAG AI agent with your own resources.

aiagentrag
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0
DNALinux
RAG.md

Why We Move from RLM to RLM-Graph

This document explains **why** we evolve from a traditional **Recursive Language Model (RLM)** to **RLM-Graph**.

aiagentrag
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0
omardimarzio
RAG.md

THE LJPW CODEX

**Version:** 5.1 (The Expansive Edition)

0
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BruinGrowly