About this project

This repository is a curated educational resource that distills the 107 most influential research papers shaping modern generative AI into accessible, comprehensive summaries. Designed for practitioners, students, and enthusiasts who lack the time to read 220,000+ words of dense academic literature, it provides structured learning paths, quick references, and decision guides across the full landscape of generative AI. The collection is organized into five major categories: Foundational Architectures (11 papers), Language Models (25 papers), Image Generation (14 papers), Multimodal (7 papers), and Techniques & Methods (50 papers). Each paper summary is housed in its own directory with a dedicated summary.md file, numbered in the order it entered the collection. Key papers covered include Attention Is All You Need (2017), BERT (2018), GPT-3 (2020), GPT-4 (2023), GPT-4o (2024), GPT-5 (2025), LLaMA series (2023-2025), DeepSeek-V3 and DeepSeek-R1 (2024-2025), Qwen3 (2025), Claude 3.5 Sonnet and Claude 4 Family (2024-2026), Gemini 2.5 and Gemini 3 (2025), Stable Diffusion (2022), DALL-E 3 (2023), Sora/DiT (2024), CLIP (2021), Whisper (2022), LLaVA (2023), GPT-4V (2023), SAM 2 (2024), and many more. The repository includes extensive documentation: a Learning Roadmap with four structured tracks from beginner to expert, a Reading Guide distinguishing historical context from modern relevance, a Quick Reference one-page overview, side-by-side Comparisons with trade-off analysis, a Glossary of 150+ key terms, and a Coverage & Gaps map showing what the collection covers and what is queued next. Papers are also indexed by category (INDEX.md), by topic tag across 45 topics (TAGS.md), and in a visual grid view (BROWSE.md). Machine-readable manifests are provided as papers.json and papers.csv, generated by a stdlib-only Python build pipeline (scripts/build_manifest.py) that also regenerates frontmatter, INDEX.md, TAGS.md, and the site navigation. Additional scripts handle cross-linking (add_cross_links.py) and relative-link validation (check_links.py) as a CI gate. The documentation site is published at patrickwiloak.github.io/genai-research-papers-summarized, featuring search, dark mode, and no signup required. The site is built with MkDocs using a hand-maintained mkdocs.yml configuration. Each paper summary includes a relevance indicator (CRITICAL, HIGH, HISTORICAL, or THEORY), key contributions, and links to the original paper or system card. The collection emphasizes practical understanding—explaining not just what each paper did, but why it matters and how it connects to modern systems. For example, the ResNet summary explains that residual connections (x + f(x)) are inside every Transformer block ever trained, while the Grouped-Query Attention summary explains why long context and local inference are now affordable. The repository is maintained by Patrick Wiloak at Nobler Works, a custom software consultancy. It is licensed under CC BY 4.0 and welcomes contributions via a documented CONTRIBUTING.md workflow. The collection was last updated in August 2026.