$ show mental2008/awesome-papers
This repository is a curated, actively-maintained personal knowledge base of academic paper reading notes, focused on systems for machine learning, cloud computing, and related areas. It is organized by conference venue (e.g., OSDI, SOSP, MLSys) and by topic (e.g., LLM systems, GPU sharing, memory disaggregation). The core content consists of markdown files with structured summaries (TL;DR, background, design, evaluation) and cross-linked topic aggregation pages. The repository is the source for a GitBook static site and includes scripts for building and serving that site, as well as utilities for PDF text extraction. While not a data pipeline or software application, it functions as a high-quality research resource with consistent formatting, curation guidelines, and reusable templates for note-taking and paper list organization.
The LLM identified 3 files worth highlighting for reuse:
A comprehensive, hierarchical index of LLM systems papers organized by training, inference, and alignment. Each entry includes title, venue, affiliations, links (paper, arXiv, code), and optional personal note links. The structure (with 'reading map' hint boxes and sub-categories) serves as a reusable template for curating research topics in any ML domain.
Detailed guidelines for curating conference pages, including selection criteria (prioritize LLM systems, model serving, distributed training), subgroup taxonomy, entry formatting, and anti-patterns. This skill is a standalone, reusable document that can be adapted for any research community maintaining a paper list.
A representative paper reading note following the consistent template: Meta Info, TL;DR, Background, Designs, Implementation, Evaluation, Limitations. This pattern is applied across all notes and can be extracted as a template for any technical paper review, demonstrating reproducibility and clarity.