# aimerou/awesome-ai-papers

A curated list of the most impressive AI papers

Repository: https://github.com/aimerou/awesome-ai-papers
Canonical: https://ross.abutalabs.com/products/awesome-ai-papers
License Family: other
Topics: ai, deep-learning, machine-learning, papers, research, sota, state-of-the-art, awesome, awesome-list
Last push: 2025-07-01T13:31:54+00:00

## Health v2 (maintenance only)
Score: 44/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 29, release rhythm 35, longevity 91
- inputs: {"age_days": 1277, "days_push": 428, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1306, forks 118 (observed 2026-08-28T04:04:18.801240+00:00)

## What it is
A curated, regularly updated list of the most significant AI research papers organized by publication date and field, covering computer vision, NLP, audio processing, multimodal learning, and reinforcement learning. Papers are classified by citation-based criteria such as historical, important, and trending works.

## Use cases
- find the most influential AI research papers
- keep up with state-of-the-art machine learning papers
- discover important computer vision and NLP papers by month
- find historical AI papers with major impact
- get a curated reading list for learning deep learning
- track trending new AI papers with growing adoption

## When to choose
- you want a citation-ranked, organized overview of landmark AI papers across multiple subfields
- you need a chronological reading list spanning 2022, 2023, and historical works
- you want to quickly survey state-of-the-art results in vision, NLP, audio, multimodal, and RL

## When to avoid
- you need full paper content or implementations rather than links
- you require comprehensive coverage of every paper rather than a subjective curated selection
- you need papers from fields outside the five covered areas

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation
- domain: artificial-intelligence, machine-learning, deep-learning, computer-vision, awesome-lists
- platform: -
- tags: awesome-list, research-papers, curated-list, state-of-the-art, academic-papers, natural-language-processing, audio, web-server

## Member repositories
- aimerou/awesome-ai-papers (main) score 44

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:18.801240+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:50:57.065009+00:00, confidence not recorded.
  - readme: https://github.com/aimerou/awesome-ai-papers (fetched 2026-08-28T04:04:18.801240+00:00, sha 8db15c8a126c)
- Data as of 2026-08-30T08:39:29.467469+00:00.