2026 edition · frozen 2026-10-29
AI reading lists
What should you actually read in AI? A ranked answer, computed by a published method from expert-list consensus, era-adjusted citations, test-of-time awards and existing must-read lists, with every editor's change recorded on the entry.
Top 500
The 500 most important AI research papers first released by 2025, from 1943 on. For researchers and practitioners.
100 papers to read in your lifetime
A reading list for learners: lasting ideas you can actually get through, with difficulty and what to read first.
12 domain pages
The best papers in each part of the field, from learning theory to AI for science.
Recent papers
Papers first released in 2026. They become eligible for the next edition.
Books
Textbooks and monographs. Ranked by the same score as the papers, but never inside the Top 500.
Surveys & tutorials
Reviews, tutorials and overviews that map a field rather than introduce one idea.
Other landmark works
Reports, essays, proposals and software papers that shaped the field but are not research papers.
Method
Signals, weights, eligibility rules and every editor's change, in the open.
From the Top 500
The top ten
- 1
ImageNet Classification with Deep Convolutional Neural Networks
Alex Krizhevsky · 132,655 citations · Semantic Scholar
AwardEditor's note - 2
Deep Residual Learning for Image Recognition
Kaiming He · 242,121 citations · Papers With Code
AwardEditor's note - 3
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau · 30,207 citations · Papers With Code
AwardEditor's note - 4
Intriguing properties of neural networks
Christian Szegedy · 17,244 citations · Semantic Scholar
AwardEditor's note - 5
- 6
Adam: A Method for Stochastic Optimization
Diederik P. Kingma · 172,427 citations · Papers With Code
AwardEditor's note - 7
You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon · 53,721 citations · OpenAlex
AwardEditor's note - 8
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe · 48,020 citations · Papers With Code
AwardEditor's note - 9
Sequence to Sequence Learning with Neural Networks
Ilya Sutskever · 22,497 citations · Papers With Code
AwardEditor's note - 10
ImageNet: A Large-Scale Hierarchical Image Database
Jia Deng · 78,060 citations · Semantic Scholar
AwardEditor's note
Data sources
Catalogue data from OpenAlex (CC0), Crossref (CC0 metadata; no abstracts are shown), arXiv, and Papers With Code (CC BY-SA 4.0), and 
Semantic Scholar (used under its non-commercial API licence). Each count names its source. See the method.