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Papers that changed AI, with a plain-language explainer for each.
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- 2025
ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems
arXiv
A 2025 paper introducing ARC-AGI-2, a benchmark of input-output puzzle tasks built to measure abstract reasoning in frontier AI systems, with human testing as a baseline and a task set designed to give a more granular signal of progress.
- 2024
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
ICML
Tri Dao and Albert Gu's paper introduces state space duality, a framework showing that structured state space models and attention variants are closely related, and uses it to derive Mamba-2, whose core layer runs 2 to 8 times faster than Mamba's.
- 2022
Learning skillful medium-range global weather forecasting
Science
GraphCast is a graph neural network trained on four decades of reanalysis data that produces 10-day global weather forecasts at 0.25 degree resolution in under a minute, outperforming the leading operational deterministic systems on most verification targets.
- 2022
Scalable Diffusion Models with Transformers (DiT)
arXiv
A 2022 paper by William Peebles and Saining Xie that replaces the U-Net backbone of diffusion models with a transformer operating on latent patches, showing that image quality improves predictably as compute grows and reaching a state-of-the-art FID of 2.27 on ImageNet 256×256.
- 2022
Magnetic control of tokamak plasmas through deep reinforcement learning
Nature
A 2022 Nature paper by Degrave, Felici and colleagues showing that a deep reinforcement learning controller can learn on its own to command all of a tokamak's magnetic control coils, producing and holding a range of plasma shapes.
- 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)
arXiv
A 2020 paper showing that a plain Transformer applied directly to sequences of image patches, with no convolution at all, can match state-of-the-art convolutional networks on image classification.
- 2020
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
arXiv 2010.11929 (ICLR 2021)
A 2020 paper by Alexey Dosovitskiy and colleagues showing that a pure Transformer applied directly to sequences of image patches — the Vision Transformer, or ViT — matches state-of-the-art convolutional networks on image classification when pre-trained on large amounts of data.
- 2020
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
arXiv
A 2020 paper by Mildenhall, Srinivasan, Tancik, Barron, Ramamoorthi and Ng that represents a scene as a neural radiance field: a network mapping 5D coordinates to density and colour, optimized from a sparse set of images with known camera poses.
- 2019
Mastering Atari, Go, chess and shogi by planning with a learned model
Nature
MuZero is a DeepMind reinforcement-learning algorithm that learns a model of its environment and plans inside that model, mastering Atari, Go, chess and shogi without being given the rules of those games.
- 2019
On the Measure of Intelligence (ARC-AGI)
arXiv
A 2019 paper by Francois Chollet that defines intelligence as skill-acquisition efficiency and introduces the Abstraction and Reasoning Corpus (ARC), a benchmark of grid tasks that assumes no specialised knowledge or language.
- 2019
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (T5)
JMLR
A 2019 Google paper that frames every language task as text in, text out, using one encoder-decoder Transformer and one training setup. It also introduced the C4 dataset, and the released T5 models became common research starting points.
- 2018
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
NAACL
Google's 2018 BERT paper introduced an encoder-only Transformer that reads text in both directions and is pre-trained by hiding words. It set new results on eleven language tasks and made download-and-fine-tune the default workflow.
- 2018
Improving Language Understanding by Generative Pre-Training (GPT)
OpenAI
OpenAI's 2018 paper introducing GPT, a Transformer decoder pre-trained to predict the next token and then fine-tuned for specific tasks. It improved the state of the art on 9 of 12 tasks studied and set the recipe the GPT series followed.
- 2017
A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play
Science
A 2017 arXiv paper, later published in Science, reporting that a single self-play reinforcement learning algorithm, AlphaZero, mastered chess, shogi and Go and beat strong specialised programs in each.
- 2017
Attention Is All You Need
NeurIPS
A 2017 paper that introduced the Transformer, a neural network built entirely on attention with no recurrence. It set new machine translation results and became the architecture behind almost every large language model since.
- 2016
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
arXiv
A 2016 paper describing Google's neural machine translation system, GNMT: a deep LSTM encoder-decoder with attention, designed to make end-to-end translation practical by addressing rare words, parallelism and inference cost.
- 2014
Neural Machine Translation by Jointly Learning to Align and Translate
ICLR
Submitted to arXiv in September 2014 by Dzmitry Bahdanau, Kyunghyun Cho and Yoshua Bengio, this paper let a translation model look back at the whole source sentence while producing each word. This mechanism, called attention, later became a basis for Transformer models.
- 1990
Elephants Don't Play Chess
Rodney Brooks's 1990 paper argued for building artificial intelligence from the ground up, with ongoing physical interaction with the environment as the main constraint on design, rather than through the abstract manipulation of symbols that had dominated the field for roughly thirty years.