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Papers that changed AI, with a plain-language explainer for each.
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- 2026
GLM-5: from Vibe Coding to Agentic Engineering
arXiv
Zhipu's GLM-5 report describes a 744-billion-parameter mixture-of-experts model built on sparse attention and asynchronous reinforcement learning, aimed at moving AI-assisted programming from vibe coding toward agentic engineering.
- 2026
Terminal-Bench: Benchmarking Agents on Hard, Realistic Tasks in Command Line Interfaces
arXiv
Terminal-Bench 2.0 is a curated benchmark of 89 hard tasks carried out in computer terminal environments, built to measure how well AI agents handle realistic command-line work; frontier models and agents score below 65% on it.
- 2025
The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text
arXiv
An eight-terabyte collection of public domain and openly licensed text for pretraining language models, released together with two 7B-parameter models trained on it to show the data is enough.
- 2024
The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
arXiv
FineWeb is a 15-trillion-token pretraining dataset built from 96 Common Crawl snapshots and released in 2024 with its curation code and ablation models; its educational subset, FineWeb-Edu, improves results on knowledge- and reasoning-heavy benchmarks.
- 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.
- 2024
LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
arXiv
A benchmark for code LLMs that keeps collecting fresh contest problems from LeetCode, AtCoder and CodeForces, so that scores reflect ability rather than memorised test data, and that also tests code repair and execution reasoning.
- 2024
Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
arXiv
Dolma is an openly released English corpus of three trillion tokens, built from web text, papers, code, books, social media and Wikipedia, with a paper documenting both the dataset and the pipeline used to curate it.
- 2023
Scaling deep learning for materials discovery
Nature
A 2023 Nature paper from researchers at Google DeepMind presenting a scaled-up deep-learning approach to predicting stable crystal structures, screening 2.2 million candidates and reporting a large jump in stability-prediction accuracy.
- 2023
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
arXiv
A benchmark of 2,294 real software engineering problems taken from GitHub issues and the pull requests that resolved them, in which a model must edit a codebase to fix an issue; on release the best model solved under 2% of them.
- 2023
Segment Anything
arXiv 2304.02643
A 2023 paper that introduces the Segment Anything project: a promptable segmentation task, a model, and a dataset of over 1 billion masks on 11 million images, all released to advance foundation models for computer vision.
- 2023
RedPajama: open reproduction of the LLaMA training dataset
Together AI blog / GitHub
RedPajama is an open-source project that began by reproducing the LLaMA training dataset, releasing a 1.2-trillion-token corpus as a clean-room, fully open implementation.
- 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
Holistic Evaluation of Language Models (HELM)
arXiv
A 2022 paper by Percy Liang, Rishi Bommasani, Tony Lee and many co-authors that ran 30 language models through the same 42 scenarios and seven metrics, turning scattered benchmarks into one standardized evaluation.
- 2022
LAION-5B: An open large-scale dataset for training next generation image-text models
arXiv
LAION-5B is an openly available dataset of 5.85 billion CLIP-filtered image-text pairs, released so that researchers outside a handful of labs could study and reproduce large vision-language models.
- 2022
Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (Imagen)
arXiv
Imagen is a text-to-image diffusion model that uses a large pretrained language model to encode prompts; it reached a zero-shot FID of 7.27 on COCO and introduced the DrawBench benchmark.
- 2022
Training Compute-Optimal Large Language Models (Chinchilla)
NeurIPS
A DeepMind study showing that, for a fixed computing budget, language models should be trained on far more data than was then standard; its 70-billion-parameter Chinchilla beat larger contemporaries.
- 2021
High-Resolution Image Synthesis with Latent Diffusion Models
arXiv 2112.10752 (CVPR 2022)
A 2021 paper by Rombach, Blattmann, Lorenz, Esser and Ommer that runs diffusion models inside the latent space of pretrained autoencoders, cutting the compute needed for high-resolution image synthesis while adding cross-attention for text and bounding-box control.
- 2021
Program Synthesis with Large Language Models (MBPP)
arXiv
A 2021 arXiv paper by Austin, Odena, Nye and eight other authors that introduced MBPP, a benchmark of 974 short Python programming tasks, and measured how language models from 244M to 137B parameters turn natural-language descriptions into working code.
- 2021
Evaluating Large Language Models Trained on Code (Codex / HumanEval)
arXiv
A 2021 paper from OpenAI introduces Codex, a GPT model fine-tuned on public GitHub code, and HumanEval, an evaluation set that judges generated Python programs by whether they work rather than by how closely they match a reference.
- 2021
Zero-Shot Text-to-Image Generation (DALL·E)
arXiv
A 2021 OpenAI paper describing a single 12-billion-parameter autoregressive transformer trained on 250 million image-text pairs, which generates images from text and, evaluated zero-shot, is competitive with domain-specific models.