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Papers that changed AI, with a plain-language explainer for each.
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- 2026
Kimi K3: Open Frontier Intelligence
arXiv
Kimi K3 is Moonshot AI's 2.8-trillion-parameter open mixture-of-experts model with native vision and a one-million-token context, released with full weights. Its report describes the architecture, training and serving work behind it, and reports frontier-level results that still trail the strongest proprietary models.
- 2026
DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence
arXiv
A preview report on the DeepSeek-V4 series, describing a 1.6-trillion-parameter Pro model and a 284-billion-parameter Flash model, both with one-million-token context, plus the attention, connection and optimization changes that make that length affordable.
- 2026
Qwen3.5-Omni Technical Report
arXiv
The Qwen3.5-Omni Technical Report documents Alibaba's Qwen3.5-Omni, a hundreds-of-billions-parameter model that understands long audio and video and generates speech across 10 languages, reporting state-of-the-art results on 215 audio and audio-visual benchmarks.
- 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.
- 2025
Kimi K2: Open Agentic Intelligence
arXiv
Kimi K2 is Moonshot AI's trillion-parameter mixture-of-experts model, pre-trained with the MuonClip optimizer and post-trained on large-scale agentic data plus reinforcement learning; the report reports state-of-the-art results among open-source non-thinking models and releases base and post-trained checkpoints.
- 2025
Qwen3 Technical Report
arXiv
The Qwen3 Technical Report presents Alibaba's Qwen3 family of dense and mixture-of-experts language models, which unify thinking and non-thinking modes, add a controllable thinking budget, and are released under the Apache 2.0 licence.
- 2025
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
arXiv
The DeepSeek-R1 report described how a reasoning model was trained largely with reinforcement learning, rewarding correct answers, and reached performance comparable to OpenAI's o1 on math, code and reasoning benchmarks. Its weights were released openly.
- 2024
DeepSeek-V3 Technical Report
arXiv
A technical report from DeepSeek-AI on DeepSeek-V3, a mixture-of-experts language model with 671 billion total parameters, describing its architecture, training pipeline, and evaluations, including a full training run of 2.788 million H800 GPU hours.
- 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
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.
- 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
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.
- 2020
Denoising Diffusion Probabilistic Models
NeurIPS
A 2020 paper by Jonathan Ho, Ajay Jain and Pieter Abbeel that trained a U-Net to reverse a gradual noising process, making diffusion models a practical rival to GANs and paving the way for DALL-E 2, Imagen and Stable Diffusion.
- 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.
- 2018
A Style-Based Generator Architecture for Generative Adversarial Networks
arXiv
This 2018 paper introduces a style-based GAN generator that separates high-level attributes from stochastic variation and allows scale-specific control, improving distribution quality, interpolation, and disentanglement.
- 2018
Large Scale GAN Training for High Fidelity Natural Image Synthesis
arXiv
A 2018 paper by Andrew Brock, Jeff Donahue and Karen Simonyan that trained generative adversarial networks at the largest scale attempted at the time, introduced BigGANs and set a new state of the art in class-conditional image synthesis.
- 2018
Glow: Generative Flow with Invertible 1x1 Convolutions
arXiv
A 2018 paper by Diederik P. Kingma and Prafulla Dhariwal presenting Glow, a flow-based generative model built on invertible 1x1 convolutions, which improved log-likelihood on standard benchmarks and could synthesise and manipulate large images.
- 2017
Neural Discrete Representation Learning
arXiv
A 2017 paper by van den Oord, Vinyals and Kavukcuoglu proposing VQ-VAE, a generative model whose latent code is discrete rather than continuous, and whose prior is learned rather than fixed, so that useful representations can be learned without supervision.
- 2017
Progressive Growing of GANs for Improved Quality, Stability, and Variation
arXiv
A 2017 paper by Karras, Aila, Laine and Lehtinen that trains generative adversarial networks by growing the generator and discriminator from low to high resolution, improving stability, quality and variation.
- 2017
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
arXiv
CycleGAN learns image-to-image translation between two domains without aligned pairs, combining an adversarial loss with a cycle-consistency constraint to constrain the mapping.