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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.
- 2024
FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision
arXiv
FlashAttention-3 is an attention algorithm for NVIDIA Hopper (H100) GPUs that reaches up to 75% utilization in FP16 and close to 1.2 PFLOPs/s in FP8, using asynchronous execution and low-precision techniques.
- 2023
FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning
arXiv
FlashAttention-2 is a 2023 revision of the FlashAttention attention kernel that reorganizes work across GPU thread blocks and warps, roughly doubling speed and reaching up to 73% of the A100's theoretical peak throughput.
- 2023
QLoRA: Efficient Finetuning of Quantized LLMs
arXiv
QLoRA is a finetuning method that trains a 65B-parameter language model on a single 48GB GPU while matching full 16-bit finetuning quality, using 4-bit quantization, double quantization and paged optimizers.
- 2022
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
arXiv
FlashAttention is an exact attention algorithm built around GPU memory traffic: by tiling computation so intermediates stay on chip, it cuts reads and writes to high bandwidth memory, training Transformers faster and enabling longer sequences.
- 2021
LoRA: Low-Rank Adaptation of Large Language Models
arXiv
LoRA is a fine-tuning method that freezes a pre-trained model's weights and trains small low-rank matrices injected into each layer, cutting trainable parameters and memory by orders of magnitude.
- 2020
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding
arXiv
GShard, submitted to arXiv in June 2020, is a set of annotation APIs and an XLA compiler extension from Google that lets existing model code express parallel computation, scaling a sparsely gated mixture-of-experts translation model past 600 billion parameters.
- 2011
Torch7: A Matlab-like Environment for Machine Learning
A 2011 workshop paper by Ronan Collobert, Koray Kavukcuoglu and Clement Farabet describing Torch7, a Lua-based numeric computing framework and machine learning library built for flexible experimentation on fast low-level kernels.
- 2010
Understanding the difficulty of training deep feedforward neural networks
AISTATS / PMLR
A 2010 paper by Xavier Glorot and Yoshua Bengio on why deep feedforward networks are hard to train: sigmoid saturation under random initialization and layer Jacobians whose singular values sit far from 1, plus a new initialization scheme that converges substantially faster.
- 2004
MapReduce: Simplified Data Processing on Large Clusters
OSDI
Jeffrey Dean and Sanjay Ghemawat's 2004 OSDI paper presents MapReduce, a programming model in which users write a map function and a reduce function while the run-time system parallelizes the work across large clusters of commodity machines.