Show HN: Landmark AI and ML research explained, redrawn, animated

Rudrite Research — the frontier, made legible

Interactive, animated, visual explainers of landmark AI & ML papers — the systems and ideas behind the models you use, redrawn and made legible. Free and open.

Browse all 100 explainers · Guided reading tracks

Attention Is All You Need

FlashAttention

PagedAttention (vLLM)

Megatron-LM

DeepSeek-R1

GPT-3: Language Models are Few-Shot Learners

ZeRO: Zero Redundancy Optimizer

Mixtral of Experts

Training Compute-Optimal Large Language Models

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

BERT: Pre-training of Deep Bidirectional Transformers

DeepSeek-V3

Qwen3

OLMo 2

MiniMax-01

Gemma 4

Scaling Laws for Neural Language Models

Adam: A Method for Stochastic Optimization

Deep Residual Learning for Image Recognition

Denoising Diffusion Probabilistic Models

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

LoRA: Low-Rank Adaptation of Large Language Models

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

GSPMD: General and Scalable Parallelization for ML Computation Graphs

Pathways: Asynchronous Distributed Dataflow for ML

Ring Attention with Blockwise Transformers for Near-Infinite Context

Efficiently Scaling Transformer Inference

Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving

Fast Inference from Transformers via Speculative Decoding

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Training language models to follow instructions with human feedback

Direct Preference Optimization: Your Language Model is Secretly a Reward Model

DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Constitutional AI: Harmlessness from AI Feedback

DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Tree of Thoughts: Deliberate Problem Solving with Large Language Models

ReAct: Synergizing Reasoning and Acting in Language Models

FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision

Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality

DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model

EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

RoFormer: Enhanced Transformer with Rotary Position Embedding

An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Learning Transferable Visual Models From Natural Language Supervision

High-Resolution Image Synthesis with Latent Diffusion Models

Scalable Diffusion Models with Transformers

Robust Speech Recognition via Large-Scale Weak Supervision

Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention

Group Sequence Policy Optimization

DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving

CacheBlend: Fast Large Language Model Serving for RAG with Cached Knowledge Fusion

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

YaRN: Efficient Context Window Extension of Large Language Models

Efficient Streaming Language Models with Attention Sinks

Generative Adversarial Networks

Segment Anything

Visual Instruction Tuning

s1: Simple test-time scaling

Tülu 3: Pushing Frontiers in Open Language Model Post-Training

Let's Verify Step by Step

Self-Consistency Improves Chain of Thought Reasoning in Language Models

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

KAN: Kolmogorov–Arnold Networks

Differential Transformer

Mixture-of-Depths: Dynamically allocating compute in transformer-based language models

RWKV: Reinventing RNNs for the Transformer Era

Titans: Learning to Memorize at Test Time

Byte Latent Transformer: Patches Scale Better Than Tokens

The Llama 3 Herd of Models

Mistral 7B

Phi-4 Technical Report

FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Scaling Rectified Flow Transformers for High-Resolution Image Synthesis

Flow Matching for Generative Modeling

Beyond Binary Rewards: Training LMs to Reason About Their Uncertainty

Rewarding Doubt: Calibrated Confidence Expression of LLMs

Why Language Models Hallucinate

τ-bench: Tool-Agent-User Interaction in Real-World Domains

ToolRL: Reward is All Tool Learning Needs

Group-in-Group Policy Optimization for LLM Agent Training

MiniMax-M1: Scaling Test-Time Compute with Lightning Attention

ProRL: Prolonged RL Expands Reasoning Boundaries

The Entropy Mechanism of RL for Reasoning Language Models

Spurious Rewards: Rethinking Training Signals in RLVR

GenPRM: Generative Process Reward Models

From Hard Refusals to Safe-Completions

Proximal Policy Optimization Algorithms

Efficiently Modeling Long Sequences with Structured State Spaces

Auto-Encoding Variational Bayes

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Toolformer: Language Models Can Teach Themselves to Use Tools

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Muon is Scalable for LLM Training

Consistency Models