Seminal AI Research Paper Breakdowns
Architectural teardowns, mathematical proofs, and BibTeX citations of seminal intelligence papers.
Attention Is All You Need: The Transformer Architecture Explained
The landmark research paper that introduced the Transformer architecture, replacing recurrent neural networks (RNNs) and convolutional networks (CNNs) with pure...
Read Architectural Teardown →DeepSeek-R1: Emergence of Reasoning via Pure Reinforcement Learning
Demonstrated that large language models can develop complex reasoning behaviors—including self-verification, backtracking, and exploration—purely through large-...
Read Architectural Teardown →Constitutional AI: Harmlessness from AI Feedback (Anthropic)
Introduced Constitutional AI (CAI), replacing extensive human crowdworker feedback with automated Reinforcement Learning from AI Feedback (RLAIF) guided by a wr...
Read Architectural Teardown →High-Resolution Image Synthesis with Latent Diffusion Models
The research paper that created Stable Diffusion by shifting the diffusion process from raw high-dimensional pixel space into a compressed, perceptually equival...
Read Architectural Teardown →FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Revolutionized deep learning inference and training by making the attention mechanism aware of GPU memory hierarchy (SRAM vs HBM), unlocking million-token conte...
Read Architectural Teardown →FrontierMath: The Benchmark for Evaluating Advanced Mathematical Reasoning
Introduced FrontierMath, a benchmark of hundreds of original, research-level mathematical problems created by leading mathematicians to test the limits of AI re...
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