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 Paper Breakdown: Reasoning via Reinforcement Learning (arXiv:2501.12948)
Complete architectural teardown of DeepSeek-R1 (arXiv:2501.12948). Discover how Group Relative Policy Optimization (GRPO) incentivizes emergent reasoning, test-...
Read Architectural Teardown →Constitutional AI: Harmlessness from AI Feedback (arXiv:2212.08073 Anthropic Paper)
Complete architectural teardown of Anthropic's landmark paper (arXiv:2212.08073 by Yuntao Bai et al.). Learn how RLAIF, critique-revision loops, and written con...
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...
Read Architectural Teardown →Non-Autoregressive Neural Sequence Generation: Foundational Research Behind Parallel Sampling
The pioneering paper that broke the sequential decoding barrier in deep transformers, demonstrating that neural networks can predict sequence positions concurre...
Read Architectural Teardown →Visual Jev: Accurate and Efficient Decisions from Shared Visual Context
Introduced the Visual Jev architecture for vision systems requiring multiple forced-choice decisions on a single image. Evaluates batched question suffixes over...
Read Architectural Teardown →From Text Decisions to Pixels: An Study of Jev-Style Visual Choice Model (PixelJev)
Introduced PixelJev, a native-image decision interface mapping raw pixels, task instructions, and dynamic candidate choices directly to structured classificatio...
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