データベースセクション · 12 レコード
リサーチ
The papers the modern stack is built on — from the transformer to scaling laws, RLHF and reasoning models.
フィールドノート
数十本のPDFが、1兆ドル規模のインフラを支えている。アイデアが最初に登場した論文がここにある――その多くは驚くほど読みやすく、各レコードは数式なしで「何が重要だったか」を伝える。
まずこの三つから
AI
Attention Is All You Need (2017)
8人の著者、一つのアーキテクチャ、そしてその後のすべて。
TC
Training Compute-Optimal LLMs — Chinchilla (2022)
すべてのモデルの学習方法を変えた、修正。
DR
DeepSeek-R1 — Reasoning via RL (2025)
推論のオープンレシピ――競合他社が秘密にしている間に公開された。
Foundational papers
12
AI
Attention Is All You Need (2017)
The eight Google authors who invented the transformer.
A(
AlexNet (2012)
The ImageNet result that started the deep-learning era — on two gaming GPUs.
CA
Constitutional AI (2022)
Anthropic's method: models critique themselves against written principles.
DR
DeepSeek-R1 — Reasoning via RL (2025)
The open recipe for reasoning models — a Nature cover, from a hedge fund's lab.
DD
Denoising Diffusion Probabilistic Models (2020)
The DDPM paper behind every image and video generator.
F(
FlashAttention (2022)
The kernel trick that made long context affordable.
HA
Highly Accurate Protein Structure Prediction (2021)
AlphaFold 2 in Nature — the result that won a Nobel.
I—
InstructGPT — RLHF at Scale (2022)
How raw language models became assistants people can use.
LM
Language Models are Few-Shot Learners (2020)
The GPT-3 paper: capability from scale, tasks from prompts.
LL
LoRA: Low-Rank Adaptation (2021)
Fine-tuning for the GPU-poor — tiny adapters instead of full weights.
SL
Scaling Laws for Neural Language Models (2020)
Kaplan et al.: loss falls as a power law in compute — the investment thesis, quantified.
TC
Training Compute-Optimal LLMs — Chinchilla (2022)
DeepMind's correction: most models were undertrained on data.