Current study materials

There is only one model - by Jack Morris - Token for Token

The Big LLM Architecture Comparison

Cohere Labs: 2025 Summer School - recorded talks

FIIR

Google File System - Ghemawat, Gobioff, Leung: paper.dvi - gfs-sosp2003.pdf

MapReduce: Simplified data processing on large clusters - Dean, Ghemawat: mapreduce-osdi04.pdf

Stanford CS336 | Language Modeling from Scratch

Marin

Introduction | RLHF Book by Nathan Lambert

SciCode - SciCode Benchmark

Aurora GPT - Argonne National Lab

Evaluation Framework for AI Systems in "the Wild" | alphaXiv

EvalEval 2024 - neurips 2024 workshop

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects - 2505.18893v4.pdf

Physics of Language Models - Allen Zhu

Lecture Videos | Introduction to Algorithms | Electrical Engineering and Computer Science | MIT OpenCourseWare

Blob store design challenges - Iroh

[2502.15657] Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?

Bertsekas - RL courses and book - mit.edu/~dimitrib/RLbook.html

Puterman - Markov Processes book - martyput/MDP_book

Raft implemented in Go, Eli Bendersky

Layers of Memory, Layers of Compression - Tim Kellogg

CSES - CSES Problem Set - Tasks

Things that go wrong with disk IO | notes.eatonphil.com

Dependency Injection for Artificial Intelligence (DI4AI)

[2503.05336v3] Toward an Evaluation Science for Generative AI Systems

Statistical Significance, p-Values, and the Reporting of Uncertainty - imbens-2021-statistical-significance-p-values-and-the-reporting-of-uncertainty.pdf

AI and the Everything in the Whole Wide World Benchmark

A Philosophy of Software Design - John Ousterhout (pdf)

[1807.02811] A Tutorial on Bayesian Optimization

Going beyond open data – increasing transparency and trust in language models with OLMoTrace | Ai2

Tracing the thoughts of a large language model \ Anthropic

Bridging the human–AI knowledge gap through concept discovery and transfer in AlphaZero | PNAS

[2401.17173] Zero-Shot Reinforcement Learning via Function Encoders

Introducing Cogito Preview

Severe deviation in protein fold prediction by advanced AI: a case study | Scientific Reports

Building multi-source ingestion pipelines the right way

MouseGPT: A Large-scale Vision-Language Model for Mouse Behavior Analysis - 2025.03.27.645630v1.full.pdf

[2411.08019] Language Models as Causal Effect Generators

[2012.03826] HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation

Taking a responsible path to AGI - Google DeepMind

[2502.01706] Comply: Learning Sentences with Complex Weights inspired by Fruit Fly Olfaction

[2503.20511] From reductionism to realism: Holistic mathematical modelling for complex biological systems

[1412.6980] Adam: A Method for Stochastic Optimization

Learning with not Enough Data Part 1: Semi-Supervised Learning | Lil'Log

MLOps system design is boring. - by Alexandru Vesa

[1810.04805] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding


Demystifying Chains, Trees, and Graphs of Thoughts

Sequential decision making - Kevin Murphy, DeepMind

Strategic Foundation Models - Large_Language_Models__Foundation_Models_and_Game_Theory___Research_Manifesto (16).pdf

Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch

International AI safety report - International_AI_Safety_Report_2025_accessible_f.pdf

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

7B Model and 8K Examples: Emerging Reasoning with Reinforcement Learning is Both Effective and Efficient

A Little Bit of Reinforcement Learning from Human Feedback

DeepSeek R1's recipe to replicate o1 and the future of reasoning LMs

A Mathematical Framework for Transformer Circuits

A Recipe for Training Neural Networks

Scaling and networking a modular photonic quantum computer | Nature

Large Language Diffusion Models

KindXiaoming/grow-crystals: Getting crystal-like representations with harmonic loss

Harmonic Loss Trains Interpretable AI Models

[2309.16177] Navigating the Noise: Bringing Clarity to ML Parameterization Design with O(100) Ensembles