(pinned section)
Categorization is baked into the brain, Barrett & Miller, 2026
Reasoning Models Generate Societies of Thought, Kim et al., Google (2026)
- the Google team’s github: paradigms-of-intelligence repositories
Improving the Effectiveness of Potential-Based Reward Shaping in Reinforcement Learning
[1802.06070] Diversity is All You Need: Learning Skills without a Reward Function
Fundamentals - build/cement understanding
- Stanford CS25: V2 I Introduction to Transformers w/ Andrej Karpathy - YouTube
- bengio03a.dvi - bengio03a.pdf
- 1409.3215 Sequence to Sequence Learning with Neural Networks
- 1409.0473 Neural Machine Translation by Jointly Learning to Align and Translate
- CS 230 - Recurrent Neural Networks Cheatsheet
- A Visual Guide to Attention Variants in Modern LLMs
Better gradient descent:
- Understanding Optimization in Deep Learning with Central Flows
- First-Order Optimization Algorithms for Machine Learning - Gradient Descent Progress Bound - S1.pdf lecture3.pdf
- Gradient Descent as Loss Landscape Navigation: a Normative Framework for Deriving Learning Rules
AI agents: collective intelligence, evolutionary frameworks, biology, physics, complex systems / emergent phenomena
Renormalization and effective lagrangians - ScienceDirect
Undecidability of the spectral gap | Nature
BICAN: A cell census of the developing human brain
Patterns, Predictions, and Actions (2025) - by Ben Recht
Active Inference: The Free Energy Principle in Mind, Brain, and Behavior | Books Gateway | MIT Press
- Active Inference - book_9780262369978.pdf
- concepts:
- predictive processing: brains act as approximately Bayesian prediction machines
- active inference: tools of state evaluation (sensors + processing) are the same tools that enable state change (agency / action)
- naive interpretation:
- predictive loss depends on a real-world objective (reality)
- active inference depends on model-internal objective (agent goals)
[2510.20817] KL-Regularized Reinforcement Learning is Designed to Mode Collapse
I Figured Out How to Engineer Emergence - by Erik Hoel
A Retrospective on Active Inference
- [2006.10524] Reinforcement Learning as Iterative and Amortised Inference
- [2006.12964] On the Relationship Between Active Inference and Control as Inference
- [2007.05838] Control as Hybrid Inference
- [2103.06859] Understanding the Origin of Information-Seeking Exploration in Probabilistic Objectives for Control
Variational inference - Princeton cos597C 2011
Hot take: The end of mech interp as we know it? A Pragmatic Vision for Interpretability — AI Alignment Forum
Collective Intelligence with LLMs - by CIP
[2502.01706] Comply: Learning Sentences with Complex Weights inspired by Fruit Fly Olfaction
AI - mix
[2510.21890] The Principles of Diffusion Models
[2510.25781] A Practitioner's Guide to Kolmogorov-Arnold Networks
The Actuary's Final Word - by Ben Recht - arg min
Severity: Strong vs Weak | Error Statistics Philosophy
Stephen Shenker: Chaos, Black Holes, and Quantum Mechanics - YouTube
Building and evaluating alignment auditing agents
[2502.01492] Develop AI Agents for System Engineering in Factorio
Common Elements of Frontier AI Safety Policies
[2507.20964] Core Safety Values for Provably Corrigible Agents
Introduction to deep learning with applications to stochastic control and games - YouTube
Learning the natural history of human disease with generative transformers | Nature
Claude Code: Behind-the-scenes of the master agent loop
- core numerical methods derived from an exact theoretical principle
- contrast with popular optimizers like Adam, which have more heuristic origins
David Ha’s early work: ōtoro.net
Molnar: From Frequencies to Coverage: Rethinking What “Representative” Means
Molnar: Don’t fix your imbalanced data
- [lcamtuf: Minkowski dimension](How many dimensions is this? - lcamtuf’s thing)
GPT-oss from the Ground Up - by Cameron R. Wolfe, Ph.D.
Gemma 3 270M: Can Tiny Models Learn New Tasks?
Building CERN for AI - An institutional blueprint - Centre for Future Generations
Process knowledge is crucial to economic development
The Big LLM Architecture Comparison
On the criteria to be used in decomposing systems into modules - 361598.361623.pdf
Aurora GPT - Argonne National Lab
[2401.17173] Zero-Shot Reinforcement Learning via Function Encoders
Severe deviation in protein fold prediction by advanced AI: a case study | Scientific Reports
Learning with not Enough Data Part 1: Semi-Supervised Learning | Lil'Log
Demystifying Chains, Trees, and Graphs of Thoughts
Sequential decision making - Kevin Murphy, DeepMind
Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch
A Recipe for Training Neural Networks
AI benchmarks and evals
Reading today's open-closed performance gap, Nathan Lambert
OII | Study identifies weaknesses in how AI systems are evaluated
AI and the Everything in the Whole Wide World Benchmark
Evaluation Framework for AI Systems in "the Wild" | alphaXiv
splattered with iffy claims but also some directionally-interesting commentary: AI is Hitting a Measurement Wall - by Devansh
AI safety & alignment
International AI safety report - International_AI_Safety_Report_2025_accessible_f.pdf
How to Make the Future Better: Concrete Actions for Flourishing
[2503.05336v3] Toward an Evaluation Science for Generative AI Systems
Guillotine: Hypervisors for Isolating Malicious AIs - guillotine.pdf
The Artificiality of Alignment - by jessica dai - Reboot
Mech interp
Tracing the thoughts of a large language model \ Anthropic
Sparsify: A mechanistic interpretability research agenda — AI Alignment Forum
Evergreen re-reads
The Feynman Lectures on Physics
[2502.15657] Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?
Physics of Language Models - Allen Zhu
Bertsekas - RL courses and book - mit.edu/~dimitrib/RLbook.html
Stanford CS336 | Language Modeling from Scratch
Introduction | RLHF Book by Nathan Lambert
A Little Bit of Reinforcement Learning from Human Feedback
Causal Artificial Intelligence Book
[1807.02811] A Tutorial on Bayesian Optimization
CSES - CSES Problem Set - Tasks
There is only one model - by Jack Morris - Token for Token
Jared Kaplan: ContemporaryMLforPhysicists (pdf)
- Starting on page 55, the Architectures section covers the structure and componentes of deep neural networks from a (mathematical and statistical) modeling perspective.
Tips for Empirical Alignment Research — AI Alignment Forum