Examining the causal structures of deep neural networks using information theory. - Simon Mattsson; Eric J. Michaud; Erik Hoel (CoRR, 2020)

Understanding Learned Reward Functions. - Eric J. Michaud; Adam Gleave; Stuart Russell 001 (CoRR, 2020)

Examining the Causal Structures of Deep Neural Networks Using Information Theory. - Scythia Marrow; Eric J. Michaud; Erik Hoel (Entropy, 2020)

Towards Understanding Grokking: An Effective Theory of Representation Learning. - Ziming Liu; Ouail Kitouni; Niklas Nolte; Eric J. Michaud; Max Tegmark; Mike Williams (NeurIPS, 2022)

Omnigrok: Grokking Beyond Algorithmic Data. - Ziming Liu; Eric J. Michaud; Max Tegmark (ICLR, 2023)

The Quantization Model of Neural Scaling. - Eric J. Michaud; Ziming Liu; Uzay Girit; Max Tegmark (NeurIPS, 2023)

Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback. - Stephen Casper; Xander Davies; Claudia Shi; Thomas Krendl Gilbert; Jérémy Scheurer; Javier Rando; Rachel Freedman; Tomasz Korbak; David Lindner; Pedro Freire; Tony Tong Wang; Samuel Marks; et al. (Trans. Mach. Learn. Res., 2023)

Precision Machine Learning. - Eric J. Michaud; Ziming Liu; Max Tegmark (Entropy, 2023)

Opening the AI black box: program synthesis via mechanistic interpretability. - Eric J. Michaud; Isaac Liao; Vedang Lad; Ziming Liu; Anish Mudide; Chloe Loughridge; Zifan Carl Guo; Tara Rezaei Kheirkhah; Mateja Vukelic; Max Tegmark (CoRR, 2024)

Not All Language Model Features Are Linear. - Joshua Engels; Isaac Liao; Eric J. Michaud; Wes Gurnee; Max Tegmark (CoRR, 2024)

Survival of the Fittest Representation: A Case Study with Modular Addition. - Xiaoman Delores Ding; Zifan Carl Guo; Eric J. Michaud; Ziming Liu; Max Tegmark (CoRR, 2024)

The Geometry of Concepts: Sparse Autoencoder Feature Structure. - Yuxiao Li; Eric J. Michaud; David D. Baek; Joshua Engels; Xiaoqing Sun; Max Tegmark (CoRR, 2024)

Not All Language Model Features Are One-Dimensionally Linear. - Joshua Engels; Eric J. Michaud; Isaac Liao; Wes Gurnee; Max Tegmark (ICLR, 2025)

Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models. - Samuel Marks; Can Rager; Eric J. Michaud; Yonatan Belinkov; David Bau; Aaron Mueller (ICLR, 2025)

Efficient Dictionary Learning with Switch Sparse Autoencoders. - Anish Mudide; Joshua Engels; Eric J. Michaud; Max Tegmark; Christian Schröder de Witt (ICLR, 2025)

Physics of Skill Learning. - Ziming Liu; Yizhou Liu 001; Eric J. Michaud; Jeff Gore; Max Tegmark (CoRR, 2025)

Open Problems in Mechanistic Interpretability. - Lee Sharkey; Bilal Chughtai; Joshua Batson; Jack Lindsey; Jeff Wu 003; Lucius Bushnaq; Nicholas Goldowsky-Dill; Stefan Heimersheim; Alejandro Ortega; Joseph Isaac Bloom; Stella Biderman; Adrià Garriga-Alonso; et al. (CoRR, 2025)

On the creation of narrow AI: hierarchy and nonlocality of neural network skills. - Eric J. Michaud; Asher Parker-Sartori; Max Tegmark (CoRR, 2025)