Tegmark, Max

37 publications

ICML 2025 Are Sparse Autoencoders Useful? a Case Study in Sparse Probing Subhash Kantamneni, Joshua Engels, Senthooran Rajamanoharan, Max Tegmark, Neel Nanda

TMLR 2025 DafnyBench: A Benchmark for Formal Software Verification Chloe R Loughridge, Qinyi Sun, Seth Ahrenbach, Federico Cassano, Chuyue Sun, Ying Sheng, Anish Mudide, Md Rakib Hossain Misu, Nada Amin, Max Tegmark

TMLR 2025 Decomposing the Dark Matter of Sparse Autoencoders Joshua Engels, Logan Riggs Smith, Max Tegmark

NeurIPS 2025 Dense SAE Latents Are Features, Not Bugs Xiaoqing Sun, Alessandro Stolfo, Joshua Engels, Ben Peng Wu, Senthooran Rajamanoharan, Mrinmaya Sachan, Max Tegmark

ICLR 2025 Efficient Dictionary Learning with Switch Sparse Autoencoders Anish Mudide, Joshua Engels, Eric J Michaud, Max Tegmark, Christian Schroeder de Witt

TMLR 2025 Harmonic Loss Trains Interpretable AI Models David D. Baek, Ziming Liu, Riya Tyagi, Max Tegmark

ICLRW 2025 High Frequency Latents Are Features, Not Bugs Xiaoqing Sun, Joshua Engels, Max Tegmark

ICLR 2025 KAN: Kolmogorov–Arnold Networks Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark

ICLRW 2025 Language Models Use Trigonometry to Do Addition Subhash Kantamneni, Max Tegmark

ICML 2025 Low-Rank Adapting Models for Sparse Autoencoders Matthew Chen, Joshua Engels, Max Tegmark

ICLRW 2025 Low-Rank Adapting Models for Sparse Autoencoders Matthew Chen, Joshua Engels, Max Tegmark

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

NeurIPS 2025 On the Creation of Narrow AI: Hierarchy and Nonlocality of Neural Network Skills Eric J Michaud, Asher Parker-Sartori, Max Tegmark

TMLR 2025 Open Problems in Mechanistic Interpretability Lee Sharkey, Bilal Chughtai, Joshua Batson, Jack Lindsey, Jeffrey Wu, Lucius Bushnaq, Nicholas Goldowsky-Dill, Stefan Heimersheim, Alejandro Ortega, Joseph Isaac Bloom, Stella Biderman, Adrià Garriga-Alonso, Arthur Conmy, Neel Nanda, Jessica Mary Rumbelow, Martin Wattenberg, Nandi Schoots, Joseph Miller, William Saunders, Eric J Michaud, Stephen Casper, Max Tegmark, David Bau, Eric Todd, Atticus Geiger, Mor Geva, Jesse Hoogland, Daniel Murfet, Thomas McGrath

NeurIPS 2025 Remarkable Robustness of LLMs: Stages of Inference? Vedang Lad, Jin Hwa Lee, Wes Gurnee, Max Tegmark

NeurIPS 2025 Scaling Laws for Scalable Oversight Joshua Engels, David D. Baek, Subhash Kantamneni, Max Tegmark

ICLRW 2025 Towards Understanding Distilled Reasoning Models: A Representational Approach David D. Baek, Max Tegmark

NeurIPSW 2024 DafnyBench: A Benchmark for Formal Software Verification Chloe R Loughridge, Qinyi Sun, Seth Ahrenbach, Federico Cassano, Chuyue Sun, Ying Sheng, Anish Mudide, Md Rakib Hossain Misu, Nada Amin, Max Tegmark

ICMLW 2024 How Do Transformers "Do" Physics? Investigating the Simple Harmonic Oscillator Subhash Kantamneni, Ziming Liu, Max Tegmark

ICLR 2024 Language Models Represent Space and Time Wes Gurnee, Max Tegmark

ICMLW 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

ICMLW 2024 The Remarkable Robustness of LLMs: Stages of Inference? Vedang Lad, Wes Gurnee, Max Tegmark

NeurIPSW 2023 Grokking as Simplification: A Nonlinear Complexity Perspective Ziming Liu, Ziqian Zhong, Max Tegmark

NeurIPSW 2023 Growing Brains in Recurrent Neural Networks for Multiple Cognitive Tasks Ziming Liu, Mikail Khona, Ila Fiete, Max Tegmark

NeurIPSW 2023 Growing Brains: Co-Emergence of Anatomical and Functional Modularity in Recurrent Neural Networks Ziming Liu, Mikail Khona, Ila R Fiete, Max Tegmark

ICLR 2023 Omnigrok: Grokking Beyond Algorithmic Data Ziming Liu, Eric J Michaud, Max Tegmark

ICML 2023 PFGM++: Unlocking the Potential of Physics-Inspired Generative Models Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi Jaakkola

NeurIPS 2023 The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas

NeurIPS 2023 The Quantization Model of Neural Scaling Eric Michaud, Ziming Liu, Uzay Girit, Max Tegmark

NeurIPS 2022 Poisson Flow Generative Models Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola

NeurIPS 2022 Towards Understanding Grokking: An Effective Theory of Representation Learning Ziming Liu, Ouail Kitouni, Niklas S Nolte, Eric Michaud, Max Tegmark, Mike Williams

NeurIPSW 2021 Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning Ziming Liu, Yuanqi Du, Yunyue Chen, Max Tegmark

NeurIPS 2020 AI Feynman 2.0: Pareto-Optimal Symbolic Regression Exploiting Graph Modularity Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark

UAI 2019 Learnability for the Information Bottleneck Tailin Wu, Ian Fischer, Isaac L. Chuang, Max Tegmark

ICLRW 2019 Learnability for the Information Bottleneck Tailin Wu, Ian Fischer, Isaac Chuang, Max Tegmark

ICLR 2018 The Power of Deeper Networks for Expressing Natural Functions David Rolnick, Max Tegmark

ICML 2017 Tunable Efficient Unitary Neural Networks (EUNN) and Their Application to RNNs Li Jing, Yichen Shen, Tena Dubcek, John Peurifoy, Scott Skirlo, Yann LeCun, Max Tegmark, Marin Soljačić