2 citations · 2 across the 1 of their papers we have counts for
3 papers
Transformers Use Causal World Models in Maze-Solving Tasks
Alex F. Spies, William Edwards, Michael I. Ivanitskiy +5
Recent studies in interpretability have explored the inner workings of transformer models trained on tasks across various domains, often discovering that these networks naturally d…
Structured World Representations in Maze-Solving Transformers
Michael Igorevich Ivanitskiy, Alex F. Spies, Tilman Räuker +9
Transformer models underpin many recent advances in practical machine learning applications, yet understanding their internal behavior continues to elude researchers. Given the siz…
A Configurable Library for Generating and Manipulating Maze Datasets
Michael Igorevich Ivanitskiy, Rusheb Shah, Alex F. Spies +8
Understanding how machine learning models respond to distributional shifts is a key research challenge. Mazes serve as an excellent testbed due to varied generation algorithms offe…