Linearly Structured World Representations in Maze-Solving Transformers. - Michael I. Ivanitskiy; Alex F. Spies; Tilman Räuker; Guillaume Corlouer; Chris Mathwin; Lucia Quirke; Can Rager; Rusheb Shah; Dan Valentine; Cecilia G. Diniz Behn; Katsumi Inoue; Samy Wu Fung (UniReps, 2023)

Safety of self-assembled neuromorphic hardware. - Can Rager; Kyle Webster (CoRR, 2023)

A Configurable Library for Generating and Manipulating Maze Datasets. - Michael Igorevich Ivanitskiy; Rusheb Shah; Alex F. Spies; Tilman Räuker; Dan Valentine; Can Rager; Lucia Quirke; Chris Mathwin; Guillaume Corlouer; Cecilia G. Diniz Behn; Samy Wu Fung (CoRR, 2023)

An Adversarial Example for Direct Logit Attribution: Memory Management in gelu-4l. - James Dao; Yeu-Tong Lau; Can Rager; Jett Janiak (CoRR, 2023)

Attribution Patching Outperforms Automated Circuit Discovery. - Aaquib Syed; Can Rager; Arthur Conmy (CoRR, 2023)

Structured World Representations in Maze-Solving Transformers. - Michael I. Ivanitskiy; Alex F. Spies; Tilman Räuker; Guillaume Corlouer; Chris Mathwin; Lucia Quirke; Can Rager; Rusheb Shah; Dan Valentine; Cecilia G. Diniz Behn; Katsumi Inoue; Samy Wu Fung (CoRR, 2023)

Measuring Progress in Dictionary Learning for Language Model Interpretability with Board Game Models. - Adam Karvonen; Benjamin Wright; Can Rager; Rico Angell; Jannik Brinkmann; Logan Smith; Claudio Mayrink Verdun; David Bau; Samuel Marks (NeurIPS, 2024)

NNsight and NDIF: Democratizing Access to Foundation Model Internals. - Jaden Fiotto-Kaufman; Alexander R. Loftus; Eric Todd; Jannik Brinkmann; Caden Juang; Koyena Pal; Can Rager; Aaron Mueller; Samuel Marks; Arnab Sen Sharma; Francesca Lucchetti; Michael Ripa; et al. (CoRR, 2024)

The Quest for the Right Mediator: A History, Survey, and Theoretical Grounding of Causal Interpretability. - Aaron Mueller; Jannik Brinkmann; Millicent L. Li; Samuel Marks; Koyena Pal; Nikhil Prakash; Can Rager; Aruna Sankaranarayanan; Arnab Sen Sharma; Jiuding Sun; Eric Todd; David Bau; et al. (CoRR, 2024)

Evaluating Sparse Autoencoders on Targeted Concept Erasure Tasks. - Adam Karvonen; Can Rager; Samuel Marks; Neel Nanda (CoRR, 2024)

NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals. - Jaden Fried Fiotto-Kaufman; Alexander Russell Loftus; Eric Todd; Jannik Brinkmann; Koyena Pal; Dmitrii Troitskii; Michael Ripa; Adam Belfki; Can Rager; Caden Juang; Aaron Mueller; Samuel Marks; et al. (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)

SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability. - Adam Karvonen; Can Rager; Johnny Lin; Curt Tigges; Joseph Isaac Bloom; David Chanin; Yeu-Tong Lau; Eoin Farrell; Callum McDougall; Kola Ayonrinde; Matthew Wearden; Arthur Conmy; et al. (CoRR, 2025)

Discovering Forbidden Topics in Language Models. - Can Rager; Chris Wendler; Rohit Gandikota; David Bau (CoRR, 2025)