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)

Emergent World Models and Latent Variable Estimation in Chess-Playing Language Models. - Adam Karvonen (CoRR, 2024)

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

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)

Revisiting End-To-End Sparse Autoencoder Training: A Short Finetune Is All You Need. - Adam Karvonen (CoRR, 2025)

Learning Multi-Level Features with Matryoshka Sparse Autoencoders. - Bart Bussmann; Noa Nabeshima; Adam Karvonen; Neel Nanda (CoRR, 2025)

Robustly Improving LLM Fairness in Realistic Settings via Interpretability. - Adam Karvonen; Samuel Marks (CoRR, 2025)

Steering Out-of-Distribution Generalization with Concept Ablation Fine-Tuning. - Helena Casademunt; Caden Juang; Adam Karvonen; Samuel Marks; Senthooran Rajamanoharan; Neel Nanda (CoRR, 2025)