Can Transformers Learn to Solve Problems Recursively? - Shizhuo Dylan Zhang; Curt Tigges; Stella Biderman; Maxim Raginsky; Talia Ringer (CoRR, 2023)

Linear Representations of Sentiment in Large Language Models. - Curt Tigges; Oskar John Hollinsworth; Atticus Geiger; Neel Nanda (CoRR, 2023)

LLM Circuit Analyses Are Consistent Across Training and Scale. - Curt Tigges; Michael Hanna 001; Qinan Yu; Stella Biderman (NeurIPS, 2024)

Transformer-Based Models Are Not Yet Perfect At Learning to Emulate Structural Recursion. - Shizhuo Dylan Zhang; Curt Tigges; Zory Zhang; Stella Biderman; Maxim Raginsky; Talia Ringer (Trans. Mach. Learn. Res., 2024)

Sparse Autoencoders Do Not Find Canonical Units of Analysis. - Patrick Leask; Bart Bussmann; Michael T. Pearce; Joseph Isaac Bloom; Curt Tigges; Noura Al Moubayed; Lee Sharkey; Neel Nanda (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)