Variable Selection for Chronic Disease Outcome Prediction Using a Causal Inference Technique: A Preliminary Study. - John Richard Lee; Bilal Chughtai; Rema Padman (ICHI, 2018)

A Toy Model of Universality: Reverse Engineering how Networks Learn Group Operations. - Bilal Chughtai; Lawrence Chan; Neel Nanda (ICML, 2023)

Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs. - Rudolf Laine; Bilal Chughtai; Jan Betley; Kaivalya Hariharan; Mikita Balesni; Jérémy Scheurer; Marius Hobbhahn; Alexander Meinke; Owain Evans (NeurIPS, 2024)

Summing Up the Facts: Additive Mechanisms Behind Factual Recall in LLMs. - Bilal Chughtai; Alan Cooney; Neel Nanda (CoRR, 2024)

Can Language Models Explain Their Own Classification Behavior? - Dane Sherburn; Bilal Chughtai; Owain Evans (CoRR, 2024)

Transformer Circuit Faithfulness Metrics are not Robust. - Joseph Miller; Bilal Chughtai; William Saunders (CoRR, 2024)

Towards evaluations-based safety cases for AI scheming. - Mikita Balesni; Marius Hobbhahn; David Lindner; Alexander Meinke; Tomek Korbak; Joshua Clymer; Buck Shlegeris; Jérémy Scheurer; Charlotte Stix; Rusheb Shah; Nicholas Goldowsky-Dill; Dan Braun; et al. (CoRR, 2024)

Model Tampering Attacks Enable More Rigorous Evaluations of LLM Capabilities. - Zora Che; Stephen Casper; Robert Kirk; Anirudh Satheesh; Stewart Slocum; Lev E. McKinney; Rohit Gandikota; Aidan Ewart; Domenic Rosati; Zichu Wu; Zikui Cai; Bilal Chughtai; et al. (Trans. Mach. Learn. Res., 2025)

Open Problems in Mechanistic Interpretability. - Lee Sharkey; Bilal Chughtai; Joshua Batson; Jack Lindsey; Jeff Wu 003; Lucius Bushnaq; Nicholas Goldowsky-Dill; Stefan Heimersheim; Alejandro Ortega; Joseph Isaac Bloom; Stella Biderman; Adrià Garriga-Alonso; et al. (CoRR, 2025)

Detecting Strategic Deception Using Linear Probes. - Nicholas Goldowsky-Dill; Bilal Chughtai; Stefan Heimersheim; Marius Hobbhahn (CoRR, 2025)