Risks from Learned Optimization in Advanced Machine Learning Systems. - Evan Hubinger; Chris van Merwijk; Vladimir Mikulik; Joar Skalse; Scott Garrabrant (CoRR, 2019)

Neural networks are a priori biased towards Boolean functions with low entropy. - Chris Mingard; Joar Skalse; Guillermo Valle Pérez; David Martínez-Rubio; Vladimir Mikulik; Ard A. Louis (CoRR, 2019)

Meta-trained agents implement Bayes-optimal agents. - Vladimir Mikulik; Grégoire Delétang; Tom McGrath; Tim Genewein; Miljan Martic; Shane Legg; Pedro A. Ortega (NeurIPS, 2020)

Algorithms for Causal Reasoning in Probability Trees. - Tim Genewein; Tom McGrath; Grégoire Delétang; Vladimir Mikulik; Miljan Martic; Shane Legg; Pedro A. Ortega (CoRR, 2020)

Causal Analysis of Agent Behavior for AI Safety. - Grégoire Delétang; Jordi Grau-Moya; Miljan Martic; Tim Genewein; Tom McGrath; Vladimir Mikulik; Markus Kunesch; Shane Legg; Pedro A. Ortega (CoRR, 2021)

Alignment of Language Agents. - Zachary Kenton; Tom Everitt; Laura Weidinger; Iason Gabriel; Vladimir Mikulik; Geoffrey Irving (CoRR, 2021)

Scaling Language Models: Methods, Analysis & Insights from Training Gopher. - Jack W. Rae; Sebastian Borgeaud; Trevor Cai; Katie Millican; Jordan Hoffmann; H. Francis Song; John Aslanides; Sarah Henderson; Roman Ring; Susannah Young; Eliza Rutherford; Tom Hennigan; et al. (CoRR, 2021)

Teaching language models to support answers with verified quotes. - Jacob Menick; Maja Trebacz; Vladimir Mikulik; John Aslanides; H. Francis Song; Martin J. Chadwick; Mia Glaese; Susannah Young; Lucy Campbell-Gillingham; Geoffrey Irving; Nat McAleese (CoRR, 2022)

Tracr: Compiled Transformers as a Laboratory for Interpretability. - David Lindner; János Kramár; Sebastian Farquhar; Matthew Rahtz; Tom McGrath; Vladimir Mikulik (NeurIPS, 2023)

Tracr: Compiled Transformers as a Laboratory for Interpretability. - David Lindner; János Kramár; Matthew Rahtz; Thomas McGrath; Vladimir Mikulik (CoRR, 2023)

Does Circuit Analysis Interpretability Scale? Evidence from Multiple Choice Capabilities in Chinchilla. - Tom Lieberum; Matthew Rahtz; János Kramár; Neel Nanda; Geoffrey Irving; Rohin Shah; Vladimir Mikulik (CoRR, 2023)

The Hydra Effect: Emergent Self-repair in Language Model Computations. - Thomas McGrath; Matthew Rahtz; János Kramár; Vladimir Mikulik; Shane Legg (CoRR, 2023)

Challenges with unsupervised LLM knowledge discovery. - Sebastian Farquhar; Vikrant Varma; Zachary Kenton; Johannes Gasteiger; Vladimir Mikulik; Rohin Shah (CoRR, 2023)

An Approach to Technical AGI Safety and Security. - Rohin Shah; Alex Irpan; Alexander Matt Turner; Anna Wang; Arthur Conmy; David Lindner; Jonah Brown-Cohen; Lewis Ho; Neel Nanda; Raluca Ada Popa; Rishub Jain; Rory Greig; et al. (CoRR, 2025)

Reasoning Models Don't Always Say What They Think. - Yanda Chen; Joe Benton; Ansh Radhakrishnan; Jonathan Uesato; Carson Denison; John Schulman; Arushi Somani; Peter Hase; Misha Wagner; Fabien Roger; Vladimir Mikulik; Samuel R. Bowman; et al. (CoRR, 2025)

Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety. - Tomek Korbak; Mikita Balesni; Elizabeth Barnes; Yoshua Bengio; Joe Benton; Joseph Bloom; Mark Chen 003; Alan Cooney; Allan Dafoe; Anca D. Dragan; Scott Emmons; Owain Evans; et al. (CoRR, 2025)