bilalchughtai

Authors: Joshua Engels*, Callum McDougall*, Bilal Chughtai*, Janos Kramar, Senthoran Rajamanoharan, Cindy Wu, Arthur Conmy, Asic Q Chen, Jean Tarbouriech, Min Ma, Brendan O'Donoghue+, João Gabriel Lopes de Oliveira+, Rohin Shah+, Neel Nanda+ *Primary Contributor +Advising Paper here: https://arxiv.org/abs/2606.20560 Overview In a recent collaboration between the GDM interpretability team and...

This is the third in a series of informal research updates from the Google DeepMind Language Model Interpretability team, in interpretability and adjacent areas. The second post can be found here. In this short post, we describe a surprising finding: most safety relevant properties in Gemini seem to be caused...

This is the second in a series of informal research updates from the Google DeepMind Language Model Interpretability team, in interpretability and adjacent areas. The first post can be found here. TL;DR * It is possible to build extremely simple agents that reliably find interesting behavioural differences between distinct models....

Authors: Reilly Haskins*, Bilal Chughtai**, Joshua Engels** * primary contributor ** advice and mentorship This is the updated version of our earlier preliminary results post, covering the final results from our paper. The paper extends our preliminary work to eight models, a harder agentic task, CoT controllability analysis, and RL...

Authors: Reilly Haskins*, Bilal Chughtai**, Joshua Engels** * primary contributor ** advice and mentorship Summary [Note: This is a research update sharing preliminary results as part of ongoing work] Will future models obfuscate their CoT when they learn during pretraining that their CoT is being monitored? We investigate this question...

New research from the GDM mechanistic interpretability team. Read the full paper on arxiv or check out the twitter thread. > Abstract > Building reliable deception detectors for AI systems—methods that could predict when an AI system is being strategically deceptive without necessarily requiring behavioural evidence—would be valuable in mitigating...

Executive Summary * Over the past year, the Google DeepMind mechanistic interpretability team has pivoted to a pragmatic approach to interpretability, as detailed in our accompanying post [1] , and are excited for more in the field to embrace pragmatism! In brief, we think that: * It is crucial to...