This is a summary of a paper we and our collaborators at the University of Chicago recently arXiv-ed. tl;dr: We seed models with some property (e.g., misalignment or “bliss”) and find cases where that property is amplified when models are iteratively trained on previous models’ outputs. However, this phenomenon is...
This project was conducted as part of the SPAR Fall 2025 cohort. TL;DR * Chain-of-thought (CoT) monitoring may serve as a core pillar for AI safety if further advancements in AI capabilities do not significantly degrade the monitorability of LLM serial reasoning. * As such, we studied the effects of...
This work was supported by UChicago XLab. Today, we are announcing our first major release of the XLab AI Security Guide: a set of online resources and coding exercises covering canonical papers on jailbreaks, fine-tuning attacks, and proposed methods to defend AI systems from misuse. Each page on the course...