Alek Westover

TL;DR: We previously motivated two complementary ways to use distillation for AI safety; we’re releasing a paper empirically testing those ideas. Distillation for Incrimination (DFI) distills an untrusted model into a weaker, trusted one, aiming to transfer the teacher's misalignment but not its ability to hide it, so that auditing...

Authors: Kyle O’Brien¹, Spencer Kitts², Cameron Tice¹, Alek Westover² ¹Geodesic Research, ²Redwood Research Figure 1: We pretrained LLMs from scratch with and without filtering subversion-relevant information, finding that subversion-relevant knowledge is significantly diminished while retaining general ML knowledge. TLDR: Filtering subversion-relevant information — subversion strategies, information about subversion defences, and...

Edited 2026-09-29; see note at the start of the conclusion. Summary: Currently, “chain of thought” (CoT) is our most valuable tool for understanding the reasoning and cognition of AI systems. However, some architectures would enable AI models to reason much more extensively in latent states rather than in text CoT....

We use RL to teach a deep recurrent model and a normal CoT model to solve a math problem while hiding from a CoT monitor which of two possible problems it is solving. The deep recurrent model moves its reasoning into latents (thereby evading the CoT monitor) within 40 steps...

Here, we’ve given an operationalization of opaque serial depth, a metric designed to be a good (albeit imperfect) proxy for the amount of opaque serial cognition that models can perform. While maintaining low opaque serial depth is helpful for preserving oversight of models, it doesn’t guarantee this. In this post...

We measure GPT-6-Astra's capabilities when its prompt is padded with a variable number of meaningless “filler” tokens (e.g., dots) and it is told to answer immediately[1] without reasoning. On tasks designed to require lots of serial cognition, Astra performs significantly better with filler tokens than without (e.g., improving from ~10-20%[2]...

Currently, chain-of-thought (CoT) is a valuable tool for overseeing AI models. However, some architectural shifts could significantly reduce CoT monitorability. We have recently proposed that AI companies should transparently share information about the degree to which their architectures may allow for latent reasoning and communication. To assist with this proposal,...