Towards embedded evaluations for scheming propensities
Dylan Bowman
Abstract
TL;DR: A safety case for scheming must be able to substantiate these four key claims: scheming was not incentivized during training, strong evaluations and red teaming suggest that the model does not have a propensity to scheme, scheming was never attempted during internal deployment, and scheming reasoning would be detected (e.g., via CoT monitoring). All four of these claims should be addressed by embedded evaluators with persistent, deep access. Introduction A scheming AI is one that covertly and strategically pursues goals its developers didn’t intend. It might hide and protect its true capabilities and objectives from the people and processes meant to align, monitor, and control it. In 2024, Apollo Research demonstrated that frontier models were capable of scheming in controlled settings, and in 2025 we collaborated with OpenAI to determine how well their alignment techniques mitigate scheming . Models must not be trained to scheme against humans, i.e. covertly and strategically pursue misaligned goals. Evaluations should find no propensity to scheme, models should never attempt to scheme in deployment, and their reasoning must remain legible so that scheming would be noticed