> We find multiple different scheming behaviors: models strategically introduce subtle mistakes into their responses, attempt to disable their oversight
mechanisms, and even exfiltrate what they believe to be their model weights to external servers. Additionally, this deceptive behavior proves persistent.<p>Interesting work on attempting to quantify misalignment. Keep in mind:<p>> Our evaluation scenarios are purposefully simplistic because we want to catch the
earliest signs of scheming capabilities. This implies that the scenarios are quite toy and not representative of real deployments. For example, in our scenarios, situational awareness information (e.g., knowing about its goal misalignment and its supervision mechanisms) is provided very explicitly.<p>Related blog post: <a href="https://www.apolloresearch.ai/research/scheming-reasoning-evaluations" rel="nofollow">https://www.apolloresearch.ai/research/scheming-reasoning-ev...</a>