The Model That Forgot Itself: Why LLMs Drift Without Knowing
A chatbot can say the right thing for ten turns and still forget what it was trying to do. That is the uncomfortable idea behind Probing the Lack of Stable Internal Beliefs in LLMs, a paper that studies whether large language models can maintain an unstated goal across a multi-turn interaction.1 The paper is not asking whether a model can avoid obvious contradictions. That is the familiar version of consistency: did the assistant say one thing on Monday and the opposite thing on Tuesday? ...