Why do LLMs struggle so much with recovering from early wrong turns in multi-turn conversations — even when all prior context is available and tokenized?

Is it due to the model's training distribution (mostly single-shot completions), the way context windows are encoded, or an architectural bottleneck?

Feels like there's no dynamic internal state that evolves over the conversation — only a repeated re-parsing of static history. Has anyone seen work on integrating memory/state mechanisms that allow belief revision within a session, not just regurgitation of past tokens?