Researchers have published a paper introducing task-level autoregressive reasoning as a method to bridge what they call the 'know-act gap' in AI systems — the persistent disconnect between what models know and what they can reliably execute. The work proposes a structured reasoning architecture that operates at the task level rather than the token level, aiming to improve agent consistency and decision-making in complex, multi-step environments.