Part 2/11:
Current large language models exhibit impressive proficiency in generating human-like responses, but they often struggle with recognizing and fixing their own errors. Unlike humans, who can debug their code or double-check their math, most AI models lack the built-in mechanisms to dynamically identify inaccuracies and revise their outputs effectively.
This deficiency becomes especially problematic in multi-step reasoning tasks. A single error early in a process can cascade into an entirely incorrect final outcome. Traditional solutions—such as prompt-based tweaks after the fact or multiple attempts—are inconsistent, resource-intensive, and often insufficient when faced with complex problems that require layered reasoning.