I’ve noticed that large language models (like GPT-style or Claude models) often perform well on individual reasoning steps, but struggle to maintain consistency across longer multi-step tasks. For example, in tasks involving planning, logical chains, or iterative refinement, the model may produce correct intermediate reasoning but still arrive at inconsistent or incorrect final conclusions. Is this primarily due to: context window limitations,...
Originally asked by Avalon Brooks on Stack Exchange (ai) (CC BY-SA 4.0).
