Correct So Far, But Where Next? Diagnosing Next-Step Failures in Multi-Step Reasoning

Chain-of-thought reasoning improves multi-step problem solving by decomposing a difficult computation into simpler steps, but decomposition only helps if the steps are composed correctly. When a reasoning chain first goes wrong after a sequence of correct steps, existing evaluations do not distinguish whether the model chose the wrong computation to perform next or chose the right one and executed it incorrectly. Meanwhile, verified, truncated, or relayed reasoning traces are reused in practice on the assumption that a correct intermediate result is a state from which the computation can continue. We show that this assumption can fail even when every supplied step is correct and the remaining computation is well within the model's ability. Across controlled serial tasks with a unique correct next step, models often preserve the correct intermediate result yet continue from the wrong place, repeating or skipping steps or taking the wrong branch. These failures primarily reflect choosing the wrong computation to perform next rather than executing the right one incorrectly. Explicitly identifying the next step or the remaining computation removes much of the gap, whereas merely restating the current result does not. Training and internal-state interventions reinforce this distinction: the same remaining computation can succeed or fail depending on how it is entered, and internal states formed by a fresh run can repair a failed continuation. A correct intermediate result is therefore not necessarily a reasoning state from which the model can reliably continue.