Dr.CoT: Diagnosing and Regulating Chain-of-Thought via Sparse Feature Coverage

Large reasoning models improve complex problem solving through extended chain-of-thought (CoT) reasoning, but excessive reasoning increases inference cost and can even degrade accuracy, a phenomenon known as overthinking. Existing approaches mitigate overthinking by estimating reasoning sufficiency from trajectory-derived signals, yet lack explicit prompt-conditioned modeling of the reasoning state, limiting their ability to diagnose sufficiency and realign the trajectory when reasoning deviates from the prompt. To address these limitations, we propose **Dr.CoT**, a framework for diagnosing and regulating chain-of-thought reasoning via prompt-conditioned sparse feature coverage. Specifically, Dr.CoT first represents the prompt and reasoning trajectory in a shared sparse feature space, and uses prompt-conditioned feature coverage as an indicator of the current reasoning state. It then tracks feature coverage and its gain to diagnose reasoning sufficiency, adaptively stopping reasoning when coverage is sufficient and gains remain persistently low, which improves the accuracy–efficiency trade-off. During continued reasoning, it further uses uncovered prompt features to steer the model toward addressing the prompt, correcting deviations of the reasoning trajectory from the prompt. Extensive experiments across three model scales show that Dr.CoT reduces average token usage by **17.01%** while improving average accuracy by **2.16%** over standard CoT, and also outperforms all evaluated baselines.