Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models
The paper evaluates the phenomenon of harmful overthinking in Large Reasoning Models (LRMs). It introduces a new evaluation protocol to assess whether additional reasoning after reaching a correct answer is beneficial or detrimental. Findings suggest that many reasoning tasks require less reasoning than previously thought, and stopping at the first correct answer can significantly improve accuracy.
- ▪The study investigates the impact of excessive reasoning in Large Reasoning Models.
- ▪It introduces a prefix-level trajectory evaluation protocol to assess reasoning sufficiency.
- ▪Results indicate that stopping at the first correct answer can improve accuracy by up to 21%.
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Computer Science > Artificial Intelligence arXiv:2606.02835 (cs) [Submitted on 1 Jun 2026] Title:Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models Authors:Simone Caldarella, Davide Talon, Rahaf Aljundi, Elisa Ricci, Massimiliano Mancini View a PDF of the paper titled Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models, by Simone Caldarella and 4 other authors View PDF HTML (experimental) Abstract:Large Reasoning Models (LRMs) improve performance by generating explicit intermediate reasoning traces through increased test-time compute, yet the assumption that longer reasoning is consistently beneficial remains under-examined.
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