arXiv

Thinking Past the Answer: Evaluating Harmful Overthinking in Large Reasoning Models

Title: Looking Beyond the Solution: Assessing the Dangers of Excessive Deliberation in Large Reasoning Models

Abstract:

Large Reasoning Models (LRMs) have demonstrated enhanced capabilities by leveraging increased test-time compute to produce explicit intermediate reasoning steps. However, the prevailing assumption that extended reasoning periods are uniformly advantageous has not been sufficiently scrutinized. Although recent studies indicate that prolonged deliberation can cause models to overthink, a critical question remains: once the correct answer is identified, does subsequent reasoning refine the outcome or lead the model astray?

To investigate the dynamics that occur after a model achieves correctness, we propose a trajectory evaluation protocol at the prefix level, based on the concept of reasoning sufficiency. This approach defines the minimum reasoning effort needed for a model to generate the correct answer for the first time. This framework enables us to distinguish between verbose overthinking—where extra reasoning is superfluous yet benign—and harmful overthinking, where continued analysis destabilizes a previously correct path.

Our analysis, initially conducted on multimodal benchmarks, reveals that many tasks classified as reasoning-intensive actually demand surprisingly minimal cognitive effort. Notably, terminating the process at the first correct prefix boosts accuracy by up to 21% compared to standard reasoning protocols. This finding suggests that current models are constrained not merely by their reasoning capabilities, but also by their failure to recognize when to stop.

Additionally, while standard efficiency measures such as early stopping can significantly curtail verbose overthinking (by as much as 50%), they prove ineffective against harmful overthinking. Our failure analysis indicates that deviations from correctness are primarily caused by logical drift and the reinterpretation of visual data. Finally, we demonstrate that these issues extend to language-only reasoning benchmarks, positioning harmful overthinking as a significant general reliability concern. Code is available at https://simonecaldarella.github.io/thinking-past-the-answer.


Source: arXiv Generated at: 2026-06-03 00:00:00 UTC

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