Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention

arXiv:2510.13940v4 Announce Type: replace Abstract: Recent progress in large language models (LLMs) has focused on test-time scaling to improve reasoning via increased inference computation, but often at the cost of efficiency. We revisit test-time behavior and uncover a simple yet underexplored phenomenon: reasoning uncertainty is highly localized-only a small subset of high-entropy tokens dominantly affects output correctness. Motivated by this, we propose Minimal Test-Time Intervention (MTI), a training-free framework that enhances reasoning accuracy and stability with minimal overhead. MTI

Source: arXiv cs.CL — read the full report at the original publisher.

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