The February 12.2026 research from the University of Virginia and Google introduces the deep-thinking ratio (DTR), a novel metric designed to measure the true reasoning effort of large language models by analyzing internal token stabilization. While traditional metrics like token count often fail to predict accuracy due to "overthinking," DTR tracks how many layers a model requires before its internal predictions converge. Findings across several benchmarks indicate that higher DTR scores correlate strongly with correct answers, whereas mere output length often shows a negative correlation with performance. Using this insight, the authors developed Think@n, a test-time scaling strategy that identifies and prioritizes high-quality reasoning traces early in the generation process. This method allows models to match or exceed the accuracy of standard self-consistency while cutting computational costs by roughly half. Ultimately, the study suggests that reasoning quality is better reflected by a model's internal depth-wise processing than by the superficial length of its responses. Source: February 12 2026 Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens University of Virginia, Google Wei-Lin Chen, Liqian Peng, Tian Tan, Chao Zhao, Blake JianHang Chen, Ziqian Lin, Alec Go, Yu Meng https://arxiv.org/pdf/2602.13517