Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
Measures how late in a transformer’s layers each token prediction settles, then uses this signal to score a reasoning trace. Tests early rejection of low-scoring samples on math and science questions, measuring savings in generated tokens.
First public: · Added to this list: October 22, 2026
Cite this paper
Published version
Think Deep, Not Just Long: Measuring LLM Reasoning Effort via Deep-Thinking Tokens
Wei-Lin Chen, Liqian Peng, Tian Tan, Chao Zhao, Jianhang Chen, Ziqian Lin, Alec Go, Yu Meng
Proceedings of the 43rd International Conference on Machine Learning, 2026, pp. 16342-16360.
Explore this approach
Analysis and evaluation — Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.