Uncertainty Profiles Predict Language Model Reasoning Accuracy
A new study on arXiv (2605.07776) introduces uncertainty trace profiles to analyze language model reasoning. Researchers treat intermediate token sequences (Chain-of-Thought traces) as evolving model states, summarizing each with features like slope and linearity. Across five LMs tested on GSM8K and ProntoQA, these profiles predict final answer correctness with AUROC up to 0.807, improving on prior work. Using only the first few hundred tokens, AUROC reaches 0.801, enabling early error detection. The study compares correct and incorrect traces to understand reasoning dynamics.
Key facts
- Study on arXiv (2605.07776) published in 2025.
- Focuses on uncertainty quantification in language model reasoning.
- Introduces uncertainty trace profiles summarizing trace features.
- Evaluated on five language models using GSM8K and ProntoQA datasets.
- Achieves AUROC up to 0.807 for predicting correct answers.
- Early detection possible with AUROC 0.801 using first few hundred tokens.
- Compares correct and incorrect reasoning traces.
- Chain-of-Thought reasoning is also called test-time scaling.
Entities
Institutions
- arXiv