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How Reliable are Confidence Estimators for Large Reasoning Models? A Systematic Benchmark on High-Stakes Domains (2026.eacl-long)
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Reza Khanmohammadi, Erfan Miahi, Simerjot Kaur, Charese Smiley, Ivan Brugere, Kundan S Thind, Mohammad M. Ghassemi
| Challenge: | Large Reasoning Models often struggle with confidence calibration, authors say . authors: accurate confidence scores are essential to build trustworthy systems . |
| Approach: | They propose a Reasoning Model Confidence estimation benchmark to assess LRM confidence . the benchmark is constructed from 347,496 reasoning traces from six popular LRMs . |
| Outcome: | The proposed benchmark compares ten different representation-based methods on a wide range of architectures. |