Papers with representation-based methods
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. |
Mitigating Hallucinations in Large Vision-Language Models without Performance Degradation (2026.acl-long)
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| Challenge: | Recent advances in large vision-language models produce hallucinations that compromise output reliability. |
| Approach: | They propose a dual-stage framework for mitigating hallucinations without performance degradation . they propose semantic-aware component disentanglement and interpretable parameter updates . |
| Outcome: | The proposed model reduces hallucinations by 23.4% while maintaining 97.4% of general generative capability. |