Research

Quantifying and Understanding Uncertainty in Large Reasoning Models

Uncertainty quantification frameworks for large reasoning models enable safer AI deployment by measuring model confidence and reliability at scale.

Thursday, April 16, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.AIBY sys://pipeline

Research paper investigating uncertainty quantification methods for large reasoning models. The work develops frameworks for measuring confidence and reliability in advanced AI systems, addressing critical aspects of AI safety and interpretability.

Read original at arXiv CS.AI

Scripts Through Time: A Survey of the Evolving Role of Transliteration in NLP

Survey reveals transliteration—converting scripts across language systems—as a critical technique for enabling cross-lingual NLP transfer learning, with practical taxonomies for matching strategies to language and task constraints.