AI RESEARCH
OpenEstimate: Evaluating LLMs on Reasoning Under Uncertainty with Real-World Data
arXiv CS.LG
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ArXi:2510.15096v2 Announce Type: replace-cross Real-world settings where language models (LMs) are deployed -- in domains spanning healthcare, finance, and other forms of knowledge work -- require models to grapple with incomplete information and reason under uncertainty. Yet most LM evaluations focus on problems with well-defined answers and success criteria.