LEAP uses a domain-specialized LLM and Bayesian optimization to prioritize perovskite additives, achieving average PCEs of 20.13% and 20.87% in later screening rounds versus 19.25% control, with a champion of 21.32%.
Note on the sampling error of the difference between correlated proportions or percentages.Psychometrika, 12(2):153–157
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Intent-aware retrieval over assertion-labeled knowledge graphs improves clinical QA accuracy by 22 percentage points on a new MIMIC-IV benchmark that stresses negation, temporality, and attribution.
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LEAP: A closed-loop framework for perovskite precursor additive discovery
LEAP uses a domain-specialized LLM and Bayesian optimization to prioritize perovskite additives, achieving average PCEs of 20.13% and 20.87% in later screening rounds versus 19.25% control, with a champion of 21.32%.
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ClinicalBench: Stress-Testing Assertion-Aware Retrieval for Cross-Admission Clinical QA on MIMIC-IV
Intent-aware retrieval over assertion-labeled knowledge graphs improves clinical QA accuracy by 22 percentage points on a new MIMIC-IV benchmark that stresses negation, temporality, and attribution.