newsNature Machine IntelligenceTrust 88 · LabPublished 5d agoLive · 3d ago
Large language models as uncertainty-calibrated optimizers for experimental discovery
Nature Machine Intelligence, Published online: 28 August 2026; doi:10.1038/s42256-026-01283-z Although language models can be helpful in molecular design, they are not typically calibrated for uncertainty. Rankovic and colleagues present a method to train language models while taking into account the uncertainty of the data.
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paperProbing Chemical Language Models: Effects of Pre-training and Fine-tuningpaperMonroe: A Molecular Foundation Model for In-Context Probabilistic InferencepaperUncertainty-Aware Generation and Decision-Making Under AmbiguitypaperTraining Chemical Plausibility-Aware Large Language Models for Single-Step RetrosynthesispaperNuclearQAv2: A Structured Benchmark for Evaluating Domain-Science Competence in Large Language ModelspaperAn Experimental Design Approach to Evaluating Agentic AI's Autonomous Model DiscoverypaperBefore the Action: Benchmarking LLMs on Prospective Hypothesis DiscoverypaperCoMet: Context and Multiplicity Decomposition for Multimodal Uncertainty EstimationpaperStochastic Estimation of Transduced Language ModelspaperThe Invisible Editorial Layer: Formalizing Undisclosed Inference-Time Steering, Probability Placement, and the Attribution Problem in Deployed Language Models
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