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Large language models as uncertainty-calibrated optimizers for experimental discovery

Research Bioinformatics AI

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TL;DR - Rankovic and colleagues present a method for training large language models as uncertainty-calibrated optimizers for experimental molecular discovery. Accounting for uncertainty in the underlying data could make model-guided design decisions more reliable.

  • Targets a key limitation of language models in molecular design: poorly calibrated uncertainty.
  • Incorporates data uncertainty directly into language-model training.
  • Frames language models as optimizers for selecting or proposing experimental discoveries.
  • The provided summary does not specify benchmarks, molecular tasks, or quantitative results.

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Large language models as uncertainty-calibrated optimizers for experimental discovery

Nature Machine Intelligence Bojana Ranković, Ryan-Rhys Griffiths, Philippe Schwaller 2026-08-28 doi:10.1038/s42256-026-01283-z
Public signals OpenAlex citations 0
Providers: Hugging Face · N/A OpenAlex · Citations 0 Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:27:05.264571 UTC

TL;DR - Rankovic and colleagues present a method for training large language models as uncertainty-calibrated optimizers for experimental molecular discovery. Accounting for uncertainty in the underlying data could make model-guided design decisions more reliable.

  • Targets a key limitation of language models in molecular design: poorly calibrated uncertainty.
  • Incorporates data uncertainty directly into language-model training.
  • Frames language models as optimizers for selecting or proposing experimental discoveries.
  • The provided summary does not specify benchmarks, molecular tasks, or quantitative results.
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