LGBO integrates LLM semantic preferences continuously into Bayesian optimization iterations, with a theoretical worst-case guarantee and empirical gains including 90% of best value in 6 iterations on a wet-lab battery task.
Bayesian Optimization of Catalysis With In-Context Learning
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LILO integrates LLMs to translate natural language feedback into preference signals for Gaussian process-based Bayesian optimization, outperforming standard preference BO and LLM-only methods on benchmarks.
ChemCrow augments LLMs with 18 expert chemistry tools to autonomously plan and execute syntheses and guide molecular discoveries in organic synthesis, drug discovery, and materials design.
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
citing papers explorer
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Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery
LGBO integrates LLM semantic preferences continuously into Bayesian optimization iterations, with a theoretical worst-case guarantee and empirical gains including 90% of best value in 6 iterations on a wet-lab battery task.
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LILO: Bayesian Optimization with Natural Language Feedback
LILO integrates LLMs to translate natural language feedback into preference signals for Gaussian process-based Bayesian optimization, outperforming standard preference BO and LLM-only methods on benchmarks.
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ChemCrow: Augmenting large-language models with chemistry tools
ChemCrow augments LLMs with 18 expert chemistry tools to autonomously plan and execute syntheses and guide molecular discoveries in organic synthesis, drug discovery, and materials design.
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Evolving Roles of LLMs in Scientific Innovation: Assistant, Collaborator, Scientist, and Evaluator
The paper proposes a four-role framework for LLMs in scientific innovation and reviews methods, benchmarks, and limitations across Assistant, Collaborator, Scientist, and Evaluator roles.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.