Analytica uses soft propositional reasoning to decompose problems, ground facts with tools, and average outputs with linear models, yielding 15.84% average accuracy gains and lower variance on forecasting tasks.
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CodeDistiller distills 250 materials-science GitHub repositories into vetted code libraries that improve the accuracy and scientific soundness of experiments generated by ASD agents.
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Analytica: Soft Propositional Reasoning for Robust and Scalable LLM-Driven Analysis
Analytica uses soft propositional reasoning to decompose problems, ground facts with tools, and average outputs with linear models, yielding 15.84% average accuracy gains and lower variance on forecasting tasks.
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CodeDistiller: Automatically Generating Code Libraries for Scientific Coding Agents
CodeDistiller distills 250 materials-science GitHub repositories into vetted code libraries that improve the accuracy and scientific soundness of experiments generated by ASD agents.