Builds a 2M-contribution graph from 230k papers with 12.5M prerequisite links and reports 0.48 MAP on temporal backtesting for predicting enabling technologies.
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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.
CodeDistiller distills 250 materials-science GitHub repositories into vetted code libraries that improve the accuracy and scientific soundness of experiments generated by ASD agents.
citing papers explorer
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The Scientific Contribution Graph: Automated Literature-based Technological Roadmapping at Scale
Builds a 2M-contribution graph from 230k papers with 12.5M prerequisite links and reports 0.48 MAP on temporal backtesting for predicting enabling technologies.
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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.