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.
Matter-of-Fact: A Benchmark for Verifying the Feasibility of Literature-Supported Claims in Materials Science
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Outcome evidence improves LLM accuracy on scientific feasibility assessment more consistently than experiment descriptions, which introduce brittleness under partial context.
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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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Experiments or Outcomes? Probing Scientific Feasibility in Large Language Models
Outcome evidence improves LLM accuracy on scientific feasibility assessment more consistently than experiment descriptions, which introduce brittleness under partial context.