DN-Hypo-Pipeline operationalizes three philosophy-of-science accounts to direct LLMs toward principle-based hypothesis generation, claims superior performance over direct prompting, and derives two new transformer algorithms from the resulting hypotheses.
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SmartIterator supplies method-specific workflows and coordinated visualizations to systematically supervise and interpret parameter sweeps of unsupervised data grouping techniques.
Sycamore shows grounding synthetic personas with real-user artifacts aligns their feedback language and concerns more closely with experts, yet both synthetic conditions miss experts' image-modality preference and converge on a find-and-adapt evaluation frame.
citing papers explorer
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DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanations
DN-Hypo-Pipeline operationalizes three philosophy-of-science accounts to direct LLMs toward principle-based hypothesis generation, claims superior performance over direct prompting, and derives two new transformer algorithms from the resulting hypotheses.
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SmartIterator: Visual Analytics Workflows for Supervising Unsupervised Data Grouping
SmartIterator supplies method-specific workflows and coordinated visualizations to systematically supervise and interpret parameter sweeps of unsupervised data grouping techniques.
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Sycamore: Characterizing Synthetic Personas for Evaluating Genomics Visualization Retrieval
Sycamore shows grounding synthetic personas with real-user artifacts aligns their feedback language and concerns more closely with experts, yet both synthetic conditions miss experts' image-modality preference and converge on a find-and-adapt evaluation frame.