EndoGov uses specialist agents plus a governance layer with hard and soft rule paths to deliver guideline-compliant endometrial cancer risk stratification, reporting 0.943 accuracy and 0.93% logic-violation rate on TCGA-UCEC while outperforming neural baselines on CPTAC-UCEC.
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LSDTs framework uses LLMs to build semantic ontologies from unstructured documents and integrate them into digital twins for regulation-aware infrastructure planning, evaluated via a Maryland offshore wind farm case study including Hurricane Sandy.
citing papers explorer
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EndoGov: A knowledge-governed multi-agent expert system for endometrial cancer risk stratification
EndoGov uses specialist agents plus a governance layer with hard and soft rule paths to deliver guideline-compliant endometrial cancer risk stratification, reporting 0.943 accuracy and 0.93% logic-violation rate on TCGA-UCEC while outperforming neural baselines on CPTAC-UCEC.
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LSDTs: LLM-Augmented Semantic Digital Twins for Adaptive Knowledge-Intensive Infrastructure Planning
LSDTs framework uses LLMs to build semantic ontologies from unstructured documents and integrate them into digital twins for regulation-aware infrastructure planning, evaluated via a Maryland offshore wind farm case study including Hurricane Sandy.