A diagnosis-driven framework detects behavioral inconsistency in LLM-generated BPMN models from ambiguous natural language specs, localizes issues to gateway logic, maps them to source text, and repairs the specifications to reduce variability.
Structure-aware optimization of decision diagrams for health guidance via integer programming.https://arxiv.org/abs/2603.22996
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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LLM-based pipeline converts medical guidelines into executable BPMN models with over 92% per-patient decision agreement and an entropy detector for policy ambiguity.
citing papers explorer
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Ambiguity Detection and Elimination in Automated Executable Process Modeling
A diagnosis-driven framework detects behavioral inconsistency in LLM-generated BPMN models from ambiguous natural language specs, localizes issues to gateway logic, maps them to source text, and repairs the specifications to reduce variability.
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Automatic Generation of Executable BPMN Models from Medical Guidelines
LLM-based pipeline converts medical guidelines into executable BPMN models with over 92% per-patient decision agreement and an entropy detector for policy ambiguity.