Heeyun Heo

When to Trust the Map: Confidence-Aware LLM Routing for Automotive CVE-to-ATM Mapping

The proposed framework generates candidate mappings via hierarchical in-context learning, then fuses self-consistency and LLM-based evidence verification signals into a calibrated meta-model, which can support selective automation by isolating auto-confirmable mappings from those requiring analyst review.