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Traditional knowledge graph matchers fall short, with LLMs outperforming them by a significant margin in threat report evaluations.
HoRFFI achieves remarkable gains in novel-class accuracy and AUC, proving that high-openness RFFI can be effectively addressed with minimal labeled data.
