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Diagnosing Generalization Boundaries in AI-Based Vulnerability Detection via Calibrated Embedding Distances
High benchmark scores in learning-based vulnerability detection mask severe degradation under distribution shifts. This paper demonstrates that calibrated embedding geometry exposes these hidden generalization boundaries on syntactically similar vulnerable and patched code pairs. An evaluation of Co
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