GlyphNet’s own results support this: their best CNN (VGG16 fine-tuned on rendered glyphs) achieved 63-67% accuracy on domain-level binary classification. Learned features do not dramatically outperform structural similarity for glyph comparison, and they introduce model versioning concerns and training corpus dependencies. For a dataset intended to feed into security policy, determinism and auditability matter more than marginal accuracy gains.
These aren't contrived scenarios invented by test authors in total vacuum. They're consequences of the spec's design and reflect real world bugs.
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def parse_list(self, html: str) - Tuple[List[str], Optional[str]]: