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.
It's not being rolled out yet, but I expect it to be deployed by this time next year.
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双方联合开发 Stateful Runtime Environment（有状态运行时环境），让 AI ·Agent 能记住上下文、调用工具；。关于这个话题，WPS官方版本下载提供了深入分析