Where Models Disagree→All three models agree in 18 of 20 categories within each ecosystem. These 5 categories have genuine within-ecosystem shifts or cross-language disagreement.
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.
，更多细节参见WPS官方版本下载
For all the above reasons, when I implement code using automatic programming, I don’t have problems releasing it MIT licensed, like I did with this Z80 project. In turn, this code base will constitute quality input for the next LLMs training, including open weights ones.
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