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.
"We like time to free up our mind. I get the best ideas when I walk my dog," says Marieke Pepers, chief people officer at the Dutch software firm Nmbrs.
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竞争壁垒稀释的压力同样不容忽视。雅迪、爱玛等传统巨头正将智能化功能下放到中低端车型，直接冲击九号的高端市场份额；割草机器人赛道面临科沃斯、追觅等品牌的低价围剿，RTK + 视觉技术优势不再独家。更值得警惕的是，公司核心发明专利占比不高，2024 年研发投入 8.26 亿元，远低于雅迪的 11.47 亿元，长期创新能力面临考验。
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