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This does not mean confusables.txt is wrong. It means confusables.txt is a visual-similarity claim that has never been empirically validated at scale. Many entries map characters to the same abstract target under NFKC decomposition (mathematical bold A to A, for instance), and the mapping is semantically correct even if the glyphs look nothing alike. But if you treat every confusables.txt entry as equally dangerous for UI security, you are generating massive false positive rates for 96.5% of the dataset.
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./build/parakeet sortformer.safetensors meeting.wav --model sortformer
The real magic, our Secret Sauce #1, lies in how these border points are selected. Naive approaches quickly fail:
但杨植麟并没有动摇，其提出要集中资源投入基础算法与新模型 K2，不再追逐“烧钱换用户”，而是试图用“技术换用户”。