尽管面临市场竞争与产业调整，从入库企业的科创数据来看，多数企业仍然在加码研发、视科创为面向未来的投资与提升运营效率的重要途径。
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confusable-vision takes the 1,418 TR39 confusable pairs that map a non-Latin character to a Latin target (a-z, 0-9), renders both characters across every available system font, and computes SSIM for each pairing. The output is a scored JSON artifact: one continuous similarity score per pair, per font.
Initially I aimed to test with at least 10 formulas for each model for SAT/UNSAT, but it turned out to be more expensive than I expected, so I tested ~5 formulas for each case/model. First, I used the openrouter API to automate the process, but I experienced response stops in the middle due to long reasoning process, so I reverted to using the chat interface (I don't if this was a problem from the model provider or if it's an openrouter issue). For this reason I don't have standard outputs for each testing, but I linked to the output for each case I mentioned in results.