Tapan Parikh · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28281 · open-access PDF
一个API背后的模型可以通过其单词的答案来识别日常问题,如“命名声音”或“命名诗人”。一个模型的个人资料记录了它对每个问题给出的每个答案的频率。我们将新答案归因于最有可能给出的模型。在94个模型中,40个答案确定了准确的模型92.5%的时间,而1.1%是偶然的。通过OpenRouter构建的配置文件在盲标下识别了DeepInfra服务的所有10个模型。
Machine translation (Meta M2M100); the English original follows.
A model behind an API can be identified from its one-word answers to everyday questions such as "Name a sound" or "Name a poet". A model's profile records how often it gives each answer to each question. We attribute new answers to the model most likely to have given them. On a panel of 94 models, 40 answers identify the exact model 92.5% of the time, against 1.1% by chance. Profiles built through OpenRouter identified all ten models served by DeepInfra under blinded labels. A system prompt lowers accuracy by 6 to 16 points. We applied the method to 96 endpoints of 13 open models on OpenRouter
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