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Evaluating Perplexity Bias in Academic Detection
Quantitative assessments demonstrate that standardized models consistently mistake concise, rule-based second-language phrasing for synthetic generation.
The protocol generates a cryptographic signature confirming the record’s integrity, without exposing unpublished findings.