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This paper formalizes when benchmark contamination is detectable, deriving information-theoretic limits and proposing power-calibrated audits that distinguish a clean benchmark from a powerless detector. It reports two-sided empirical findings on calibration efficacy and validity gates.
This paper introduces STELA, a linguistics-aware watermarking framework for LLMs that leverages syntactic predictability via POS n-grams to balance text quality and detection robustness. The method enables publicly verifiable watermark detection without requiring access to model logits, demonstrating superior performance across typologically diverse languages (English, Chinese, Korean).