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This paper demonstrates that large language models can effectively deanonymize authors of scientific papers from titles and abstracts alone, threatening the validity of double-blind peer review. The authors argue that stable patterns in problem framing and research focus act as latent conceptual signatures of authorship, necessitating a re-evaluation of anonymity practices in AI-augmented research ecosystems.