ARB: A Matched Authorship-Rewriting Benchmark Dataset for AI-Text Detector Evaluation

AI-text detectors are typically evaluated on human-versus-LLM text, but this may not reflect real-world cases where humans revise their own writing using LLMs. ARB tests this directly, creating matched text variants --- human-written, LLM-generated, human-text rewritten by LLMs, and LLM-text rewritten by LLMs --- across multiple source datasets and generator models. Results show detectors that catch direct LLM text well fail dramatically when detecting LLM-rewritten human writing. This is critical for academic integrity systems, content moderation platforms, and journalism tools relying on AI-text detection, revealing a major blind spot in current detection methods. Authors: Gaetano Perrone, Simon Pietro Romano Paper: https://arxiv.org/abs/2607.29539v1