C-ReD: A Comprehensive Chinese Benchmark for AI-Generated Text Detection Derived from Real-World Prompts

C-ReD addresses a specific regional gap (Chinese language) in AI detection, which is often neglected by Western-centric benchmarks. However, the project's defensibility is low (3/10) because detection is a notoriously difficult 'cat-and-mouse' game where benchmarks lose relevance as soon as newer, more capable models (like GPT-5 or next-gen GLM models) are released. With 0 stars and 8 forks, it currently serves primarily as an academic artifact rather than a production-ready tool. The 'frontier risk' is high because frontier labs (OpenAI, Google) and major Chinese players (Baidu, Alibaba, Tencent) are incentivized to build native watermarking and detection capabilities directly into their platforms, potentially rendering third-party benchmarks obsolete. Furthermore, the market for AI detection is rapidly consolidating toward model providers who can offer 'provenance' rather than post-hoc detection. The 6-month displacement horizon reflects the rapid iteration cycle of LLMs, which necessitates constant dataset updates to remain effective.