Machine-generated text detection remains difficult in Chinese, where systematic benchmarks and large paired corpora are still scarce. We revisit this problem with a compact 4-layer character-level Transformer, trained on a 363.4-million-character Chinese detection corpus, to our knowledge the largest AI-human paired Chinese resource for this task. The resulting data-centric pipeline attains strong scores on Chinese benchmarks and, for the independently trained English variant, state-of-the-art RAID leaderboard numbers that remain stable under 11 RAID adversarial attack types. Ablations expose a double-edged sword role for punctuation: it eases optimization yet encourages overfitting to surface cues; symbol cleaning shifts the model toward deeper syntactic and discourse patterns and improves generalization. Minimal architecture plus rich supervision thus yields compact detectors suitable as a document-level authorship authenticity screen before downstream Chinese text processing; on a laptop RTX 3070 Ti we measure 5.90 x 10^5 tokens/s at under 100 MB VRAM. We present GiantMelon-ZH (Chinese) and GiantMelon-EN (English for RAID): same backbone, disjoint training corpora, transparent data descriptions.