这篇论文用SenFlow模型做混合人机文本检测，在MOSAIC基准上比现有方法高出4个多点的F1分数，还发现AI句子有长度特征。
SenFlow将混合文档中的句子级AI文本检测重构为结构化预测问题，在图传播与线性链CRF基础上实现整文档级联合解码。MOSAIC基准包含16,000篇混合文档，由DeepSeek-V3.2和Kimi K2生成，并引入困惑度一致性过滤。在跨域迁移最难协议下，SenFlow平均Macro-F1比现有方法高出4.15个百分点。研究还发现，即使经困惑度过滤后，AI插入的句子长度仍存在生成器依赖的差距。
SenFlow: Inter-Sentence Flow Modeling for AI-Generated Text Detection in Hybrid Documents
Sentence-level AI-generated text detection (S-AGTD) for hybrid documents, where humans and LLMs co-author one text, faces two gaps: existing methods classify each sentence in isolation, discarding inter-sentence dependencies, and existing benchmarks omit the newest generation of generators. We construct MOSAIC, a benchmark of 16,000 hybrid documents over PubMed and XSum, generated by DeepSeek-V3.2 and Kimi K2 under stringent quality controls including a perplexity-consistency filter absent from prior benchmarks. We recast S-AGTD as structured prediction over the document sentence sequence and instantiate it as SenFlow, integrating graph-based inter-sentence propagation with linear-chain CRF decoding in a single document-level pass over a sentence graph. SenFlow reaches state-of-the-art performance on MOSAIC, with a +4.15 pp average Macro-F1 margin on cross-domain transfer, the hardest of three protocols of increasing difficulty. We further find that even after the perplexity filter equalizes overt cues, AI insertions retain a generator-dependent sentence-length gap that sentence-level detectors still exploit. Code and data: https://github.com/luojingkun22/SenFlow