The paper introduces Relational Over‑Regularization (ROR), a structural signal at the sentence‑pair level that captures inflated inter‑sentence transition variance in AI‑generated text. It proposes the Cross‑Source Stylometric Fingerprint Graph (CSFG), a graph‑based framework that encodes positional, sequential, semantic, and transition deviation signals as learnable GNN edge features, achieving 97.14% accuracy in binary detection and outperforming existing graph‑based baselines. The method demonstrates robust generalization to unseen large language models in the inflated‑variance regime while maintaining a low false‑positive rate.
By Hyeonchu Park, Bugeun Kim
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.
The paper introduces MOSAIC, a large adversarial benchmark for detecting AI-generated text, and presents NeuroStat, a new framework that combines token‑level probabilistic logits with deep semantic hidden states from a single language model. NeuroStat fuses these signals via Macro‑State Residual Modulation and uses orthogonal and contrastive losses to learn complementary representations. Experiments show that NeuroStat outperforms existing methods on MOSAIC, achieving superior robustness against adversarial attacks.
By Peiming Li, Yifan Wang, Zhiyuan Hu, Shiyu Li, Zheng Wei, Yang Tang
The paper introduces a context‑aware neural grapheme‑to‑phoneme (G2P) system for unsegmented languages like Japanese, using a discriminative conditional random field over a word lattice built from dictionaries. It addresses data scarcity by generating over two million sentences with large language models. Experiments show the method surpasses traditional morphological analyzers and neural sequence models, achieving 99.62% target word reading accuracy and very low phoneme error rates on the Joyo‑Kanji‑Yomi benchmark.
By Rui Hu, Zhenpeng Zhan, Xiaolong Lin
The paper introduces a context‑aware neural grapheme‑to‑phoneme (G2P) system for unsegmented languages like Japanese, combining a discriminative conditional random field over a dictionary‑based word lattice with large language model‑generated training data. By generating over two million synthetic sentences, the method addresses data scarcity and achieves superior performance compared to traditional morphological analyzers and neural sequence models. On the Joyo‑Kanji‑Yomi benchmark, it attains 99.62% target word reading accuracy, 0.32% target word phoneme error rate, and 0.14% sentence phoneme error rate.
Distinguishing machine-generated text (MGT) from human-written text (HWT) becomes increasingly important due to potential misuse. However, most supervised detectors often degrade out-of-domain (OOD) a...