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Text Detection

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Papers

Beyond Global Scalars: Synergizing Token-Level Statistics and Deep Semantics for Adversarial AIGC Text Detection

2026-08-28 · Peiming Li, Yifan Wang, Zhiyuan Hu, Shiyu Li 외 arxiv

The rapid evolution of large language models necessitates robust machine-generated text detection. Existing paradigms typically follow two isolated tracks. Training-free methods rely on global statistical scalars such as…

Text Detection

D2C-Routing: Dimension-to-Composition Evidence Routing for Mixed-Origin AI-Generated Text Detection

2026-08-27 · Xin Chen, Fuwei Zhang, Yiqi Tong, Wei Guo 외 arxiv

AI-generated text detection is commonly framed as a binary document-level judgment about whether a text is human-written or machine-generated. This framing breaks down for mixed-origin writing, where content origin and e…

Text Detection

Relational Over-Regularization: Graph-Based AI-Generated Text Detection via Sentence Transition Deviation

2026-08-27 · Hyeonchu Park, Bugeun Kim arxiv

Detecting AI-generated text (AIGT) remains challenging because existing approaches rely on token-level statistical signals or independent stylometric features, causing them to overfit to specific generators and fail unde…

Text Detection

Unveiling Spectral Mechanisms in Training-Free LLM Text Detection

2026-08-26 · Haitong Luo, Xuying Meng, Weiyao Zhang, Wenji Zou 외 arxiv

The rapid advancement of Large Language Models (LLMs) makes it increasingly difficult to distinguish human writing from machine-generated text. Training-free detection offers a scalable solution, yet common confidence-ba…

Text Detection

DeBERTa-Sentinel: Toward Transparent and Trustworthy Detection of AI-Generated Text

2026-08-02 · Muhammad Yousaf Rehman, Muhammad Islam arxiv

The rapid spread of large language models (LLMs) across the web raises concerns about misinformation, academic integrity, automated content manipulation, and risks to vulnerable online communities. Existing transformer-b…

Text Detection

ASK-NN: An Asymmetric Nearest-Neighbor Test that detects Distribution Drifts in Natural Language

2026-07-17 · Sergey Zakharov, Rodion Oblovatny, Alexey Zaytsev arxiv

Hallucinations and artificial text in LLM-generated outputs often appear as distributional deviations between prompt and response hidden-state distributions. Since prompts or retrieved contexts typically serve as referen…

Text Detection

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