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    "headline": "Sentence Transitions Became the Detection Signal",
    "slug": "sentence-transitions-became-the-detection-signal",
    "dek": "A graph-based detector looks for deviations between adjacent sentences instead of treating style features independently.",
    "summary": "A graph-based detector looks for deviations between adjacent sentences instead of treating style features independently.",
    "body_text": "The paper calls the signal relational over-regularization: recurring similarity bursts and transition patterns create sentence-pair variance that differs from human text in the tested data. Its CSFG implementation reports 97.14 percent binary accuracy, a 1.57 percent false-positive rate and an 11.14-point gain over the strongest graph baseline. The authors also show the boundary: performance falls when a generator's transition variance reaches or drops below the human baseline.",
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    "tags": [
      "AI-generated text",
      "graph learning",
      "detection"
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      "title": "arXiv preprint 2608.26694",
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    "title": "Sentence Transitions Became the Detection Signal",
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