research
Sentence Transitions Became the Detection Signal
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.
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.
Why it matters
A graph-based detector looks for deviations between adjacent sentences instead of treating style features independently.
Limits and context
No additional limitation was separately recorded.
Key claims
A graph-based detector looks for deviations between adjacent sentences instead of treating style features independently.
Evidence: source-2026-08-30-008
Sources
- arXiv preprint 2608.26694arXiv · primary research
Corrections
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