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

Researchers propose Relational Over-Regularization (ROR), a sentence-pair-level signal showing LLMs produce inter-sentence transition variance that deviates from human writing, and validate it across four benchmarks (p < 0.001). Their Cross-Source Stylometric Fingerprint Graph (CSFG) achieves 97.14% accuracy in binary AI-generated text detection, outperforming the strongest graph-based baseline by 11.14 percentage points with a 1.57% false-positive rate, though detection degrades for generators with transition variance at or below the human baseline.

arXiv:2608.26694v1 Announce Type: new Abstract: 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 under distribution shift. We identify a structural signal at the sentence-pair level: LLMs produce inter-sentence transition variance that deviates from human writing through inflated variance driven by recurring similarity bursts at paragraph boundaries and templated transitions. We formalize this as Relational Over-Regularization ROR and validate it across four benchmarks p < 0.001 . The central contribution is this relational problem formulation, not a novel GNN architecture; CSFG is one concrete instantiation for operationalizing ROR. To exploit this signal, we propose 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. The per-edge signed deviation {\delta} ij operationalizes ROR without hand-crafted thresholds and acts as a false-positive calibrator. CSFG achieves 97.14% accuracy under binary detection, outperforming the strongest graph-based baseline by 11.14 pp, with a false-positive rate of 1.57% and robust generalization to unseen LLMs in the inflated-variance regime; detection degrades for generators whose transition variance falls at or below the human baseline.