arXiv:2611.05678v1 Announce Type: new Abstract: Audio deepfake detection (ADD) aims to distinguish bonafide speech from synthetic or manipulated speech. Representations from different layers of self-supervised learning (SSL) speech encoders vary in their ability to distinguish bonafide from spoofed speech. Comparing representations of the same utterance across encoder depths may reveal useful spoofing cues, yet common layer-selection and aggregation methods do not provide these comparisons as detection inputs. We propose Cross-Layer Relation Modeling (\ours{}), which explicitly encodes cross-layer relations alongside layer-specific features, making both directly available for ADD. CLRM reuses the existing detector's SSL encoder and identifies informative layer pairs by combining speaker-relative representation geometry with source-domain detection performance. The resulting relation score is fused with the detector's original score, requiring only the training of compact relation heads and fusion parameters while keeping the existing detector frozen. Across five external datasets, CLRM reduces the mean equal error rate (EER) for all three evaluated detectors, achieving relative reductions of 7.8--16.7%. Controlled comparisons with identical layer summaries and approximately matched auxiliary-head parameter counts also show a lower mean EER for explicit relations than direct concatenation, both before and after score fusion. These findings support explicit cross-layer comparisons as useful detection inputs for SSL-based audio deepfake detectors.