{"slug": "scale-aware-attention-for-scarce-neural-data-an-rg-flow-transformer-on-sleep-edf", "title": "Scale-Aware Attention for Scarce Neural Data: An RG-Flow Transformer on Sleep-EDF EEG", "summary": "Researchers from an undisclosed institution found that the RG-Flow Transformer, a neural network with a renormalization-group inductive bias, matches the accuracy of a vanilla transformer on sleep staging from scarce EEG data (77.3% vs 77.0%) but uniquely recovers the spectral exponent β (R²=0.416), offering interpretability that the vanilla model lacks.", "body_md": "arXiv:2607.11950v1 Announce Type: new\nAbstract: Brain field potentials are scale-free: their power spectra follow a $1/f^{\\beta}$ law whose aperiodic exponent $\\beta$ tracks cortical state, and sleep depth in particular is a shift in $\\beta$. We ask whether a transformer endowed with an explicit renormalization-group (RG) inductive bias -- the RG-Flow Transformer, which couples ordinary self-attention to a scale-aware stream with a learnable anomalous dimension $\\gamma$, block-spin coarse-graining, and an entropy-gated synchronization bridge -- has an advantage over a parameter-matched vanilla transformer on \\emph{real, scarce} EEG. Using the PhysioNet Sleep-EDF corpus with a strict leakage-free by-subject hold-out, we (i) benchmark RG-Flow against a param-matched vanilla transformer and a hierarchy-only ablation on 5-class AASM sleep staging, (ii) sweep the per-subject data budget to look for the inductive-bias crossover predicted when data are scarce, and (iii) test whether RG-Flow's learned $\\gamma$ tracks the measured spectral exponent $\\beta$ out-of-sample -- a quantity the vanilla model does not possess. Across $5$ subjects and $5$ seeds under leave-one-subject-out cross-validation, RG-Flow and the vanilla transformer are statistically indistinguishable on 5-class staging (77.3\\% vs 77.0\\% accuracy; paired $p=0.294$), and the predicted scarce-data crossover does not appear: vanilla is numerically ahead at every data-limited budget. What does separate the models is interpretability -- RG-Flow recovers the continuous spectral exponent out-of-sample ($\\beta$-recovery $R^2 = 0.416$), a capability the vanilla architecture has no analogue for.", "url": "https://wpnews.pro/news/scale-aware-attention-for-scarce-neural-data-an-rg-flow-transformer-on-sleep-edf", "canonical_source": "https://arxiv.org/abs/2607.11950", "published_at": "2026-07-15 04:00:00+00:00", "updated_at": "2026-07-15 04:21:10.042071+00:00", "lang": "en", "topics": ["machine-learning", "neural-networks", "artificial-intelligence"], "entities": ["RG-Flow Transformer", "Sleep-EDF", "PhysioNet", "AASM"], "alternates": {"html": "https://wpnews.pro/news/scale-aware-attention-for-scarce-neural-data-an-rg-flow-transformer-on-sleep-edf", "markdown": "https://wpnews.pro/news/scale-aware-attention-for-scarce-neural-data-an-rg-flow-transformer-on-sleep-edf.md", "text": "https://wpnews.pro/news/scale-aware-attention-for-scarce-neural-data-an-rg-flow-transformer-on-sleep-edf.txt", "jsonld": "https://wpnews.pro/news/scale-aware-attention-for-scarce-neural-data-an-rg-flow-transformer-on-sleep-edf.jsonld"}}