发表机构
Mindverse Computing LLC（Mindverse计算有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究在稀缺脑电数据上比较具有RG归纳偏差的RG流变压器和普通变压器，通过睡眠脑电语料库实验发现二者在睡眠分期准确率上无显著差异，但RG流变压器具有可解释性，能在样本外恢复连续谱指数。
AI 中文摘要
脑电场电位是无标度的：其功率谱遵循\(1/f^{\beta}\)定律，非周期指数\(\beta\)跟踪皮层状态，尤其是睡眠深度是\(\beta\)的一种变化。我们研究了一种具有显式重整化群（RG）归纳偏差的变压器——RG流变压器，它将普通自注意力与具有可学习反常维度\(\gamma\)、块自旋粗粒化和熵门控同步桥的尺度感知流相结合，在真实、稀缺的脑电图上是否比参数匹配的普通变压器具有优势。使用PhysioNet睡眠脑电语料库并进行严格的无泄漏受试者留出，我们（i）在5类AASM睡眠分期上，将RG流与参数匹配的普通变压器和仅层次结构的消融进行基准测试，（ii）扫描每个受试者的数据预算，以寻找数据稀缺时预测的归纳偏差交叉点，（iii）测试RG流学习到的\(\gamma\)是否能在样本外跟踪测量的谱指数\(\beta\)——普通模型不具备这个量。在留一受试者交叉验证下的5个受试者和5个种子中，RG流和普通变压器在5类分期上在统计上没有区别（准确率分别为77.3%和77.0%；配对\(p = 0.294\)），并且预测的稀缺数据交叉点没有出现：在每个数据有限的预算下，普通模型在数值上领先。区分模型的是可解释性——RG流在样本外恢复连续谱指数（\(\beta\)恢复\(R^2 = 0.416\)），这是普通架构所没有类似能力的。
英文摘要
Brain field potentials are scale-free: their power spectra follow a $1/f^β$ law whose aperiodic exponent $β$ tracks cortical state, and sleep depth in particular is a shift in $β$. 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 $γ$, 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 $γ$ tracks the measured spectral exponent $β$ 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 ($β$-recovery $R^2 = 0.416$), a capability the vanilla architecture has no analogue for.
