A Universal Reproducing Kernel Hilbert Space from Polynomial Alignment and IMQ Distance
About
We introduce the Yat kernel $$k_{b,\varepsilon}(\mathbf{w},\mathbf{x})=\frac{(\mathbf{w}^\top\mathbf{x}+b)^2}{\|\mathbf{x}-\mathbf{w}\|^2+\varepsilon},\qquad b\ge 0,\ \varepsilon>0,$$ a rational hidden-unit primitive whose units are Mercer sections over a shared input/weight space. For $b\ge 0$ the kernel is PSD; for $b>0$ it dominates a scaled inverse-multiquadric (IMQ) in the Loewner order, yielding fixed-kernel universality, characteristicness, and strict positive definiteness on every compact domain. The polynomial numerator opens nonradial alignment channels absent from finite IMQ expansions, witnessed by the directional far-field trace $T_\infty g_\varepsilon(\cdot;\mathbf{w},b)(\mathbf{u})=(\mathbf{u}^\top\mathbf{w})^2$. Algebraically, a second finite difference in the bias recovers any IMQ atom from three positive-bias Yat atoms exactly, sharp at three atoms in every dimension at exact pointwise equality. A trained shared-$(b,\varepsilon)$ Yat layer is therefore a finite learned-center expansion in a fixed universal characteristic RKHS, with closed-form norm $\boldsymbol{\alpha}^\top\mathbf{K}\boldsymbol{\alpha}$ and explicit diagonal $(\|\mathbf{x}\|^2+b)^2/\varepsilon$ driving a Rademacher generalization bound.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Zero-shot Reasoning | HellaSwag | Accuracy29.62 | 53 | |
| Question Answering | ARC-Challenge 0-shot (test) | Accuracy22.44 | 48 | |
| Zero-shot Commonsense Reasoning | PIQA zero-shot | Accuracy62.44 | 9 | |
| Next-word prediction | WikiText-103 | Perplexity39.04 | 8 | |
| Long-range Next-token prediction | PG-19 long-context | Perplexity (PPL)101.1 | 5 | |
| Zero-shot Question Answering | OpenBookQA | Accuracy28.4 | 5 | |
| Zero-shot word prediction | LAMBADA | Accuracy24.16 | 5 | |
| Zero-shot Question Answering | ARC Easy | Accuracy33.02 | 5 |