提出新数据集验证模型对形状不变性的学习能力，证明结构优先架构更可靠。
Diagnostic Benchmarks for Invariant Learning Dynamics: Empirical Validation of the Eidos Architecture
- 构建分离拓扑不变性与纹理相关性的诊断基准
- Eidos架构在新数据集上达99%准确率，零样本字体迁移81.67%
- 适合研究几何建模与鲁棒泛化的研究人员
我们提出了PolyShapes-Ideal（PSI）数据集，一套用于隔离拓扑不变性——即在仿射变换下保持结构身份的能力——与主导标准视觉基准的纹理相关性的诊断基准。通过三种诊断探测（带噪声的多边形分类、从MNIST零样本字体迁移、渐进变形下的几何坍缩映射），我们证明Eidos架构在PSI上实现>99%准确率，并在30种未见字型间实现81.67%的零样本迁移，无需预训练。这些结果验证了“形式优先”假设：结构受限架构中的泛化是几何完整性而非统计规模的属性。
原文摘要 · Abstract (English)
We present the PolyShapes-Ideal (PSI) dataset, a suite of diagnostic benchmarks designed to isolate topological invariance -- the ability to maintain structural identity across affine transformations -- from the textural correlations that dominate standard vision benchmarks. Through three diagnostic probes (polygon classification under noise, zero-shot font transfer from MNIST, and geometric collapse mapping under progressive deformation), we demonstrate that the Eidos architecture achieves >99% accuracy on PSI and 81.67% zero-shot transfer across 30 unseen typefaces without pre-training. These results validate the "Form-First" hypothesis: generalization in structurally constrained architectures is a property of geometric integrity, not statistical scale.
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