正規化リーク：群対称性の下で標準代表が教師あり学習を混乱させる仕組み
Canonicalization Leakage: How Canonical Representatives Confound Supervised Learning under Group Symmetry
研究概要
モデルが不変構造ではなく標準代表の表現上のアーティファクトを利用する問題を研究し、CL-DIAG 診断法を提案する。
原文要旨（英語）
Models trained on canonical representatives of equivalence classes under group symmetry can exploit representation artifacts rather than learning invariant structure. We propose CL-DIAG, a six-step diagnostic protocol that detects, localizes, and quantifies this "canonicalization leakage." Applied to circuit complexity prediction over 616,126 NPN equivalence classes of 5-input Boolean functions (|G| = 7,680), CL-DIAG reveals that a baseline MLP achieves Spearman r_s = 0.788 on canonical data but only r_s = 0.254 when NPN-averaged, with 0% prediction consistency. Signal decomposition shows canonical performance decomposes into classical invariant signal (r_s = 0.635), neural invariant signal (+0.142), and canonicalization leakage (+0.011). NPN augmentation at 7x recovers r_s = 0.777, exceeding the classical invariant ceiling by 14 percentage points. A matched-volume control confirms the gain is from symmetry-consistent augmentation, not generic regularization.