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arXiv · 2605.10536

HH-SAE: Discovering and Steering Hierarchical Knowledge of Complex Manifolds

Honghan Wu·Tianyan Wang·Jiacong Mi·Zhoyang Jiang·Yunsoo Kim
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Abstract

Rare semantic innovations in high-dimensional, mission-critical domains are often obscured by dense background contexts, a challenge we define as \textit{feature density conflict}. We introduce the \textbf{Hybrid Hierarchical SAE (HH-SAE)} to resolve this by factorizing manifolds into a nested hierarchy of \textbf{Contextual} ($L_0$), \textbf{Atomic} ($f_1$), and \textbf{Compository} ($f_2$) tiers. Evaluating across disparate manifolds, HH-SAE demonstrates superior resolution by \textbf{``fracturing'' administrative clinical labels into physiological modes} and achieving a peak \textbf{cross-domain zero-shot AUC of 0.9156 in fraud detection}. Path ablation confirms the architecture's structural necessity, revealing a 13.46\% utility collapse when contextual subtraction is removed. Finally, knowledge-steered synthesis achieves a +9.9\% AUPRC lift over state-of-the-art generators, proving that HH-SAE effectively prioritizes high-order mechanistic innovation over environmental proxies to enable high-precision discovery in high-stakes environments.

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Honghan Wu, Tianyan Wang, Jiacong Mi, Zhoyang Jiang, Yunsoo Kim. 2026-05-11. HH-SAE: Discovering and Steering Hierarchical Knowledge of Complex Manifolds. https://arxiv.org/abs/2605.10536

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