Sparse Crosscoders for diffing MoEs and Dense models

Marmik Chaudhari; Nishkal Hundia; Idhant Gulati

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arXiv · 2603.05805

Sparse Crosscoders for diffing MoEs and Dense models

Marmik Chaudhari·Nishkal Hundia·Idhant Gulati

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Abstract

Mixture of Experts (MoE) achieve parameter-efficient scaling through sparse expert routing, yet their internal representations remain poorly understood compared to dense models. We present a systematic comparison of MoE and dense model internals using crosscoders, a variant of sparse autoencoders, that jointly models multiple activation spaces. We train 5-layer dense and MoEs (equal active parameters) on 1B tokens across code, scientific text, and english stories. Using BatchTopK crosscoders with explicitly designated shared features, we achieve $\sim 87\%$ fractional variance explained and uncover concrete differences in feature organization. The MoE learns significantly fewer unique features compared to the dense model. MoE-specific features also exhibit higher activation density than shared features, whereas dense-specific features show lower density. Our analysis reveals that MoEs develop more specialized, focused representations while dense models distribute information across broader, more general-purpose features.

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Marmik Chaudhari, Nishkal Hundia, Idhant Gulati. 2026-03-06. Sparse Crosscoders for diffing MoEs and Dense models. https://arxiv.org/abs/2603.05805

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