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arXiv Machine Learning By Riteng Zhang, Xiaoqian Wang

Rethinking SAE Evaluation: An Atomic Interpretable Unit Framework Reveals Hidden Polysemanticity and Redundancy

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The paper introduces a new framework for evaluating sparse autoencoders (SAEs) by requiring their features to form an atomic interpretable unit basis, a coordinate system for semantically meaningful explanation. It formalizes this objective and organizes existing and new metrics, such as DAME, into a comprehensive benchmarking framework. Experiments across many SAE architectures reveal that some models appear monosemantic under current metrics yet are functionally polysemantic, with dominant features being less interpretable and more redundant.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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