# Expert Specialization Analysis

Researchers at ByteDance Seed and Shanghai University of Finance and Economics trained a sparse autoencoder (SAE, a dictionary that reconstructs a layer's output as sparse interpretable features) on the MoE output of layers 4, 30 and 46 of Qwen3-30B-A3B, and used the encoder's affinity in the expert outputs to decompose each feature's activation exactly over the experts. Averaged over a corpus, each expert gets the set of features it principally drives; most of those features have only one or two dominators, and the division of labor runs below coarse domains, down to Java versus front-end web code and geometry versus arithmetic and polynomials.

Researchers at ByteDance Seed and Shanghai University of Finance and Economics trained a sparse autoencoder (SAE, a dictionary that reconstructs a layer's output as sparse interpretable features) on the MoE output of layers 4, 30 and 46 of Qwen3-30B-A3B, and used the encoder's affinity in the expert outputs to decompose each feature's activation exactly over the experts. Averaged over a corpus, each expert gets the set of features it principally drives; most of those features have only one or two dominators, and the division of labor runs below coarse domains, down to Java versus front-end web code and geometry versus arithmetic and polynomials.