cross-language-reasoning

This research investigates whether multilingual large language models develop shared internal representations for mathematical reasoning across languages, introducing a novel Geometry-Invariant Sparse Autoencoder (GI-SAE) method. It finds that cross-language feature sharing is model-dependent and that geometric similarity does not consistently imply functional interchangeability.