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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.
This paper investigates the production-evaluation gap in large reasoning models (LRMs), finding that they fail to robustly evaluate reasoning despite near-perfect solution production, due to an answer confirmation bias.