Mathematical Framework for Analyzing Transformer Circuits

This paper presents a mathematical framework for reverse-engineering transformer circuits, revealing how attention heads implement in-context learning through compositional patterns. It identifies 'induction heads' as critical for two-layer models, demonstrating the potential for analyzing complex neural network behaviors through simplified architectures.

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