MIT Professor: Algebraic Structure Predicts Transformer Length Generalization
ProfBuehlerMIT · x · 2026-08-15
ProfBuehlerMIT shares interesting work using Tilson's categories as algebra to shed light on when/why transformers length-generalize on structured sequence tasks. The authors show that for some tasks, the transformer learns a computation that keeps working as the sequence gets longer, while for others it breaks outside the training regime. The algebraic structure of the task predicts this distinction. This could help design training curricula, learning methods, and architectures that favor extrapolating computations.
Related event: Algebra and Category Theory Explain Transformer Length Generalization(2 posts)→
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