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cs.LG2026
Length Generalization for Transformers via Compression
Georg Zetzsche, Hongjian Jiang, Andy Yang +4
Recent advancements in transformer length generalization theory enable us to reliably predict when a transformer can learn to solve a task. In particular, the C-RASP hypothesis (a…
cs.LG2026
The Polynomial Counting Capabilities of Message Passing Neural Networks
Marco Sälzer, Pascal Bergsträßer, Anthony W. Lin
The counting power of Message Passing Neural Networks (MPNN) has been the subject of many recent papers, showing that they can express logic that involves counting up to a threshol…
cs.LG2026
Length Generalization Bounds for Transformers
Andy Yang, Pascal Bergsträßer, Georg Zetzsche +2
Length generalization is a key property of a learning algorithm that enables it to make correct predictions on inputs of any length, given finite training data. To provide such a g…