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cs.LG2026
Why Are Linear RNNs More Parallelizable?
William Merrill, Hongjian Jiang, Yanhong Li +2
The community is increasingly exploring linear RNNs (LRNNs) as language models, motivated by their expressive power and parallelizability. While prior work establishes the expressi…
cs.LG2026
Length Generalization Bounds for Transformers
Andy Yang, Pascal BergsträÃer, 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…
cs.LG2026
The Polynomial Counting Capabilities of Message Passing Neural Networks
Marco Sälzer, Pascal BergsträÃer, 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…