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cs.CL2026
Structured Recurrent Mixers for Massively Parallelized Sequence Generation
Benjamin L. Badger
Over the last two decades, language modeling has experienced a shift from the use of predominantly recurrent architectures that process tokens sequentially during training and infe…
cs.LG2026
Toeplitz MLP Mixers are Low Complexity, Information-Rich Sequence Models
Benjamin L. Badger, Ethan Roland
Transformer-based large language models are in some respects limited by the quadratic time and space computational complexity of attention. We introduce the Toeplitz MLP Mixer (TMM…
cs.CL2026
Language Model Memory and Memory Models for Language
Benjamin L. Badger
The ability of machine learning models to store input information in hidden layer vector embeddings, analogous to the concept of `memory', is widely employed but not well character…