Author

Philipp Nazari

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Preprint Nov 2026

Quantizing Recurrent Language Models Is Easier Than You Think

Linear recurrences are becoming an increasingly common building block in large language models, replacing the growing KV cache of softmax attention with a fixed-size recurrent state that enables efficient decoding. However, the quantization stack has largely been developed around softmax Transformers, whose numerical p...

Philipp Nazari, Neehal Tumma, Daniela Rus et al. · 0 citations

#machine learning Preprint Feb 2026

On State Reduction in Linear Attention

This work proposes a hardware-aware strategy to structurally prune the key and query matrices, reducing the state size while retaining compatibility with existing fast kernels, and proposes a robust structured pruning method based on a rank-revealing QR decomposition.

Philipp Nazari, T. Konstantin Rusch · 6 citations