AI RESEARCH
Lizard: An Efficient Linearization Framework for Large Language Models
arXiv CS.LG
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ArXi:2507.09025v4 Announce Type: replace-cross We propose Lizard, a linearization framework that transforms pretrained Transformer-based Large Language Models (LLMs) into subquadratic architectures. Transformers faces severe computational and memory bottlenecks with long sequences due to the quadratic complexity of softmax attention and the growing Key-Value (KV) cache that makes inference memory-bound by context length. Lizard addresses these limitations by