This arXiv paper proposes a KV-cache optimization technique for transformer inference that uses top-K retrieval with fixed-size linear-attention completion. The method preserves model backbone and KV-format compatibility while reducing memory access overhead. The approach targets a key efficiency bottleneck in large language model deployment.
Research
Top-K Retrieval with Fixed-Size Linear-Attention Completion: Backbone- and KV-Format-Preserving Attention for KV-Cache Read Reduction
Top-K retrieval technique reduces KV-cache memory access overhead in transformer inference while maintaining full compatibility with existing model architectures and formats.
Wednesday, April 8, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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