arXiv paper proposing a new method for KV cache compression in LLMs using probabilistic language tries. Claims to exceed per-vector Shannon limit, suggesting improved compression efficiency for inference optimization.
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Sequential KV Cache Compression via Probabilistic Language Tries: Beyond the Per-Vector Shannon Limit
Researchers propose probabilistic language tries for KV cache compression that exceed theoretical per-vector limits, potentially reducing inference memory footprint and compute costs for LLM deployment.
Monday, April 20, 2026 12:00 PM UTC2 MIN READSOURCE: arXiv CS.LG (Machine Learning)BY sys://pipeline
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