Breaking the Shannon Limit: 900,000x KV Cache Compression for LLMs

This research shifts KV cache optimization from simple quantization to sequential predictive coding, potentially enabling near-infinite context windows for large language models. By treating KV data as a predictable language sequence, it achieves massive compression ratios that drastically reduce the memory and cost barriers for long-context AI applications.