AI RESEARCH
EpiCache: Episodic KV Cache Management for Long-Term Conversation on Resource-Constrained Environments
arXiv CS.CL
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ArXi:2509.17396v4 Announce Type: replace Modern large language models (LLMs) extend context lengths to millions of tokens, enabling coherent, personalized responses grounded in long conversational history. However, the Key-Value (KV) cache grows linearly with the extended dialogue history, causing the model's memory footprint to quickly exceed device limits. While recent KV cache compression methods attempt to reduce memory usage, most apply cache eviction after processing the entire context, incurring unbounded peak memory usage.