EpiCache: Episodic KV Cache Management for Long Conversational Question
  Answering
Authors: name: Minsoo Kim, name: Arnav Kundu, name: Han-Byul Kim, name: Richa Dixit, name: Minsik Cho

Recent advances in large language models (LLMs) have extended context
lengths, enabling assistants to sustain long histories for coherent,
personalized responses. This ability, however, hinges on Key-Value (KV)
caching, whose memory grows linearly with dialogue length and quickly dominates
under strict resource constraints. An active line of research for reducing this
overhead is KV cache compression, which seeks to limit cache size while
preserving accuracy. Yet existing methods face two major limitations: (i)
evicting entries after full-context prefill causes unbounded peak memory, and
(ii) query-dependent eviction narrows the cache to a single query, leading to
degraded accuracy in multi-turn conversations. We introduce EpiCache, a
training-free KV cache management framework for long conversational question
answering (LongConvQA) under fixed memory budgets. EpiCache bounds cache growth
through block-wise prefill and preserves topic-relevant context via episodic KV
compression, which clusters conversation history into coherent episodes and
applies episode-specific KV cache eviction. We further design an adaptive
layer-wise budget allocation strategy that measures each layer's sensitivity to
eviction and distributes the memory budget across layers accordingly. Across
three LongConvQA benchmarks, EpiCache improves accuracy by up to 40% over
recent baselines, sustains near-full KV accuracy under 4-6x compression, and
reduces latency and memory by up to 2.4x and 3.5x, thereby enabling efficient
multi-turn interaction under strict resource constraints.