Title: KVCache-Centric Memory for LLM Agents
Primary area: infrastructure, software libraries, hardware, systems, etc.
Keywords: Large Language Model, Agent Memory, KV Cache

Abstract: LLM agents in complex, long-horizon workflows are constrained by the model’s context window. Current plaintext-based memory systems suffer from unstable retrieval accuracy and disrupt prefix caching, harming both performance and efficiency. 
We propose MemArt, a novel memory paradigm that operates directly within the LLM-native format: the key-value (KV) cache. Instead of using plaintext, MemArt stores conversational turns as reusable KV cache blocks and retrieves relevant memories by computing attention scores in latent space. To enable accurate and efficient retrieval, we develop a multi-token aggregation retrieval strategy that uses compressed keys for efficient KV selection and a decoupled position encoding mechanism to ensure retrieved blocks are safely and coherently reused. On the LoCoMo benchmark, MemArt improves accuracy by over 11\% (up to 39.4\%) compared to state-of-the-art plaintext-based memory methods, nearly matching full-context performance. Critically, it achieves this while reducing prefill tokens by over two orders of magnitude (91-135$\times$), representing a significant leap forward for building powerful and efficient long-context agents.