Decoupled Attention Fusion: Accelerating RAG with Efficient KV Cache Reuse
Retrieval-Augmented Generation (RAG) effectively mitigates hallucinations in Large Language Models (LLMs) but suffers from prohibitive Time-To-First-Token (TTFT) latency in long-context scenarios. Reusing pre-computed document KV caches addresses this but introduces a distribution mismatch, where offline caches lack the inter-document attention patterns required for coherent reasoning. CacheBlend reduces recomputation via selective attention, but suffers severe accuracy degradation at longer contexts. To address these challenges, we propose Decoupled Attention Fusion (DAF), a framework that maintains high accuracy while significantly reducing recomputation overhead. DAF decouples the atte...
| Authors | Xiabao Wu, Wentao Liu, Yongchao Liu et al. |
|---|---|
| Primary category | cs.PF |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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