Understand and Accelerate Memory Processing Pipeline for Disaggregated LLM Inference

ArXi:2603.29002v1 Announce Type: cross Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse attention, retrieval-augmented generation (RAG), and compressed contextual memory, to complex reasoning. We show that these optimizations can be unified into a four-step memory processing pipeline: Prepare Memory, Compute Relevancy, Retrieval, and Apply to Inference.