AI RESEARCH
Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving
arXiv CS.LG
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ArXi:2604.26837v1 Announce Type: new Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing only a small, query-dependent subset of the KV state per decoding step and extending the KV storage to CPU memory. In practice, however, these algorithmic savings rarely translate into end-to-end system-level gains because sparse methods typically operate at different granularities and thus rely on ad hoc, per-algorithm implementations.