SPIN: Shadow Predictive Indexer for Sparse Attention

Abstract:Indexer-based sparse attention reduces the cost of core attention by passing only a fixed, small number of important tokens to it. However, the indexer must still score the entire KV cache at every decoding step. This scoring overhead becomes a major bottleneck as the context length grows. We propose SPIN (Shadow Predictive Indexer) to reduce this indexer overhead. SPIN uses lightweight, history-based prediction to identify important KV blocks, avoiding the need to score the full KV cache at every decoding step. SPIN treats KV blocks and speculative decoding as first-class design and implementation considerations. Across extensive evaluations on long-context and agentic benchmarks, SPIN achieves 30-40% sparsity while preserving task quality. In end-to-end vLLM serving, SPIN improves output throughput by up to 14.9% and reduces median inter-token latency by up to 13.2%.

Machine Learning (cs.LG)

arXiv:2610.09025 [cs.LG]

(or arXiv:2610.09025v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.09025 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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