MLIPS: Multi-Layer Indexer Pool Sharing

Sparse attention limits the keys used by each query, but finding those keys can still require a full-prefix scan at every layer. We introduce MLIPS (Multi-Layer Indexer Pool Sharing), a training-free method that shares candidate search while retaining each consumer's own scoring and selection. A producer publishes a top- M pool larger than the attention budget K; subsequent layers select their own top- K from that pool. Measurements across three sparse-attention model families show substantially higher coverage from broad pools than overlap between final selections. We implement layerwise sharing, persistent decode reuse, and a coarse producer in vLLM for DeepSeek-V4-Flash. Across four long-context suites, three consumer layers per producer give 1.14 -- 1.40 times speedups and 14.8 -- 27.4% lower GPU energy, with observed accuracy changes of-1.49 to +0.29 percentage points in single runs. More extensive sharing exposes task-dependent quality losses. Sampled prefill traces also reveal recurring selected KV positions across query groups and layers. We characterize this shared working-set structure and use it to motivate Top- G, an exploratory bounded pool construction for prefill. Its deployment, task accuracy, and serving benefits remain open.