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× 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.
