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Review regressions before expanding the test." ], "measure": "Numerical differences, end-to-end latency, throughput, and memory at realistic shapes.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2607.24593", "title": "PIVOT: Efficient Query-Group Indexing for Token-Level Sparse Attention", "summary": "PIVOT replaces the per-query full-prefix scoring pass of the DeepSeek Sparse Attention indexer with a single shared scan per group of nearby queries, using a proxy query to build a candidate set from which each query selects its top-k; it is training-free and drop-in. The cost is an approximation risk controlled by group size and by the variant chosen: PIVOT-Reuse maximizes speed by sharing one top-k across the group, while PIVOT-Refine re-scores candidates per query at additional compute to match the dense indexer. 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