{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Profiling shows attention computation is a bottleneck in your self-hosted service.",
    "change": "Check the supported GPU, tensor shapes, and precision, then replace only the relevant kernel.",
    "baseline": "The existing kernel inside the same complete serving pipeline.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. 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": "2610.11134",
  "title": "QUILT: Rethinking Sparse-Attention Prefill through Shared Query Execution",
  "summary": "QUILT replaces per-query independent sparse-attention prefill kernels by jointly processing neighboring queries, decomposing their overlapping KV selections via Shift-and-Compare Set Decomposition, and reusing shared KV entries across queries at multiple granularities. Adoption costs kernel-level integration complexity, a pipelined SCSD preprocessing stage whose overhead must be hidden, and potential quality risk from selectively dropping low-importance query-specific KV tails, all tuned to specific hardware tile behavior. It can fail when the workload lacks KV-selection overlap across neighboring queries, when the serving stack cannot accommodate a custom prefill kernel, or when tail removal degrades accuracy on query types sensitive to those entries.",
  "rationale": "The gains are validated only on GLM-5.3 and DeepSeek-3.2 under tensor and sequence parallelism, scales and models infeasible on a single 24 GB GPU, so the reader should track the technique until it lands in a serving framework usable with small self-hosted long-context models.",
  "technique": "Shared-query sparse-attention prefill kernel (QUILT)",
  "family": "kernels_e_atencao",
  "recommendation": "observar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11134",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "QUILT replaces per-query independent sparse-attention prefill kernels by jointly processing neighboring queries, decomposing their overlapping KV selections via Shift-and-Compare Set Decomposition, and reusing shared KV entries across queries at multiple granularities. Adoption costs kernel-level integration complexity, a pipelined SCSD preprocessing stage whose overhead must be hidden, and potential quality risk from selectively dropping low-importance query-specific KV tails, all tuned to specific hardware tile behavior. It can fail when the workload lacks KV-selection overlap across neighboring queries, when the serving stack cannot accommodate a custom prefill kernel, or when tail removal degrades accuracy on query types sensitive to those entries.",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.11134",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Up to 55.1% lower average sparse-attention kernel latency, up to 55.9% less KV data processed, and up to 36.8% lower TTFT versus the state-of-the-art sparse-attention kernel, with negligible accuracy degradation.",
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      "source_url": "https://arxiv.org/abs/2610.11134",
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      "source_excerpt": ""
    }
  ],
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      "dimension": "compute",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
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}