{
  "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": "2610.09025",
  "title": "SPIN: Shadow Predictive Indexer for Sparse Attention",
  "summary": "SPIN replaces full KV-cache scoring by the sparse-attention indexer at every decoding step with lightweight history-based prediction of important KV blocks, integrated with speculative decoding. It requires adopting an indexer-based sparse attention stack and a vLLM implementation, adding prediction-model complexity and a modest integration cost rather than extra memory. It can fail when token-importance history is non-stationary, so workloads with abrupt topic shifts or long agentic trajectories may see quality degradation that the reported benchmarks do not cover.",
  "rationale": "The gain is meaningful but only materializes if the reader already serves long-context workloads through a compatible vLLM sparse-attention pipeline, so it warrants a workload-specific pilot rather than immediate adoption.",
  "technique": "History-predictive KV block indexing for sparse attention",
  "family": "kernels_e_atencao",
  "recommendation": "testar",
  "published": "2026-10-06",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.09025",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "source_url": "https://arxiv.org/abs/2610.09025",
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    }
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    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "data_and_training",
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
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  "equations_status": "not_fetched",
  "core_kind": "",
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