{
  "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": "2609.39361",
  "title": "LampAttention: Look-Ahead Mixed-Precision FlashAttention for Dedicated Accelerators",
  "summary": "The method replaces uniform-precision FlashAttention kernels with a pipeline that computes QK products and exponentials in 8-bit formats and selectively recomputes sensitive sub-blocks in 16-bit, but it requires a dedicated accelerator that exists only as a proposed specification with simulated results. The cost to a practitioner is not engineering complexity but hardware availability: there is no GPU kernel to install, and the accuracy claims come from simulation of Qwen3 and Gemma 3 rather than silicon measurements. If it eventually ships, adoption risks include workload-dependent sensitivity of the sub-block selection heuristic and divergence between simulated and real hardware behavior.",
  "rationale": "The technique depends on dedicated accelerator hardware that does not exist commercially, so a practitioner with a 24 GB GPU or API access cannot adopt or even benchmark it today.",
  "technique": "Mixed-precision FlashAttention with dedicated accelerator",
  "family": "kernels_e_atencao",
  "recommendation": "observar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39361",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces uniform-precision FlashAttention kernels with a pipeline that computes QK products and exponentials in 8-bit formats and selectively recomputes sensitive sub-blocks in 16-bit, but it requires a dedicated accelerator that exists only as a proposed specification with simulated results. The cost to a practitioner is not engineering complexity but hardware availability: there is no GPU kernel to install, and the accuracy claims come from simulation of Qwen3 and Gemma 3 rather than silicon measurements. If it eventually ships, adoption risks include workload-dependent sensitivity of the sub-block selection heuristic and divergence between simulated and real hardware behavior.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.39361",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "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": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}