{
  "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": "2610.08578",
  "title": "Random Feature Gaussian Process Attention: Linear-Time Probabilistic Attention with Calibrated Uncertainty",
  "summary": "RFF-GPA replaces standard softmax attention with a Gaussian process posterior whose stationary kernel is approximated by random Fourier features, yielding linear-time computation of the posterior mean and variance. The cost is an additional low-rank feature mapping and the approximation error inherent to random Fourier features, plus integration and tuning overhead relative to drop-in softmax attention. It can fail if the stationary-kernel assumption poorly matches the task's attention structure, if the chosen number of random features underfits the kernel and degrades accuracy, or if calibration gains do not transfer to the reader's data distribution.",
  "rationale": "The method is implementable on a single 24 GB GPU and directly relevant when calibrated uncertainty matters, but the abstract provides no production-scale latency measurements and the accuracy-versus-approximation trade-off must be validated on the reader's specific workload before adoption.",
  "technique": "Random Fourier feature GP attention for calibrated uncertainty",
  "family": "kernels_e_atencao",
  "recommendation": "testar",
  "published": "2026-10-06",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.08578",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RFF-GPA replaces standard softmax attention with a Gaussian process posterior whose stationary kernel is approximated by random Fourier features, yielding linear-time computation of the posterior mean and variance. The cost is an additional low-rank feature mapping and the approximation error inherent to random Fourier features, plus integration and tuning overhead relative to drop-in softmax attention. It can fail if the stationary-kernel assumption poorly matches the task's attention structure, if the chosen number of random features underfits the kernel and degrades accuracy, or if calibration gains do not transfer to the reader's data distribution.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.08578",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Reduces attention complexity from quadratic (decoupled GP variants) or cubic (exact GP) to linear in sequence length while improving calibration and maintaining predictive accuracy; no fixed multiplicative speedup factor is stated in the abstract.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.08578",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "finding": "",
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      "next_check": "",
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      "dimension": "compute",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": ""
}