{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "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": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "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": "2609.19077",
  "title": "Probabilistic Linear Explanations",
  "summary": "The method replaces post-hoc local explainers such as LIME and MAPLE with k-sparse anchored linear explanations over the Boolean hypercube, computed via an exact MIP solver or a polynomial-time IHT approximation. Costs are an external MIP solver or iterative optimization per explanation, sampling around each instance, and the fact that exact relevance-error minimization is NP^PP-hard for neural networks, so only the empirical surrogate is solved. It can fail if the fidelity-error surrogate diverges from true relevance error outside the local distribution, if the chosen sparsity budget k omits features the audience needs, or if per-instance optimization latency is unacceptable in an interactive production path.",
  "rationale": "The compute (MIP or IHT on sampled instances) fits constrained infrastructure and the paper claims strictly better constraint satisfaction and relevance error than LIME/MAPLE, but the gain is workload-specific and the intractability caveat warrants small-scale validation before production adoption.",
  "technique": "Sparse anchored linear probabilistic explanations",
  "family": "outro",
  "recommendation": "testar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.19077",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces post-hoc local explainers such as LIME and MAPLE with k-sparse anchored linear explanations over the Boolean hypercube, computed via an exact MIP solver or a polynomial-time IHT approximation. Costs are an external MIP solver or iterative optimization per explanation, sampling around each instance, and the fact that exact relevance-error minimization is NP^PP-hard for neural networks, so only the empirical surrogate is solved. It can fail if the fidelity-error surrogate diverges from true relevance error outside the local distribution, if the chosen sparsity budget k omits features the audience needs, or if per-instance optimization latency is unacceptable in an interactive production path.",
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      "result": "",
      "baseline": "",
      "conditions": "",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Explanations consistently achieve lower relevance error than LIME and MAPLE while satisfying anchoring and k-sparsity constraints by construction; no numeric factor is reported in the abstract.",
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      "baseline": "",
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    }
  ],
  "exposure_map": [
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      "finding": "",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
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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": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}