{
  "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": "2610.08537",
  "title": "FlowCF: Sparse Counterfactual Explanations for Mixed-Type Tabular Data using Flow Matching",
  "summary": "FlowCF replaces gradient-based and amortized model-agnostic counterfactual generators (e.g., Wachter-style optimization or VAE/flow baselines) with a flow-matching transport from factual to target class, extended to mixed feature types and regularized for sparsity via a gating network. The cost is training a conditional flow-matching generative model plus gating network per dataset, which adds a training stage and hyperparameter burden beyond what query-only CF optimizers require, though inference afterward is amortized. It can fail when the learned flow does not cover the data manifold well (producing implausible or out-of-distribution counterfactuals), when dataset shift invalidates the trained generator, or on datasets too small to train the flow reliably.",
  "rationale": "Counterfactual explanation generation is an XAI task rather than an inference- or training-efficiency technique, so it is peripheral to the reader's production concerns, but it is trainable on a single 24 GB GPU for typical tabular benchmarks and may be worth a scoped evaluation if the reader ships tabular models requiring explanations.",
  "technique": "Flow-matching generative counterfactual explanations for tabular data",
  "family": "outro",
  "recommendation": "testar",
  "published": "2026-10-06",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.08537",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "FlowCF replaces gradient-based and amortized model-agnostic counterfactual generators (e.g., Wachter-style optimization or VAE/flow baselines) with a flow-matching transport from factual to target class, extended to mixed feature types and regularized for sparsity via a gating network. The cost is training a conditional flow-matching generative model plus gating network per dataset, which adds a training stage and hyperparameter burden beyond what query-only CF optimizers require, though inference afterward is amortized. It can fail when the learned flow does not cover the data manifold well (producing implausible or out-of-distribution counterfactuals), when dataset shift invalidates the trained generator, or on datasets too small to train the flow reliably.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.08537",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Produces the best numerical sparsity and proximity among compared methods: changes 29% of numerical features versus 89% for the best baseline, at 70% smaller displacement, while remaining comparable on other desiderata across six benchmark datasets.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.08537",
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    }
  ],
  "exposure_map": [
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      "dimension": "compute",
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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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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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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": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}