{
  "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": "2609.39449",
  "title": "Distributionally robust linear regression through the lens of adversarial training",
  "summary": "The method replaces standard least-squares or Lasso linear regression with a Wasserstein distributionally robust estimator that subsumes square-root Lasso and adversarial linear regression as special cases. The paper claims efficient solvability, so computational cost is comparable to classical regularized regression, but adoption requires reimplementing or integrating the estimator and validating the ambiguity-set radius on real data. It can fail if the assumed linear model is wrong for the workload, if the ambiguity radius is miscalibrated, or if the theoretical guarantees (O(n^{-1/2}) error bounds, pivotal property) do not translate into predictive gains on non-synthetic data; only numerical simulations are reported.",
  "rationale": "This is a statistical theory paper on robust linear regression with no demonstrated production system gains, so it is only relevant to the reader if their workload includes tabular regression under distribution shift.",
  "technique": "Wasserstein DRO linear regression unifying square-root Lasso and adversarial regression",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.39449",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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    }
  ],
  "exposure_map": [
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      "dimension": "compatibility",
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    },
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      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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      "dimension": "reproducibility",
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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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}