{
  "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": "2609.28438",
  "title": "Minimal-Norm Univariate Two-Layer ReLU Classification: Exact Solutions and Global Optimality with Skip Connections",
  "summary": "The paper characterizes exact minimal-norm and weakly regularized solutions for univariate two-layer ReLU classifiers and shows a free affine skip connection makes every KKT point globally optimal; it replaces nothing in production practice, as it is a theoretical analysis rather than a deployable method. It costs nothing to adopt because there is no algorithm to implement, but the skip-connection insight would require revalidation in multivariate deep networks before influencing architecture choices. The results can fail to transfer because they are restricted to one-dimensional inputs and exact geometric settings, and the paper itself notes that suboptimal KKT points and sparsity-limit phenomena depend on bias-penalty choices that differ from standard practice.",
  "rationale": "This is a univariate theoretical result with no production-ready method, quantified performance gain, or relevance to constrained-infrastructure deployment of practical models.",
  "technique": "Minimal-norm univariate ReLU classification theory with skip connections",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.28438",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The paper characterizes exact minimal-norm and weakly regularized solutions for univariate two-layer ReLU classifiers and shows a free affine skip connection makes every KKT point globally optimal; it replaces nothing in production practice, as it is a theoretical analysis rather than a deployable method. It costs nothing to adopt because there is no algorithm to implement, but the skip-connection insight would require revalidation in multivariate deep networks before influencing architecture choices. The results can fail to transfer because they are restricted to one-dimensional inputs and exact geometric settings, and the paper itself notes that suboptimal KKT points and sparsity-limit phenomena depend on bias-penalty choices that differ from standard practice.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.28438",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
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      "finding": "",
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      "next_check": "",
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    },
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      "dimension": "security",
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      "source_page": null,
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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": "",
  "exposures_reviewed_at": "",
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}