{
  "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": "2610.10666",
  "title": "Explaining the Saliency Map Sparsity of Adversarially-Trained Neural Networks",
  "summary": "This paper replaces nothing: it provides a theoretical explanation, for two-layer ReLU networks, of why adversarially trained models produce sparse gradient saliency maps, showing convergence to a minimal-gradient, minimal-Barron-norm Bayes classifier with anisotropic l-infinity geometry favoring axis-aligned gradients. There is no production cost because there is no deployable method; the experimental component only measures gradient l1-norm and thresholded sparsity to illustrate the theory. The only adoption risk is misreading an explanatory result about a toy architecture as guidance for production interpretability or robustness tooling.",
  "rationale": "The work is a theoretical analysis of an empirical phenomenon with no technique, benchmark, or efficiency claim relevant to production adoption under constrained infrastructure.",
  "technique": "Theory of saliency sparsity in adversarial training",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.10666",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "claim_id": "claim-01",
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations": [],
  "equations_status": "not_fetched",
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}