{
  "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": "2610.28930",
  "title": "Amplified Pruning and Path Selectors for Deterministic Radio Gossiping",
  "summary": "The method replaces prior deterministic radio-network gossip protocols based on Wu and Chrobak-style path selection with amplified reception certificates, adaptive pruning, and permutation selectors. Its cost is implementation complexity and performance bounds stated only as asymptotic round complexity under graph-class and degree-concentration assumptions, not deployable ML infrastructure. It can fail outside the assumed strongly connected directed radio-network setting, polynomial identifier range, collision model, and degree-concentration conditions, and the unrestricted general case remains open.",
  "rationale": "This is distributed computing theory for radio networks, not a production technique applicable to the reader's constrained AI/ML deployment setting.",
  "technique": "Deterministic radio gossiping with amplified pruning",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-20",
  "as_of": "2026-10-24",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28930",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces prior deterministic radio-network gossip protocols based on Wu and Chrobak-style path selection with amplified reception certificates, adaptive pruning, and permutation selectors. Its cost is implementation complexity and performance bounds stated only as asymptotic round complexity under graph-class and degree-concentration assumptions, not deployable ML infrastructure. It can fail outside the assumed strongly connected directed radio-network setting, polynomial identifier range, collision model, and degree-concentration conditions, and the unrestricted general case remains open.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.28930",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Improves asymptotic round complexity for deterministic gossiping in unknown directed radio networks, including O~(min{n^(4/3), N+(mn)^(3/5)}) rounds for general graphs within the stated classes and O~(min{n*sqrt(Delta), n^2/Delta}) for directed regular graphs.",
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      "source_url": "https://arxiv.org/abs/2610.28930",
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    }
  ],
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
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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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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "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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}