{
  "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": "2610.26100",
  "title": "Multilingual Voicemail and Answering Machine Detection via Pretrained Audio Embeddings and Gradient Boosting Machines",
  "summary": "The method replaces proprietary answering machine detection systems and computationally expensive real-time models (including ASR-plus-LLM pipelines) with frozen pretrained audio embeddings feeding a gradient boosting classifier. Cost is low: no GPU or fine-tuning is required for inference, training reduces to fitting a GBM on extracted embeddings, and the authors release a 500+ recording, 7-language dataset (OpenAMD) for replication. What can fail: the dataset is small for a production telephony system, per-language and per-gender performance differences were found statistically significant, and robustness degrades under additive noise and packet loss typical of real carrier networks.",
  "rationale": "The approach fits constrained infrastructure (CPU-class inference, no model training beyond a GBM) but its small evaluation dataset and demonstrated sensitivity to language, gender, noise, and packet loss require validation on the reader's own call traffic before production adoption.",
  "technique": "Pretrained audio embeddings with gradient boosting for AMD",
  "family": "outro",
  "recommendation": "testar",
  "published": "2026-10-19",
  "as_of": "2026-10-20",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.26100",
  "independent_implementations": 2,
  "total_implementations": 2,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.26100",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Best model achieves the highest precision across two datasets with low inference latency, and 94.34% recall on out-of-domain robocall detection without additional training; no multiplicative improvement factor is reported.",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "source_page": null,
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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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    },
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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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}