{
  "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": "2610.21402",
  "title": "Fault Detection and Bit-Error Rate Diagnostics in Optical Wireless Links Using Edge-AI",
  "summary": "The method replaces cloud- or external-node-based channel diagnostics for optical wireless links with on-device INT8-quantized inference on an ESP32-S3 microcontroller. It costs roughly 3 to 4 percentage points of accuracy relative to floating-point baselines and requires per-node embedded firmware integration and telemetry feature extraction. It can fail on conditions outside the four trained classes, on distribution shift in channel telemetry, and through the demonstrated quantization-induced accuracy loss on a task that is already below 80% accuracy.",
  "rationale": "This is microcontroller TinyML for optical communication hardware, outside the reader's GPU/API-based LLM production scope and offering no transferable gain for that workload.",
  "technique": "On-device TinyML fault classification for optical links",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21402",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces cloud- or external-node-based channel diagnostics for optical wireless links with on-device INT8-quantized inference on an ESP32-S3 microcontroller. It costs roughly 3 to 4 percentage points of accuracy relative to floating-point baselines and requires per-node embedded firmware integration and telemetry feature extraction. It can fail on conditions outside the four trained classes, on distribution shift in channel telemetry, and through the demonstrated quantization-induced accuracy loss on a task that is already below 80% accuracy.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21402",
      "source_page": null,
      "source_excerpt": ""
    },
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      "claim_id": "claim-02",
      "statement": "Post-training INT8 quantized TensorFlow Lite Micro model occupies 3.32 kB of SRAM and achieves 74.68% four-class accuracy, versus 77.22% for a Random Forest and 78.48% for a floating-point neural network.",
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      "source_url": "https://arxiv.org/abs/2610.21402",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "latency",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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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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      "next_check": "",
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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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}