{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A narrow, repeated product task may not need your largest model.",
    "change": "Check whether a released student model fits the task before generating training data.",
    "baseline": "Your current model and an off-the-shelf small model 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": "Student quality, serving cost, teacher calls, training cost, and failures on unusual inputs.",
    "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": "2609.29194",
  "title": "Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Models",
  "summary": "The method replaces running a heavy foundation time-series anomaly detector (TSPulse) onboard by distilling its pseudo-labels into a lightweight MiniRocket student with a Recursive Least Squares head for online adaptation. Cost: an offline teacher-labeling pipeline with fault injection, plus an uncertainty-guided active learning loop requiring occasional operator labeling; inference is cheap (4.30 ms CPU). What can fail: pseudo-label errors from the teacher propagate to the student, RLS adaptation may drift on poorly calibrated uncertainty, and results are validated on one robot platform and one benchmark, so transfer to other sensor suites is unproven.",
  "rationale": "The pattern is feasible on constrained infrastructure since the expensive teacher runs offline and the student is CPU-cheap, but the reader must validate teacher label quality and adaptation stability on their own telemetry before production adoption.",
  "technique": "Teacher-student distillation with online RLS adaptation",
  "family": "destilacao",
  "recommendation": "testar",
  "published": "2026-09-24",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.29194",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": 0,
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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