{
  "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": "2610.11906",
  "title": "RobustLDS: Learning linear dynamical systems under adversarial corruptions",
  "summary": "The method replaces ordinary least-squares identification of linear dynamical systems with least-trimmed-squares relaxations, alternating minimization, and group-sparse outlier penalization estimators that tolerate adversarially corrupted observations from a single trajectory. Its cost is added estimator complexity and iterative optimization over a nonconvex problem, with only the group-sparse-penalty variant carrying non-asymptotic error guarantees; no inference-time or memory overhead figures are reported. It can fail if the assumed linear dynamics or outlier-fraction regime does not hold, since the guarantees are tied to specific contamination and sample-complexity conditions, and the abstract reports only that the estimators 'work well in practice' without quantified results.",
  "rationale": "This is a system-identification method for classical control and time-series estimation, not a technique applicable to LLM training, inference, serving, or agent production under the reader's constrained infrastructure.",
  "technique": "Robust linear dynamical system identification under adversarial outliers",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11906",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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  "risks": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
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}