{
  "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": "2610.08977",
  "title": "SNR-Gated LSTM-Conditioned Diffusion Model for MIMO Channel Estimation",
  "summary": "The method replaces per-snapshot least-squares and prior diffusion-based channel estimators for MIMO wireless systems with a temporally conditioned diffusion denoiser using an LSTM encoder, an SNR-gated fusion shortcut, and SNR-adaptive DDIM truncation. It costs the training of a domain-specific diffusion model on channel data, added inference complexity relative to classical estimators, and tuning of SNR-adaptive step schedules, with the abstract reporting no quantified improvement figures. It can fail under channel statistics or mobility patterns outside the training distribution, and its standardized-model simulations give no evidence of transfer to measured channels or to any task the reader operates.",
  "rationale": "This is a wireless communications signal-processing paper with no connection to LLM inference, agent systems, or any production workload of an AI/ML engineer with constrained infrastructure.",
  "technique": "SNR-gated LSTM-conditioned diffusion channel estimation",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-06",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.08977",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "source_url": "https://arxiv.org/abs/2610.08977",
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      "dimension": "security",
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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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}