{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your workload might fit a smaller or differently structured model.",
    "change": "Check for available weights and a compatible runtime before planning a product trial.",
    "baseline": "Your current model on identical held-out tasks and the same resource budget.",
    "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": "Task success, latency, memory, context limits, and migration cost.",
    "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": "2610.28495",
  "title": "Model- and Layer-Level Closed-Loop Forecasting with SSM-Based Spiking Neural Networks",
  "summary": "The method replaces zero-masking or external autoregressive feed-in during the forecasting phase by feeding the network's own predictions back as inputs, at either model level or layer level. The cost is added architectural and training complexity in a spiking/Deep-SSM stack with limited tooling, plus a quality trade-off between continuous and low-bit quantized spike activations. It can fail because results are demonstrated only on the small ETT-small benchmark at parity with existing methods, so there is no evidence of advantage on larger, noisier production forecasting workloads.",
  "rationale": "The paper shows only parity with established baselines on a small academic benchmark, so adoption is unjustified while the SNN approach remains worth monitoring for its potential energy and quantization benefits.",
  "technique": "Closed-loop forecasting with SSM-based spiking neural networks",
  "family": "arquitetura_eficiente",
  "recommendation": "observar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28495",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "dimension": "security",
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      "source_page": null,
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations": [],
  "equations_status": "not_fetched",
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