{
  "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": "2609.23907",
  "title": "A discrete generative model of neuronal spiking activity on microelectrode arrays",
  "summary": "The method replaces assay-specific sorted-neuron models and flat tokenizers for microelectrode-array spike data with a shared residual vector-quantized motif vocabulary and a factorized masked transformer. Adoption costs training a VQ-VAE and transformer on sparse multi-electrode recordings and maintaining a preprocessing pipeline for array-wide binary spike volumes, none of which intersects LLM inference or agent workloads. It can fail when electrode subsets or tissue dynamics shift across assays, since motif reuse is statistical rather than guaranteed, and the reported gains are specific to neural recordings.",
  "rationale": "This is a domain-specific generative model for neuroscience spike data with no applicability to the reader's LLM production constraints.",
  "technique": "RVQ autoencoder plus masked transformer for neural spike data",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-09-20",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.23907",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
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    {
      "dimension": "compatibility",
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    {
      "dimension": "security",
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    {
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "open_questions": [],
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
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