{
  "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": "2610.29320",
  "title": "BrainCodes: A Lightweight EEG Foundation Model over Discrete Neural Codes",
  "summary": "BrainCodes replaces large EEG foundation models with a compact architecture combining a quantization-based neural tokenizer and a spatiotemporal encoder supporting arbitrary electrode configurations, pretrained self-supervised on 800+ hours of EEG. The cost is domain specificity: it applies only to EEG pipelines and requires access to EEG data and task-specific fine-tuning, offering nothing for text or general multimodal workloads. Failure modes include degraded transfer to electrode montages or recording conditions outside the pretraining distribution and the general risk that an unreviewed arXiv preprint's cross-device claims do not hold on the reader's hardware or subjects.",
  "rationale": "The method is confined to EEG signal processing and has no bearing on the reader's LLM-oriented production infrastructure unless they operate a neurophysiology product.",
  "technique": "Lightweight quantized-tokenizer EEG foundation model",
  "family": "outro",
  "recommendation": "nao_aplica",
  "published": "2026-10-21",
  "as_of": "2026-10-22",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.29320",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 4,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.29320",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Uses up to 97% fewer trainable parameters than comparable architectures while matching or surpassing state of the art on sleep staging, attention assessment, and cognitive load estimation.",
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      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.29320",
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    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}