{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A support or data assistant repeats work across related customer requests.",
    "change": "Try the paper's memory change in a replay of past tasks, with separate memory per customer.",
    "baseline": "Your current memory pipeline plus a no-memory control using the same agent.",
    "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, stale memories, retrieval misses, response latency, and all memory maintenance costs.",
    "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": "2610.20456",
  "title": "The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation",
  "summary": "COUNT replaces learned n-gram embedding tables (trained from scratch on GPU) with precomputed corpus n-gram counts stored in host memory, which weight the LM's own unembedding rows to form embeddings with no additional learned parameters. It costs host RAM for the count table, preprocessing to build it, and an added embedding lookup per token; the benefit only materializes if the backbone is pretrained with COUNT as an input. It can fail for the reader because the gains are demonstrated only for models pretrained from scratch with the method, and no compatible pretrained checkpoint may exist; count tables can also be stale or mismatched to the target domain, degrading the training-free adaptation.",
  "rationale": "The core gain requires pretraining a backbone with COUNT, which exceeds the reader's infrastructure, but the inference-time count-table swap for domain adaptation is cheap enough to validate if a compatible model becomes available.",
  "technique": "Host-resident count-weighted n-gram embeddings (COUNT)",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20456",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "COUNT replaces learned n-gram embedding tables (trained from scratch on GPU) with precomputed corpus n-gram counts stored in host memory, which weight the LM's own unembedding rows to form embeddings with no additional learned parameters. It costs host RAM for the count table, preprocessing to build it, and an added embedding lookup per token; the benefit only materializes if the backbone is pretrained with COUNT as an input. It can fail for the reader because the gains are demonstrated only for models pretrained from scratch with the method, and no compatible pretrained checkpoint may exist; count tables can also be stale or mismatched to the target domain, degrading the training-free adaptation.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.20456",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Adding COUNT as an input embedding during 1B-parameter pretraining improves validation loss as much as an LM trained with 57% more compute, and under GPU memory limits it outperforms splitting memory with learned n-gram embeddings.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.20456",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
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    },
    {
      "dimension": "compute",
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      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": ""
}