{
  "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": "2610.20399",
  "title": "Cortex: Consolidating Context into Parametric Memory",
  "summary": "The method replaces repeatedly supplying context at inference time (long prompts, retrieval, or ad-hoc fine-tuning) with offline distillation of context-conditioned behavior into mergeable low-rank or structural FFN weight updates, using prospective query generation to anticipate future uses of the context. It costs an offline training pipeline per context or edit batch (synthetic query generation, distillation passes, update merging and validation), which fits a single 24 GB GPU only for smaller backbones or via parameter-efficient setups, and it adds operational complexity for update versioning and rollback. It can fail when prospective queries do not cover actual downstream usage, when sequential updates accumulate interference and degrade non-target capabilities beyond the reported retention, and because the reported Pareto-frontier results come from the authors' own five-task testbed rather than independent production workloads.",
  "rationale": "The idea is directly relevant to production context persistence, but adopting it requires building a distillation-and-merge pipeline whose benefits are validated only on the authors' testbed, so it should be tracked and prototyped rather than adopted now.",
  "technique": "Consolidating context into parametric low-rank/FFN memory",
  "family": "memoria_e_contexto",
  "recommendation": "observar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20399",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces repeatedly supplying context at inference time (long prompts, retrieval, or ad-hoc fine-tuning) with offline distillation of context-conditioned behavior into mergeable low-rank or structural FFN weight updates, using prospective query generation to anticipate future uses of the context. It costs an offline training pipeline per context or edit batch (synthetic query generation, distillation passes, update merging and validation), which fits a single 24 GB GPU only for smaller backbones or via parameter-efficient setups, and it adds operational complexity for update versioning and rollback. It can fail when prospective queries do not cover actual downstream usage, when sequential updates accumulate interference and degrade non-target capabilities beyond the reported retention, and because the reported Pareto-frontier results come from the authors' own five-task testbed rather than independent production workloads.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.20399",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "After 50 sequential factual edits on a 14B model, Cortex-Structural recovers 88.9% of the context-induced gain while retaining 99.8% of original non-target performance; results are percentage recoveries, not a multiplicative factor.",
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      "source_url": "https://arxiv.org/abs/2610.20399",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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    },
    {
      "dimension": "compute",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "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": "",
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  "exposures_source_sha256": ""
}