{
  "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": "2609.25286",
  "title": "Learned Enterprise Data Comprehension: Compression and Routing for Data Agents",
  "summary": "The method replaces reusable markdown-style memory and skill files with learned Gaussian prototypes and soft-membership profiles that compress recurring schema and evidential structure, plus a learned query-to-prototype routing layer that materializes relevant evidence per query. The cost is training and maintaining the learned prototype and compatibility components, storing per-environment prototypes and membership profiles, and added pipeline complexity beyond prompt-level memory files. It can fail when query distributions or schemas drift after prototypes are learned, when a deployment environment lacks the recurring structure the prototypes assume, or when the benchmark's gains do not transfer to the reader's own enterprise datasets.",
  "rationale": "The approach is implementable on modest infrastructure since the learned components are small prototypes rather than foundation models, but its large benchmark gain is measured on a single 54-query benchmark and requires validation against the reader's own data environments before production adoption.",
  "technique": "Learned prototype routing for structured-data agents",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.25286",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces reusable markdown-style memory and skill files with learned Gaussian prototypes and soft-membership profiles that compress recurring schema and evidential structure, plus a learned query-to-prototype routing layer that materializes relevant evidence per query. The cost is training and maintaining the learned prototype and compatibility components, storing per-environment prototypes and membership profiles, and added pipeline complexity beyond prompt-level memory files. It can fail when query distributions or schemas drift after prototypes are learned, when a deployment environment lacks the recurring structure the prototypes assume, or when the benchmark's gains do not transfer to the reader's own enterprise datasets.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.25286",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "94.67% dataset-macro Pass@1 vs 55.51% for the Claude Opus 4.6 reference agent on the Data Agent Benchmark (54 queries, 12 datasets); this is a 39-point absolute difference, not a multiplicative factor.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.25286",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "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,
      "source_excerpt": ""
    },
    {
      "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": ""
}