{
  "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": "2610.02150",
  "title": "From Knowledge Access to Source Learning: Developing Source-Specific Competence",
  "summary": "It replaces treating repeated queries to a persistent authoritative source as independent retrieval events (static RAG indices or generic agent memory) with a progressively refined, source-specific model built via self-directed gap identification and task-guided updates reconstructed from the source. It costs additional LLM calls for the self-directed learning loop and task-guided refinement, plus storage and maintenance of a persistent per-source model, adding latency and pipeline complexity relative to a single retrieval pass. It can fail when the source changes without triggering re-learning, when the workload does not repeatedly reuse the same source (amortizing the learning cost), or when reconstructed updates drift from the authoritative content and encode errors that persist across tasks.",
  "rationale": "The method is implementable with a single GPU or API backends and shows measurable quality gains over RAG baselines, but its benefit depends on having a workload with repeated access to a persistent source, so the reader should validate the amortized cost on their own task distribution before production adoption.",
  "technique": "Persistent source-specific competence models (SourceLearn)",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.02150",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 121,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "It replaces treating repeated queries to a persistent authoritative source as independent retrieval events (static RAG indices or generic agent memory) with a progressively refined, source-specific model built via self-directed gap identification and task-guided updates reconstructed from the source. It costs additional LLM calls for the self-directed learning loop and task-guided refinement, plus storage and maintenance of a persistent per-source model, adding latency and pipeline complexity relative to a single retrieval pass. It can fail when the source changes without triggering re-learning, when the workload does not repeatedly reuse the same source (amortizing the learning cost), or when reconstructed updates drift from the authoritative content and encode errors that persist across tasks.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.02150",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Best performance in 13 of 15 settings across five benchmarks and three LLM backends, with gains of up to 22.6 points over Hybrid RAG and improvements over static source representations and experience-based memory baselines.",
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      "source_url": "https://arxiv.org/abs/2610.02150",
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    }
  ],
  "exposure_map": [
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      "dimension": "compute",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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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": "",
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}