{
  "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": "2610.23599",
  "title": "From Trajectories to Differentiated Skills: Cognitive Differentiation for Self-Improving Agents",
  "summary": "CDSE replaces monolithic skill artifacts in self-improving agents with a unified skill split into four coordinated components (Meta, Experience, Tool, Script), updated separately from semantic gradients extracted from successful and failed trajectories. The cost is a more complex skill-maintenance pipeline: component-specific extraction, consolidation, validation, cross-component coordination, and adaptive invocation logic, plus additional LLM calls for trajectory analysis and update generation. It can fail if trajectory evidence is sparse or unrepresentative, if component updates conflict despite coordination, or if the benchmark gains do not transfer to the reader's narrower production task distribution.",
  "rationale": "The method requires no model training and works with API-based agents, fitting constrained infrastructure, but its 2.6-point margin over existing skill-evolution methods is benchmark-reported and must be validated against the reader's actual workload before production adoption.",
  "technique": "Functionally differentiated skill evolution (CDSE)",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-16",
  "as_of": "2026-10-19",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.23599",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 132,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.23599",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Highest scores in all 36 settings across six benchmarks, three agent models, and two harnesses: +7.6 percentage points over the Base agent and +2.6 points over the strongest competing method on average.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.23599",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "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",
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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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}