{
  "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": "2610.20926",
  "title": "SkillNEO: Non-Elitist Evolutionary Optimization of Agent Skills",
  "summary": "It replaces greedy, elitist skill refinement (keep only edits that immediately improve reward) with an evolutionary process that separately selects the version to deploy and lower-scoring versions worth further editing. The cost is additional execution and revision rollouts via API calls (task execution, revision proposal, and skill editing), plus infrastructure for versioned skill storage and comparative trace analysis. It can fail when evaluation tasks overlap with evolution tasks (the paper evaluates on the same instances, risking overfitting), when partial-repair lineages consume budget without converging, or when skills evolved on a weak model encode workarounds that mislead stronger models.",
  "rationale": "The approach is API-feasible for a small team and shows cross-model transfer, but its gains are demonstrated on one benchmark with train/test overlap, so it requires validation on the reader's own tasks before production adoption.",
  "technique": "Non-elitist evolutionary optimization of agent skills",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-14",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.20926",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 132,
  "citations": 0,
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    {
      "claim_id": "claim-01",
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    {
      "claim_id": "claim-02",
      "statement": "Raises GPT-5.4 Mini's average reward on 86 SkillsBench tasks from 49.1% to 66.4%; evolved skills transfer to stronger models, improving Luna by 10.5 points on the 41 modified-skill tasks.",
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      "dimension": "compatibility",
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      "dimension": "security",
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  "risks": [],
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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  "equations_fetched_at": "",
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}