{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A multi-step product task fails because the agent loses track of dependencies.",
    "change": "Try the proposed planning step on tasks with checkable intermediate outcomes.",
    "baseline": "The same agent solving the same tasks without the new planning step.",
    "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 completion, unnecessary steps, planning tokens, response time, and total cost.",
    "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": "2609.26532",
  "title": "REFLEX with Jev for Efficient Selective Control in LLM Agents",
  "summary": "Replaces unconditional strong-LLM invocation for bounded agent decisions with a fast, typed decision layer (Jev) that escalates to a strong model only on low confidence or when generation is required. Costs include maintaining the typed decision layer, confidence calibration, and fallback routing logic, while task success on the internal benchmark is preserved at 95%. It can fail when action sets are large or near-valid alternatives cluster near authorization boundaries, and it offers little gain where an ordinary cheap generative router is already highly accurate.",
  "rationale": "The cost reduction is substantial and compatible with constrained infrastructure, but the paper's own external evaluations show the advantage over a simple cheap-model cascade is workload-dependent, so adoption should follow validation on the reader's specific routing accuracy and action-set structure.",
  "technique": "Typed decision layer with selective LLM fallback",
  "family": "planejamento_e_decomposicao",
  "recommendation": "testar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.26532",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 137,
  "citations": 0,
  "claims": [
    {
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      "source_url": "https://arxiv.org/abs/2609.26532",
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      "dimension": "latency",
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      "dimension": "operations",
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    {
      "dimension": "compatibility",
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    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    {
      "dimension": "reproducibility",
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  "risks": [],
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  "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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