{
  "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": "2610.00437",
  "title": "JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces",
  "summary": "The method replaces token-by-token generation of agent reasoning and actions with probabilistic prediction over action fields that are spawned, selected, and revised compositionally from natural language instructions. It costs implementation complexity in the spawning, branch-selection, and recovery machinery, and depends on retained alternatives and shared prefixes whose memory overhead is unspecified. It can fail when the derived action fields are poorly matched to the task, since the finite-field foundation limits exploration to actions the spawning step actually proposes, and results from a single paper without independent replication carry adoption risk.",
  "rationale": "The approach targets exactly the reader's latency-and-cost pain in agentic inference and claims to require no additional training, but the unquantified gains and unspecified engineering overhead mean it must be validated on the reader's own workloads before production adoption.",
  "technique": "Compositional action spawning for agentic inference",
  "family": "planejamento_e_decomposicao",
  "recommendation": "testar",
  "published": "2026-09-30",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.00437",
  "independent_implementations": 4,
  "total_implementations": 5,
  "stars": 134,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "source_url": "https://arxiv.org/abs/2610.00437",
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    },
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      "source_url": "https://arxiv.org/abs/2610.00437",
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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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}