{
  "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": "2610.01892",
  "title": "Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents",
  "summary": "The method replaces free-form chain-of-thought generation before each agent action with selection among pre-specified, reusable natural-language reasoning candidates, scored by likelihood via teacher-forced prefilling on a shared KV cache. The cost is upfront design of the reasoning candidate set plus SFT or RL training to make the model select reliably; no extra task head or hardware is required, and parallel scoring is compatible with a single 24 GB GPU serving small models. It can fail when the task requires reasoning outside the fixed candidate set, when candidates are poorly designed for the domain, or when likelihood-based selection is miscalibrated, in which case action quality degrades relative to generative reasoning.",
  "rationale": "The latency gains are large and infrastructure-compatible, but adoption requires authoring domain-specific reasoning candidates and retraining, so the reader should validate on their own agent workload before committing.",
  "technique": "Selection-based structured reasoning for multimodal agents",
  "family": "planejamento_e_decomposicao",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01892",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "statement": "Over 90% reduction in per-turn reasoning latency (a >10x factor on that component) and 28-54% reduction in total per-question model inference latency, with success rates competitive with same-scale search agents on seven multimodal search benchmarks using 2B and 4B models.",
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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",
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  "equations": [],
  "equations_status": "not_fetched",
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