{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An assistant chooses the wrong API or sends arguments your product cannot use.",
    "change": "Apply the proposed tool-selection or argument-generation change in a sandbox.",
    "baseline": "Your current agent with the same tool schemas and task budget.",
    "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": "Correct completed actions, invalid calls, retries, latency, and cost per completed task.",
    "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": "2609.38201",
  "title": "TomasuLLM: Out-of-Order Speculative Execution for LLM Agents",
  "summary": "It replaces the sequential tool-call loop of a coding agent, in which the model idles while long-running tools execute, with a runtime that drafts future actions, executes them in copy-on-write sandboxes, and commits results in trajectory order after validation. It costs sandboxing and dependency/effect-tracing infrastructure, extra compute for speculative executions that are later invalidated, and substantial engineering complexity beyond a single-GPU deployment. It can fail when drafted actions diverge from the actual trajectory, making wasted speculative runs offset the latency gains on workloads with short or unpredictable tool calls.",
  "rationale": "The speedup is real but modest and depends on a correctness-critical sandboxing and commit-validation runtime that a small team with constrained infrastructure would have to build and maintain, so it is worth tracking rather than adopting now.",
  "technique": "Out-of-order speculative tool execution for agents",
  "family": "uso_de_ferramenta",
  "recommendation": "observar",
  "published": "2026-09-22",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.38201",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
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      "claim_id": "claim-01",
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      "claim_id": "claim-02",
      "statement": "1.31x on 100 SWE-bench Verified tasks, 1.35x on 28 Terminal-Bench 2.0 tasks, and 1.27x matched progress on 18 SWE-Marathon sessions; zero false accepts across 4,010 commit-validation records.",
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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",
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