{
  "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": "2610.26233",
  "title": "Heartbeat Thinking: Speculative Reasoning and Tool Execution While Listening in Realtime Voice Agents",
  "summary": "It replaces chunk-bounded speculative execution, where generation is truncated or restarted at each speech update, with periodic Thinker invocations that let ongoing reasoning and dispatched tool calls run to completion against the latest partial transcript and a shared results record. The cost is additional inference calls during listening (raising token/API spend), plus orchestration complexity for managing concurrent invocations and a shared state record. It can fail when partial transcripts mislead speculative tool calls into wasted or incorrect executions, and the paper notes final task quality effects are benchmark-dependent, so gains may not transfer to a given workload.",
  "rationale": "The latency and tool-coverage gains are meaningful for API-based voice agents on constrained infrastructure, but the benchmark-dependent quality effects and speculative token overhead require validation on the reader's specific workload before production adoption.",
  "technique": "Heartbeat speculative reasoning during speech",
  "family": "uso_de_ferramenta",
  "recommendation": "testar",
  "published": "2026-10-19",
  "as_of": "2026-10-20",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.26233",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
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      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.26233",
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    }
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      "dimension": "compatibility",
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      "dimension": "data_and_training",
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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": "",
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}