{
  "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": "2610.18861",
  "title": "Hybrid Reasoning Agents",
  "summary": "Replaces purely text-based chain-of-thought and tool interaction in LLM agents with a unified generation process that alternates between latent reasoning (via a HybridLink module and continuous working memory) and text-based tool calls, reducing tokens and latency. Costs include training the HybridLink plus two post-training stages (HybridSFT and HybridRL), added architectural complexity over standard prompting or ReAct-style loops, and dependence on a custom serving path rather than off-the-shelf API inference. Failure modes include coordination errors at latent-text transition points, degraded interpretability if latent thoughts are inspected only superficially, and results that may not transfer when applied to backbones or tool schemas different from those used in the paper's training.",
  "rationale": "The efficiency and quality gains are relevant to a constrained-infrastructure team, but the method requires dedicated SFT and RL post-training plus custom inference plumbing, so adoption should follow a small-scale reproduction on the reader's own tool-use workload before any production commitment.",
  "technique": "Hybrid latent-text reasoning with tool integration",
  "family": "uso_de_ferramenta",
  "recommendation": "testar",
  "published": "2026-10-13",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.18861",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 20,
  "citations": null,
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      "dimension": "compatibility",
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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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