{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time 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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "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": "2609.18135",
  "title": "DualSQL: Text-to-SQL with Multi-Agent Reinforcement Learning",
  "summary": "DualSQL replaces the standard practice of training separate models for schema linking and SQL generation with two agents sharing one backbone, jointly optimized via multi-agent reinforcement learning with database access tools, rollout guardrails, and a new REX correctness metric for reward assignment. The cost is a full RL training pipeline: rollouts against live databases, reward computation via execution, guardrail machinery to prevent model collapse, and the engineering complexity of maintaining the agentic scaffold, none of which is an inference-time or API-level adoption. Failure modes include training instability and model collapse despite guardrails, reward misspecification from the REX metric, and database-dependent rollout costs that make iteration slow and expensive.",
  "rationale": "The reported gains require multi-agent RL fine-tuning infrastructure with database-grounded rollouts, which exceeds the reader's compute and budget even at 4B scale, so the method is worth tracking but not yet adoptable in their production practice.",
  "technique": "Single-backbone multi-agent RL for Text-to-SQL",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18135",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
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      "dimension": "compatibility",
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  "report_available": false,
  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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