{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "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": "Successful outcomes, response time, human rework, and total cost per successful 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": "2610.12655",
  "title": "CLEAT: Learning Value-Guided Clarification for Interactive Agents",
  "summary": "CLEAT replaces heuristic or fixed prompting strategies for deciding when an agent should ask clarifying questions with a learned controller and task policy co-trained via paired rollouts that estimate residual query value. It costs an RL training pipeline with paired rollout collection from identical interaction histories, iterative controller-guided data generation, and retraining as the policy evolves, which exceeds typical 24 GB single-GPU and small-budget constraints even at 4B scale. After adoption, value estimates can become stale or biased as the policy shifts, clarification behavior may overfit the training benchmarks' underspecification patterns, and the controller can mispredict query value on real users whose answers differ from simulated interaction outcomes.",
  "rationale": "The method requires iterative reinforcement-learning co-training with paired rollouts, which demands training infrastructure and data-collection pipelines beyond the reader's constrained setup, so it merits monitoring rather than immediate adoption.",
  "technique": "Value-guided clarification co-training for agents",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.12655",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 136,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "CLEAT replaces heuristic or fixed prompting strategies for deciding when an agent should ask clarifying questions with a learned controller and task policy co-trained via paired rollouts that estimate residual query value. It costs an RL training pipeline with paired rollout collection from identical interaction histories, iterative controller-guided data generation, and retraining as the policy evolves, which exceeds typical 24 GB single-GPU and small-budget constraints even at 4B scale. After adoption, value estimates can become stale or biased as the policy shifts, clarification behavior may overfit the training benchmarks' underspecification patterns, and the controller can mispredict query value on real users whose answers differ from simulated interaction outcomes.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.12655",
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      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "With Qwen3-4B, CLEAT achieves the highest mean score in all ten evaluation settings across four benchmarks, exceeding the strongest baseline by 13.2 points on UserGym Travel and 6.5 points on IN3 underspecification judgment accuracy.",
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      "dimension": "latency",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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    }
  ],
  "risks": [],
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  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
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}