{
  "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": "2609.35738",
  "title": "Harness Learning Enables Generalizable Test-Time Adaptation",
  "summary": "The method replaces manual, per-task engineering of agent harnesses (the code orchestrating model calls, tools, and information flow) with a proposer model trained via reinforcement learning to rewrite the harness using execution feedback, framed as meta-learning where program revisions substitute for gradient updates. Adoption costs an RL training pipeline over executable programs, a reward signal derived from task performance, execution traces for feedback, and ongoing inference for the proposer at test time, none of which fits a limited cloud budget without an existing evaluation and training infrastructure. It can fail when the reward is sparse or noisy, when learned revision policies overfit the training task distribution, and when multi-round revision degrades rather than improves the harness, a failure mode the authors note varies across settings.",
  "rationale": "The approach requires reinforcement-learning infrastructure, execution-feedback reward pipelines, and repeated solver executions that exceed a constrained 24 GB or API-only budget, and the abstract reports no quantified production gain that would justify that investment now.",
  "technique": "RL-trained harness revision for test-time adaptation",
  "family": "uso_de_ferramenta",
  "recommendation": "observar",
  "published": "2026-09-28",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.35738",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 1,
  "citations": null,
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      "dimension": "compatibility",
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      "dimension": "security",
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    {
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      "dimension": "reproducibility",
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  ],
  "risks": [],
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  "open_questions": [],
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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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  "equations_source_sha256": "",
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}