{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A narrow, repeated product task may not need your largest model.",
    "change": "Check whether a released student model fits the task before generating training data.",
    "baseline": "Your current model and an off-the-shelf small model 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": "Student quality, serving cost, teacher calls, training cost, and failures on unusual inputs.",
    "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": "2609.24974",
  "title": "Harness-Zero: Harness Distillation via Agent-as-Harness",
  "summary": "The method replaces deployment-time reliance on domain-optimized agent harnesses (and routing among many specialized harnesses) by distilling harness-induced behaviors into model weights, so a single fixed harness suffices at inference. It costs a training-time pipeline: an optimized harness per domain, a harnessing agent that rewrites student responses into the target action space, trajectory collection, and fine-tuning, which typically implies frontier-API spend and non-trivial engineering. It can fail when the distilled behaviors do not transfer across models or new task distributions, when the optimized harness itself is unavailable or weak for a domain, and because gains are validated only on the paper's three domains and may require re-distillation whenever the base model changes.",
  "rationale": "The approach presupposes per-domain evolved harnesses, a harnessing-agent data-generation loop, and fine-tuning capacity that exceed a small constrained team's near-term reach, though the results justify tracking it for later adoption via fine-tuning APIs.",
  "technique": "Agent harness distillation via agent-as-harness",
  "family": "destilacao",
  "recommendation": "observar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24974",
  "independent_implementations": 6,
  "total_implementations": 6,
  "stars": 151,
  "citations": null,
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      "dimension": "operations",
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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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  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
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