{
  "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": "2609.24972",
  "title": "RRSI: Regularized Recursive Self-Improvement of Agent Harnesses",
  "summary": "RRSI replaces unconstrained automated editing of an agent harness (prompts, control flow, tools, memory) with evolution constrained by an annealed edit budget, exploration incentives, a critic that filters benchmark-specific proposals, and a pruner that removes small, costly, or obsolete changes. The cost is an additional optimization loop requiring many agent rollouts and evaluation passes on representative tasks, plus the engineering of the proposer/critic/pruner pipeline, all on top of API or GPU inference spend. It can fail if the evolution benchmark is not representative of production traffic, if the rollout budget is too small for the regularization to distinguish reusable mechanisms from noise, or if the evolved harness overfits to a specific backbone model and does not transfer when the model is swapped.",
  "rationale": "The method addresses a real failure mode (harness overfitting) but requires repeated evolutionary runs with substantial evaluation rollouts that exceed a constrained compute and API budget, so it warrants monitoring rather than immediate adoption.",
  "technique": "Regularized evolution of agent harnesses",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-09-21",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.24972",
  "independent_implementations": 18,
  "total_implementations": 19,
  "stars": 1676,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "RRSI replaces unconstrained automated editing of an agent harness (prompts, control flow, tools, memory) with evolution constrained by an annealed edit budget, exploration incentives, a critic that filters benchmark-specific proposals, and a pruner that removes small, costly, or obsolete changes. The cost is an additional optimization loop requiring many agent rollouts and evaluation passes on representative tasks, plus the engineering of the proposer/critic/pruner pipeline, all on top of API or GPU inference spend. It can fail if the evolution benchmark is not representative of production traffic, if the rollout budget is too small for the regularization to distinguish reusable mechanisms from noise, or if the evolved harness overfits to a specific backbone model and does not transfer when the model is swapped.",
      "basis": "inferred",
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      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2609.24972",
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    },
    {
      "claim_id": "claim-02",
      "statement": "Up to 14.1 points on the evolved split and up to 4.7 points on five out-of-distribution benchmarks, with 30% fewer policy tokens than unregularized harness evolution.",
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      "source_url": "https://arxiv.org/abs/2609.24972",
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  ],
  "exposure_map": [
    {
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      "next_check": "",
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      "dimension": "compute",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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    },
    {
      "dimension": "data_and_training",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    }
  ],
  "risks": [],
  "minimum_test": [],
  "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": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}