{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Your coding assistant makes changes that still need substantial repair before merging.",
    "change": "Try the technique on held-out issues from a repository with reliable tests.",
    "baseline": "The same coding agent, repository snapshot, tools, and token 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 patches, regression failures, human repair time, and total cost per accepted change.",
    "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": "2610.12086",
  "title": "EvoAlloc: A Self-Evolving Resource Allocation Agent for Efficient Program Evolution",
  "summary": "EvoAlloc replaces the fixed evaluation-allocation strategy in LLM-based program evolution with a learned allocator agent that periodically consolidates search outcomes into reusable experience and occasionally evaluates rejected candidates via counterfactual exploration. The cost is the additional allocator-agent inference, the infrastructure to log and consolidate allocation histories, and extra evaluations spent on counterfactual probes, all layered on top of an existing evolutionary search loop. It can fail when the workload is not iterative program evolution, when evaluation is cheap enough that allocator overhead exceeds savings, or when consolidated experience misleads allocation on shifted task distributions.",
  "rationale": "The method only applies to teams already running expensive iterative program-evolution loops, so its substantial token and evaluation savings must be validated on the reader's specific workload before production adoption.",
  "technique": "Self-evolving evaluation budget allocator for program search",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.12086",
  "independent_implementations": 2,
  "total_implementations": 3,
  "stars": 133,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "EvoAlloc replaces the fixed evaluation-allocation strategy in LLM-based program evolution with a learned allocator agent that periodically consolidates search outcomes into reusable experience and occasionally evaluates rejected candidates via counterfactual exploration. The cost is the additional allocator-agent inference, the infrastructure to log and consolidate allocation histories, and extra evaluations spent on counterfactual probes, all layered on top of an existing evolutionary search loop. It can fail when the workload is not iterative program evolution, when evaluation is cheap enough that allocator overhead exceeds savings, or when consolidated experience misleads allocation on shifted task distributions.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.12086",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "EvoAlloc requires 59-82% fewer full evaluations and 61-89% fewer total LLM tokens to reach baseline-level performance, and achieves 8.7-12.0% higher final performance under the same full-evaluation budget.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.12086",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
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      "next_check": "",
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "source_page": null,
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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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      "source_url": "",
      "source_page": null,
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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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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "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": ""
}