{
  "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": "2610.01048",
  "title": "Network World Models as Environments for Algorithm Design on Complex Systems",
  "summary": "The method replaces Monte Carlo simulation of network diffusion dynamics with a learned action-conditioned world model that scores candidate intervention algorithms inside a coding-agent design loop using rollouts, action-level credit, and counterfactual probes. The cost is training a per-dynamics surrogate model plus the API or compute budget for the iterative agent loop, and it adds pipeline complexity beyond running a simulator directly. It can fail when the learned dynamics diverge from true diffusion behavior on out-of-distribution interventions or network structures, silently rewarding algorithms that exploit surrogate errors rather than real performance.",
  "rationale": "The approach targets a niche workload (intervention algorithm design on network diffusion tasks) and the code is not yet released, so a constrained-infrastructure team should validate the surrogate's fidelity on their specific network and diffusion model before adopting it in production.",
  "technique": "Learned network diffusion world model as evaluator",
  "family": "agentes_de_codigo",
  "recommendation": "testar",
  "published": "2026-10-01",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.01048",
  "independent_implementations": 4,
  "total_implementations": 5,
  "stars": 341,
  "citations": 0,
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      "dimension": "compatibility",
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      "dimension": "security",
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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",
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  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
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}