{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A multi-step product task fails because the agent loses track of dependencies.",
    "change": "Try the proposed planning step on tasks with checkable intermediate outcomes.",
    "baseline": "The same agent solving the same tasks without the new planning step.",
    "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": "Task completion, unnecessary steps, planning tokens, response time, and total cost.",
    "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": "2610.30042",
  "title": "HGM-Dual: Lineage-Aware Guided Agent Harness Optimization for Long-Horizon Planning",
  "summary": "HGM-Dual replaces unstructured agent archives and purely open-ended self-modification with lineage-aware evolution context plus dual-path child generation that targets likely constraint-violating harness components. It costs extra bookkeeping of ancestor edits, failure attribution, and multiple child evaluations per parent, so implementation complexity is nontrivial even though the base LLM can remain frozen or API-based. It can fail if constraint diagnostics are wrong, lineage records attribute improvements incorrectly, or planning tasks do not show stable constraint-violation patterns for specialized edits to exploit.",
  "rationale": "The budget efficiency is directly relevant to constrained teams, but the method depends on a diagnostic evolution loop and planning benchmarks that need independent validation before production adoption.",
  "technique": "Lineage-aware guided harness optimization for planning agents",
  "family": "planejamento_e_decomposicao",
  "recommendation": "observar",
  "published": "2026-10-21",
  "as_of": "2026-10-25",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.30042",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
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      "claim_id": "claim-02",
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      "result": "",
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    }
  ],
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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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": "",
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}