{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A workflow needs specialist review, but extra agents may add more cost than useful work.",
    "change": "Test the proposed division of work against a single-agent implementation.",
    "baseline": "One agent with the same tools and an equal total token and time 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": "Accepted outputs, coordination failures, wall-clock time, and total cost across all agents.",
    "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": "2610.06790",
  "title": "Back to the Future: Rethinking EDA Infrastructure for Agentic Systems in Chip Design Verification",
  "summary": "The method replaces manual inspection of large unstructured simulation dumps with a normalized SQLite database queried by a multi-agent system that translates natural-language verification questions into schema-aware SQL and links signal anomalies to versioned RTL. Costs include building and maintaining the dump-to-database normalization pipeline, per-query LLM API or serving spend, and orchestration complexity across agents. Failures to expect after adoption are schema drift between RTL revisions, hallucinated or subtly wrong SQL on unseen query types, and silent miscorrelation of anomalies when versioned repositories are incomplete.",
  "rationale": "The underlying pattern, database-grounded multi-agent text-to-SQL with source correlation, is transferable to production diagnostics, but the reported accuracy is tied to EDA-specific data and a small benchmark, so an ML engineer should treat it as a validated architecture pattern rather than an adoptable tool.",
  "technique": "Multi-agent NL-to-SQL over normalized simulation database",
  "family": "orquestracao_multiagente",
  "recommendation": "observar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06790",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 131,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces manual inspection of large unstructured simulation dumps with a normalized SQLite database queried by a multi-agent system that translates natural-language verification questions into schema-aware SQL and links signal anomalies to versioned RTL. Costs include building and maintaining the dump-to-database normalization pipeline, per-query LLM API or serving spend, and orchestration complexity across agents. Failures to expect after adoption are schema drift between RTL revisions, hallucinated or subtly wrong SQL on unseen query types, and silent miscorrelation of anomalies when versioned repositories are incomplete.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.06790",
      "source_page": null,
      "source_excerpt": ""
    },
    {
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      "statement": "95.33% execution accuracy on a 150-query verification benchmark.",
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      "result": "",
      "baseline": "",
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      "source_url": "https://arxiv.org/abs/2610.06790",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
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      "dimension": "compute",
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      "finding": "",
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      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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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",
      "basis": "not_evaluated",
      "finding": "",
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      "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",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "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": "",
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}