{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per 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": "2610.06825",
  "title": "PlotGround: Grounding Plot Digitization in Real Scientific Figures and Their Source Data",
  "summary": "PlotGround replaces synthetic or narrow plot-digitization benchmarks with an automated pipeline that pairs real bioRxiv figures with author-released source tables to generate human-verified questions. It costs nothing to run as a model but requires source data for ground truth, and adopting the paper's key finding means preferring table-based extraction over figure reading where tables exist. It can fail when precise recovery is needed, since all evaluated models lose 11-24 accuracy points when tolerance tightens from ±5% to ±2%.",
  "rationale": "This is an evaluation benchmark rather than a deployable technique, so it is relevant mainly as a reference for calibrating expectations about vision-model plot reading and for the actionable finding that source tables outperform figures at lower cost.",
  "technique": "Source-grounded benchmark for scientific plot digitization",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-10-05",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.06825",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 122,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "PlotGround replaces synthetic or narrow plot-digitization benchmarks with an automated pipeline that pairs real bioRxiv figures with author-released source tables to generate human-verified questions. It costs nothing to run as a model but requires source data for ground truth, and adopting the paper's key finding means preferring table-based extraction over figure reading where tables exist. It can fail when precise recovery is needed, since all evaluated models lose 11-24 accuracy points when tolerance tightens from ±5% to ±2%.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.06825",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Providing source tables instead of figures raises a coding agent's digitization accuracy from 90.0% to 97.4% while cutting cost by 72%; best multimodal model reaches 87.5% accuracy at ±5% relative error, dropping 11-24 points at ±2%.",
      "basis": "inferred",
      "result": "",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.06825",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
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      "finding": "",
      "interpretation": "",
      "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": ""
}