{
  "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": "2609.18844",
  "title": "ReFigBench: Benchmarking Scientific Figure Reconstruction as Editable PowerPoint Artifacts",
  "summary": "ReFigBench replaces proxy-based evaluation of multimodal coding agents (screenshot similarity, isolated tool-call traces) with end-to-end evaluation on 1,000 real scientific figures reconstructed as editable PowerPoint artifacts, combining deterministic checks, repeated multi-family LLM judging, and blinded human comparison. Adopting its insights costs little directly, but running the benchmark itself requires paid API access to frontier multimodal models and commercial agent harnesses, plus the complexity of assembling a judging pipeline. What can fail: the finding that workflow benefit depends on the model-harness combination means benchmark conclusions may not transfer to the reader's specific stack, and human-vs-automated judge disagreement (the specialized workflow loses native connectors yet wins human preference) shows rubric-based scores can mislead production decisions.",
  "rationale": "This is an evaluation framework rather than a deployable technique, so its value to a constrained team is indirect: it warns that agent quality depends on the model-harness pairing and that perception, not harness, is the main bottleneck, which is worth tracking when selecting a commercial agent harness but does not justify immediate adoption or benchmarking spend.",
  "technique": "Benchmark of figure-to-PPTX reconstruction agents",
  "family": "avaliacao_de_agente",
  "recommendation": "observar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18844",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "ReFigBench replaces proxy-based evaluation of multimodal coding agents (screenshot similarity, isolated tool-call traces) with end-to-end evaluation on 1,000 real scientific figures reconstructed as editable PowerPoint artifacts, combining deterministic checks, repeated multi-family LLM judging, and blinded human comparison. Adopting its insights costs little directly, but running the benchmark itself requires paid API access to frontier multimodal models and commercial agent harnesses, plus the complexity of assembling a judging pipeline. What can fail: the finding that workflow benefit depends on the model-harness combination means benchmark conclusions may not transfer to the reader's specific stack, and human-vs-automated judge disagreement (the specialized workflow loses native connectors yet wins human preference) shows rubric-based scores can mislead production decisions.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.18844",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
    {
      "dimension": "operations",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "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": ""
}