{
  "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": "2609.27349",
  "title": "MolDesignBench: Evaluating LLM-based Agent for Scenario-grounded Molecular Design",
  "summary": "MolDesignBench replaces narrow, single-constraint molecular design benchmarks with a 2K-instance scenario-grounded evaluation of tool-augmented LLM agents over 17 chemistry tools, including infeasible cases. It costs nothing in model infrastructure but requires a chemistry domain workload and the benchmark's tool stack to be useful, and it provides an evaluation harness rather than a deployable capability. It offers no production method to adopt; its ~43% ceiling measures current agent limits rather than a performance gain.",
  "rationale": "This is a domain-specific evaluation benchmark for chemistry agents, not a technique the reader can adopt for inference, training, or general-purpose agent work.",
  "technique": "Scenario-grounded benchmark for chemistry tool-use agents",
  "family": "avaliacao_de_agente",
  "recommendation": "nao_aplica",
  "published": "2026-09-23",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.27349",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "MolDesignBench replaces narrow, single-constraint molecular design benchmarks with a 2K-instance scenario-grounded evaluation of tool-augmented LLM agents over 17 chemistry tools, including infeasible cases. It costs nothing in model infrastructure but requires a chemistry domain workload and the benchmark's tool stack to be useful, and it provides an evaluation harness rather than a deployable capability. It offers no production method to adopt; its ~43% ceiling measures current agent limits rather than a performance gain.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.27349",
      "source_page": null,
      "source_excerpt": ""
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      "source_url": "https://arxiv.org/abs/2609.27349",
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      "source_excerpt": ""
    }
  ],
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    },
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      "dimension": "latency",
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    },
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      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "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": "",
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}