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Add anonymous reviewer dataset card

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+ ---
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+ license: mit
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+ pretty_name: CAD-bench Task Payloads
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+ language:
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+ - en
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+ tags:
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+ - cad
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+ - benchmark
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+ - build123d
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+ - step
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+ - mlcroissant
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+ - language-model-evaluation
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+ size_categories:
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+ - n<1K
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+ ---
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+
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+ # CAD-bench Task Payloads
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+
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+ This dataset contains the public task payloads for CAD-bench, an executable
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+ benchmark for language-model CAD agents. Each task directory includes:
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+
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+ - `prompt.txt`: the natural-language benchmark prompt
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+ - `task.toml`: task metadata, difficulty, evaluator name, and expected values
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+ - `gold.py`: a reference Build123D solution used for validation and media generation
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+ - optional fixtures such as STEP files or Blender simulation scripts
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+
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+ The benchmark runtime and scoring code are distributed with the accompanying
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+ anonymous submission artifact. The runtime loads this dataset by setting:
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+
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+ ```bash
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+ HF_TASKS_REPO_ID=uhiguys/cad-bench-ed-2026-anonymous
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+ HF_TASKS_REVISION=0ef63c7666d04fcc7e8061e23a37b4ebce75c4a7
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+ ```
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+
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+ ## Intended Use
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+
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+ Use this dataset with the CAD-bench runtime to evaluate CAD code-generation or
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+ agentic CAD systems. Scores are diagnostic benchmark signals; they are not
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+ certifications that generated mechanical parts are safe to manufacture or deploy.
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+
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+ ## Data Provenance
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+
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+ The tasks are synthetic CAD prompts and benchmark metadata authored for this
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+ benchmark. They do not contain personal data or human-subject records. The M3
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+ socket-head task includes a STEP fixture for a standard commercial fastener,
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+ documented in the task metadata and paper license table.
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+
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+ ## Limitations
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+
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+ The release has 17 tasks. Some simple geometry tasks are close to solved by
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+ current models, while functional assembly tasks remain difficult. The current
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+ reference implementations use Build123D, although the benchmark is intended to
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+ score submitted CAD artifacts rather than a particular modeling API.
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+
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+ ## Citation
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+
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+ Anonymous Author(s). CAD-bench: An Executable Benchmark for Language-Model CAD
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+ Agents. NeurIPS Evaluations & Datasets submission, 2026.