This dataset is released for internal research review only. Access is granted per request. By requesting access you agree not to redistribute the contents and not to use them to train publicly released models. Note that source-audit/ holds upstream third-party CAD files carrying enterprise authorship metadata, included by operator decision for audit purposes; redistribution authority for those files is disputed and is not granted to you by this dataset.

FrontierBench CAD Authoring Seeds

Measured design-complexity profiles and clean-room CAD generators used to author parametric-rebuild benchmark tasks. Everything under profiles/ and clean-room/ is either a statistic measured from source material or first-party generator code. source-audit/ is the exception: it holds unmodified upstream third-party CAD files, added by operator decision — see RIGHTS.md before using them for anything.

Path	Count	What it is
`profiles/PGS-*.json`	15	Per-part design-complexity profiles: dimension counts by kind and qualifier, tolerance deviation magnitudes, nominal ranges, GD&T type histograms, datum-frame shapes, per-measure authoring units. Schema `2.0`. No geometry, no coordinates, no source identifiers.
`clean-room/*/truth.json`	11	Frozen design definitions for first-party parametric families: parameter space or discrete variation lists, derived quantities, named constraints, drawing contract, coordinate frame, units.
`clean-room/*/generate_assets.py`	11	First-party deterministic generators. Seeded sampling, no network access, no CAD addons.
`metrics/profile_metrics.jsonl`	598 rows	The same profile numbers in long format, one row per measurement: `seed_id`, `metric`, `key`, `value`. Derived from `profiles/` by the builder; this is what the dataset viewer reads.
`source-audit/`	3	Unmodified upstream material for one third-party part: a STEP solid carrying embedded PMI, its drawing PDF, and the upstream collection's licence text. Not first-party, not clean-room, not produced by the builder. Carries the upstream part number, enterprise authorship and an individual's account name in its embedded metadata.

How to use

Access is gated: request it on the dataset page, then authenticate locally with hf auth login or by exporting HF_TOKEN. Every snippet below was run against this repository as published, and the outputs shown are the real ones.

The numbers, as a table. 598 rows, four columns, one row per measurement.

from datasets import load_dataset

metrics = load_dataset(
    "SueMintony/frontierbench-cad-authoring-seeds", "profile_metrics", split="train"
)
metrics[0]
# {'key': 'inch', 'metric': 'authoring_units', 'seed_id': 'PGS-01', 'value': 1.0}

One row per part, if you want a feature matrix. Fold key into the column name and pivot. The 598 rows become 15 x 96 -- 41.5% filled, because a bucket that a part never uses has no row. Those gaps are absences, not zeros: fillna(0) is right for histogram counts and wrong for magnitudes and ranges, so choose per metric rather than globally.

df = metrics.to_pandas()
df["column"] = df["metric"] + df["key"].map(lambda k: f"[{k}]" if k else "")
wide = df.pivot(index="seed_id", columns="column", values="value")
wide.loc["PGS-01", "dimension_profile.by_kind[size]"]   # 9.0

(seed_id, metric, key) is unique, so the pivot needs no aggregation.

The profile documents. profiles/*.json is the source of truth; the table is derived from it by the builder. Fetch just those:

from huggingface_hub import snapshot_download

local = snapshot_download(
    "SueMintony/frontierbench-cad-authoring-seeds",
    repo_type="dataset",
    allow_patterns=["profiles/*.json"],
)

Or take the whole bundle without the third-party material:

hf download SueMintony/frontierbench-cad-authoring-seeds --type dataset \
  --exclude "source-audit/*" --local-dir ./seeds

Ids run PGS-01 to PGS-14 and then PGS-16 -- 15 profiles, and the numbering skips one. PGS-15 was rejected during measurement, not lost: its source tagged every dimension in inch and every geometric-tolerance magnitude in millimetre, two disjoint unit sets, which no real drawing carries. It was read as an upstream export defect and dropped before any measurement was kept, so no profile exists to publish under that id, and the id stays a placeholder rather than being reused so the others are not renumbered. A loader that assumes a dense range will go looking for a file that is not here.

Before computing on any magnitude, read the units caveat below. Fields already normalised to millimetres carry a _mm suffix; authoring_units says what the part was drawn in, and one part can legitimately mix the two.

source-audit/ is not analysable data. It is unmodified upstream third-party CAD, kept for audit and excluded from the snippets above on purpose. Read RIGHTS.md before touching it, and EXCLUDED.md for what a rebuild does and does not reproduce.

Geometry families

flanged-bushing (4 designs), flanged-bearing-support-bracket (1), finned-electronics-mounting-plate (3 design ids across 4 directories), rectangular-equipment-platform-housing (1), sprocket (1).

One further design is still withheld in full -- its truth.json and generator are not here; see EXCLUDED.md. The upstream material that design was measured from is, however, now published under source-audit/, so the withholding no longer protects that design from retrieval.

What is rewritten before publishing

Selection is an allowlist and the scan is a refusal gate, so nothing here was scrubbed into shape. Two rewrites are declared: each source_taxonomy keeps its "nothing was copied" assertions but not the source's identity, and four generators had an absolute rasteriser path replaced by the bare command, which resolves through PATH. Generator logic is unchanged, and EXCLUDED.md lists both alongside everything withheld outright. source-audit/ bypasses this pipeline entirely: those bytes are the upstream originals, unscanned and unredacted, and rebuilding the bundle will not reproduce them.

Why the viewer reads a second copy

profiles/*.json cannot be loaded as a table. Five of their fields -- by_kind, by_qualifier, note_types, by_type, datum_frames -- are sparse histograms whose buckets differ per part: 9 distinct key sets for by_qualifier across 15 files, 14 for datum_frames with a 42-bucket union. A table column has one type, so a loader infers a struct from the first file and then fails to cast the next one, taking the whole split with it. Widening every file to the union of buckets would only move the problem: the buckets are data-dependent labels, so the next part measured brings a new one and breaks it again.

metrics/profile_metrics.jsonl is therefore the viewer's copy, in long format where a new bucket is another row rather than another column. Three string columns and one float, always. A named scalar carries the dotted path with an empty key (dimension_profile.count), a histogram bucket carries its label in the key (gdt_profile.datum_frames / A|B|C), a list element carries its index, and a string value moves into the key with 1.0. Read profiles/ for the documents; read this for the numbers.

Units caveat

Length units are recorded per measure, not per file. In the source STEP files the header SI unit is only the SI base; each measure carries its own unit reference, and a single part can legitimately mix inch dimensions with millimetre GD&T magnitudes. profiles/*.json therefore carries an authoring_units list, and every magnitude field is normalised to millimetres with a _mm suffix. Reading a file header instead of the per-measure unit is a silent 25.4x error.

Known gap

Across all 15 profiles the dimension mix is size-dominated (location:size roughly 40:198) and basic/TED dimensions are nearly absent (6 total). The common real-world pattern of "basic dimensions locate the features, a POSITION tolerance references datum frame A|B|C" is therefore under-represented. Do not treat this pool as a representative sample of production MBD practice.

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