Toward Engineering AGI: Benchmarking the Engineering Design Capabilities of LLMs
Paper • 2509.16204 • Published
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ED-D08
Engineering-design tasks reformatted into the unified smart-manufacturing SFT schema. Category E (code / program), task T-E2.
The repository name is an internal code. See Provenance below for the underlying dataset.
Records
101 records (no train/val/test split).
Attachments. Images are inlined via the HF Image feature (original path kept in metadata.image_path). Text attachments (.json, .vcd) are inlined into metadata.attachments_text (relative path → file content). Genuine binary attachments (.npy arrays, .mp4 animations) ship as files under attachments/ and are listed in metadata.attached_files (repo-relative paths).
Unified SFT schema (7 fields)
| field | type | meaning |
|---|---|---|
query |
str | the question / query / instruction |
image |
Image | null | inlined image content (12 records); null otherwise. Original relative path kept in metadata.image_path |
annot |
str | list[str] | label / answer / annotation |
reasoning |
str | null | native reasoning / CoT; null when the source has none |
cate |
"A".."E" | one of the five SFT categories (this dataset: E) |
task |
"T-xx" | unified task id (this dataset: T-E2) |
metadata |
str (JSON) | all other info; carries a "split" key when the source has train/val/test |
Load
from datasets import load_dataset
ds = load_dataset("AI4Manufacturing/ED-D08")
Gated — request access on the dataset page; access is granted manually by the maintainers.
Provenance & license
This dataset is a reformatted derivative (unified SFT schema) of:
EngDesign — Toward Engineering AGI (NeurIPS 2025 Datasets & Benchmarks).
- Paper: https://arxiv.org/abs/2509.16204
- Code: https://github.com/agi4engineering/EngDesign
- Original data: https://huggingface.co/datasets/opt1zer/EngDesign
Refer to the upstream source for the original licensing terms; this reformatted version is shared for research use. Please cite the upstream work.
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