README.md

---
license: cc-by-4.0
language:
  - pt
pretty_name: EngQuant
size_categories:
  - n<1K
task_categories:
  - question-answering
  - text-generation
tags:
  - engineering
  - physical-quantities
  - quantitative-reasoning
  - structural-engineering
  - portuguese
  - benchmark
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/engquant.parquet
---

# EngQuant

**Adversarial benchmark of physical quantities in engineering, in Brazilian Portuguese.**

EngQuant is a set of **800** procedurally generated, multi-step design-and-verification
problems in engineering (Brazilian Portuguese), each with a **verifiable numeric answer key
per sub-quantity** (`gabarito`). Every case is anchored in a primary bibliographic source —
canonical textbooks, ABNT (Brazilian) technical norms, theses, and validated lecture notes —
and stresses exactly where language models tend to fail on technical text: locale-specific
numbers (decimal comma, thousands), compound dimensional units, normative/material
identifiers, symbolic notation, regime decisions, and error propagation across steps.

Inclusion criterion: ≥ 4 auditable sub-quantities, ≥ 1 high-risk tag, a resolvable reference,
and `review_status = validated`.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("aiacontext/engquant", split="train")
print(ds[0]["title"])
# verifiable answer key per sub-quantity:
print(ds[0]["gabarito"])
```

## Data fields

| field | type | description |
|---|---|---|
| `id` | string | case identifier (e.g., `civ-balanco-cons-001`) |
| `title` | string | case title with the key quantities |
| `metadata` | struct | `discipline_folder`, `language`, `has_figure`, `has_table`, `n_subgrandezas` |
| `reference` | struct | bibliographic anchor (`source_id`, `chapter`, `section`, `page_range`, `notes`) |
| `tags` | list[string] | emergent stratification tags (cross-discipline) |
| `expected_signals` | list[string] | reasoning signals a correct solution should exhibit |
| `memoria_calculo` | struct | worked calculation memory |
| `expected_norms` | list[string] | normative references expected in the solution |
| `gabarito` | list[struct] | **answer key**: one auditable sub-quantity per entry (value + unit) |
| `prompts` | struct | prompt variants |
| `messages_for_api` | struct | chat-format messages |

## Tag stratification

Disciplines are organizational folders; statistical analysis stratifies by emergent tags that
cut across disciplines. Counts over the 800 cases:

| tag | n |
|---|---|
| `cadeia-erro-propagado` | 800 |
| `disciplina-civ-estrutural` | 800 |
| `identificador-simbolo-tecnico` | 800 |
| `locale-pt-br-decimal-virgula` | 800 |
| `identificador-material-br` | 690 |
| `identificador-norma-br` | 690 |
| `bibliografia-norma-abnt` | 690 |
| `locale-pt-br-thousands` | 460 |
| `constante-normativa` | 420 |
| `bibliografia-livro-br` | 300 |
| `decisao-regime` | 200 |
| `identificador-fragmentado` | 120 |

Tag definitions are in [`tags_vocabulary.yaml`](tags_vocabulary.yaml); the bibliographic
sources are in [`references.yaml`](references.yaml); generation provenance (version, hashes,
schema) is in [`manifest.json`](manifest.json).

## Provenance

Version `0.1.5`, schema `1.0`. Cases are generated procedurally (Latin-hypercube sampling over
physically validated parameter ranges) and the answer keys are computed, not authored —
each sub-quantity is numerically verifiable. The textual style of each case is *informed by*
its bibliographic source; the cases themselves are original.

## License

[CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) © 2026 Aia Context.

## Citation

```bibtex
@misc{engquant2026,
  title        = {EngQuant: an adversarial benchmark of physical quantities in
                  Brazilian-Portuguese engineering},
  author       = {Leit\~ao Filho, Antonio de Sousa and Barros Filho, Allan Kardec Duailibe and
                  Lima, Fabr\'icio Saul and Santos, Selby Mykael Lima dos and
                  Sousa, Rejani Bandeira Vieira},
  year         = {2026},
  howpublished = {Hugging Face dataset},
  url          = {https://huggingface.co/datasets/aiacontext/engquant}
}
```