README.md

---
license: cc-by-nc-4.0
language:
  - en
task_categories:
  - question-answering
tags:
  - robotics
  - industrial
  - time-series
  - causal-reasoning
  - benchmark
  - machine-understanding
pretty_name: FactoryBench
size_categories:
  - 10K<n<100K
---

# FactoryBench

FactoryBench is a benchmark for evaluating **machine-behavior reasoning** in time-series models and LLMs over industrial robotic telemetry. Question-answer pairs are organised along the four levels of Pearl's causal hierarchy:

| Level | Capability | Example |
|-------|-----------|---------|
| **L1 — State** | Identify the operational state from raw signals | "Which fault, if any, is occurring in this episode?" |
| **L2 — Intervention** | Predict the effect of an intervention | "How would the joint torques change if the payload were doubled?" |
| **L3 — Counterfactual** | Reason about alternative histories | "Would the collision still have occurred if the speed had been 50% lower?" |
| **L4 — Decision** | Engineering decision-making (troubleshooting + optimisation) | "Given this anomaly, what is the most likely root cause and remediation?" |

The benchmark is grounded in **FactoryWave**, a dense multivariate telemetry dataset collected from a UR3 collaborative robot (125 Hz) and a KUKA KR10 industrial arm (83 Hz), supplemented with the AURSAD and voraus-AD open-source datasets.

## Dataset summary

- **70,918 Q&A pairs** across four causal levels and three splits (train/validation/test).
- **5 answer formats**: single-select MCQ, multi-select MCQ, ranking, tensor/numerical, free-form (judged by an LLM-as-judge voting protocol).
- **Telemetry from real industrial robots** with systematic fault injection (27 atomic mechanisms across pick-and-place, screwing, and peg-in-hole tasks).

## Repository layout

```
FactoryBench/
└── factorybench_qa/                 # Question-answer pairs
    ├── level_1/{train,validation,test}.jsonl
    ├── level_2/{train,validation,test}.jsonl
    ├── level_3/{train,validation,test}.jsonl
    └── level_4/{train,validation,test}.jsonl
```

## Q&A pair counts

| Level | Train  | Val   | Test  | Total   |
|-------|--------|-------|-------|---------|
| L1    | 12,674 | 1,338 | 1,309 | 15,321  |
| L2    | 33,311 | 3,428 | 3,487 | 40,226  |
| L3    | 2,353  | 265   | 321   | 2,939   |
| L4    | 9,949  | 1,251 | 1,232 | 12,432  |
| **Total** | **58,287** | **6,282** | **6,349** | **70,918** |

## Q&A fields

Each line in `factorybench_qa/level_*/*.jsonl` is a single Q&A item:

| Field | Description |
|-------|-------------|
| `id` | Unique item identifier |
| `level` | Causal level (1–4) |
| `template_id` | Question template the item was generated from |
| `template_type` | Answer format (`single_choice`, `multi_choice`, `ranking`, `tensor`, `free_form`) |
| `hides` | Channels/fields hidden from the model in this item |
| `question` | Natural-language question |
| `options` | Answer options (for MCQ/ranking templates) |
| `answer` | Ground-truth answer |
| `root_cause` | Underlying fault/cause (Level 4 only) |
| `acceptance_bounds` | Tolerance for numerical answers |
| `provenance` | Source episode(s) and channels used to derive the item |
| `context` | Time-series and metadata context exposed to the model |

## Loading the data

```python
from datasets import load_dataset

# Load a single level/split
ds = load_dataset(
    "Forgis/FactoryBench",
    data_files="factorybench_qa/level_1/test.jsonl",
    split="train",
)
```

## Citation

If you use FactoryBench, please cite the dataset and the two upstream open-source datasets it incorporates (AURSAD and voraus-AD):

```bibtex
@misc{anonymous2026factorybench,
  title  = {FactoryBench: Evaluating Industrial Machine Understanding},
  author = {Anonymous},
  year   = {2026},
  note   = {Submission under double-blind review}
}

@article{leporowski2022aursad,
  title   = {{AURSAD}: Universal Robot Screwdriving Anomaly Detection Dataset},
  author  = {Leporowski, B{\l}a{\.z}ej and Tola, Daniella and Hansen, Christian and Iosifidis, Alexandros},
  journal = {arXiv preprint arXiv:2202.03211},
  year    = {2022}
}

@misc{brockmann2024vorausad,
  title         = {voraus-{AD}: A New Dataset for Anomaly Detection in Robot Applications},
  author        = {Brockmann, Jan Thie{\ss} and Rudolph, Marco and Rosenhahn, Bodo and Wandt, Bastian},
  year          = {2024},
  eprint        = {2311.04153},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO}
}
```

## Intended use & limitations

**Intended use.** Benchmark evaluation of LLMs and time-series models on structured industrial Q&A reasoning tasks (state, intervention, counterfactual, decision-making).

**Limitations.** Domain-specific to factory and industrial robotic scenarios; may not generalise to open-domain Q&A. Faults are atomic and drawn from a closed catalogue of 27 physically injected mechanisms — different from compound or gradual real-world faults. The dataset shows a size imbalance between Levels 2 and 3.

**Out-of-scope.** Not intended for deployment in safety-critical, medical, legal, or financial decision systems without further validation by domain experts.

## License

Released under the [Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) license. You may share and adapt the dataset for non-commercial purposes with appropriate attribution.