Initial release: factorybench_qa

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README.md

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+---
+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.

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