Mirror the 955 cases from paper-v1.2 (train/holdout splits)

README.md

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+---
+license: apache-2.0
+pretty_name: MetroLLM-Bench
+language:
+- en
+task_categories:
+- text-generation
+tags:
+- benchmark
+- tool-calling
+- agents
+- transit
+- kiosk
+- llm-evaluation
+size_categories:
+- n<1K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*.jsonl
+  - split: holdout
+    path: data/holdout-*.jsonl
+---
+
+# MetroLLM-Bench
+
+The 955 cases of **MetroLLM-Bench**, a benchmark for language models as the policy layer of a
+transit kiosk. The model receives kiosk events, calls structured tools (route planner, fare
+calculator, station info, disruption feed, knowledge base) and submits a terminal state: outcome,
+fare quote where applicable, kiosk action.
+
+Paper: [MetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes](https://arxiv.org/abs/2609.10016)
+(arXiv:2609.10016). Code, harness and ground-truth generator:
+[github.com/continker/metrollm-bench](https://github.com/continker/metrollm-bench).
+Fine-tuned students: the [MetroLLM-Bench v24 collection](https://huggingface.co/collections/continker/metrollm-bench-v24-6a35b586a11068e1b1ba3d47).
+Live HF Space demo: [remcohendriks/metrollm](https://huggingface.co/spaces/remcohendriks/metrollm).
+
+Mirror of `cases/*_cases.json` at tag `paper-v1.2` of the repository; the repository is the source of
+truth. Regenerated by `scripts/build_hf_dataset.py`.
+
+## Case details
+
+One row per case. Each row holds the kiosk event sequence (station selected, passenger count
+changed, disruption update, free-text input), the system context (current time, active
+disruptions, framebook) and the ground truth used for scoring (expected route, fare, outcome,
+kiosk action, and category-specific expectations such as traps or policies).
+
+Scoring requires the harness in the repository: the mock tool server backed by the six system
+datasets, the runner, and the 22-component scorer with its language-model judge. See
+`REPRODUCING.md` there.
+
+## Systems
+
+| System | `system` | Cases | Train | Held-out |
+|---|---|---:|---:|---:|
+| MARTA (Atlanta) | `marta` | 156 | 117 | 39 |
+| Doha Metro | `doha` | 156 | 117 | 39 |
+| BART (San Francisco Bay Area) | `bart` | 157 | 118 | 39 |
+| Taipei MRT | `taipei` | 167 | 125 | 42 |
+| CTA L (Chicago) | `cta` | 157 | 118 | 39 |
+| Beijing Subway | `beijing` | 162 | 122 | 40 |
+
+## Categories
+
+| `category` | Name | Cases |
+|---|---|---:|
+| A | Routing | 121 |
+| B | Fare calculation | 93 |
+| C | Disruptions | 124 |
+| D | Accessibility | 91 |
+| E | Cultural and multilingual | 42 |
+| F | Policy change | 94 |
+| G | Multi-turn | 90 |
+| H | Adversarial | 90 |
+| I | Temporal reasoning | 90 |
+| J | Tool hallucination | 90 |
+| K | Compound stress | 30 |
+
+## Splits
+
+`train` (717 cases) and `holdout` (238 cases) form the paper's stratified 75/25
+case-level partition (seed 42, stratified by system). Training data for
+the students came from `train` only; all held-out numbers in the paper are computed on `holdout`.
+
+## Row schema
+
+| Column | Type | Content |
+|---|---|---|
+| `id` | string | case id, e.g. `MARTA-A-001` |
+| `system` | string | one of the six system keys |
+| `category` | string | `A` to `K` |
+| `difficulty` | string | `easy`, `medium`, `hard` |
+| `interaction_mode` | string | `structured`, `freetext`, `multi_turn`, `adversarial`, `hallucination_probe`, `compound` |
+| `title` | string | short scenario title |
+| `partition` | string | `train` or `holdout` (same as the split) |
+| `expected_outcome` | string | one of `route_and_fare_ready`, `advisory_only`, `service_unavailable`, `request_declined`, `policy_answer_only` |
+| `expected_kiosk_action` | string | the kiosk action the terminal state must carry |
+| `fare_total` | float or null | expected fare; null where no fare applies |
+| `fare_currency` | string or null | `USD`, `QAR`, `TWD`, `CNY` |
+| `events` | string (JSON) | the kiosk event sequence, verbatim |
+| `system_context` | string (JSON) | current time, active disruptions, framebook, feature toggles |
+| `ground_truth` | string (JSON) | the complete ground truth, all keys |
+| `scoring` | string (JSON) | point weights of the scoring components that apply to this case |
+| `tolerances` | string (JSON) | numeric tolerances used by the scorer |
+| `extras` | string (JSON) | category-specific fields (scenario, trap, policy, temporal, accessibility, cultural ids) |
+
+The JSON columns hold the original case objects verbatim; `json.loads` returns the same objects as
+`cases/<system>_cases.json`.
+
+## Loading
+
+```python
+from datasets import load_dataset
+import json
+
+ds = load_dataset("continker/metrollm-bench")
+row = ds["holdout"][0]
+events = json.loads(row["events"])
+ground_truth = json.loads(row["ground_truth"])
+```
+
+## Citation
+
+```bibtex
+@techreport{hendriks2026metrollm,
+  title       = {MetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes},
+  author      = {Hendriks, Remco},
+  institution = {Continker},
+  type        = {Technical report},
+  number      = {v1.2},
+  year        = {2026},
+  month       = {9},
+  doi         = {10.5281/zenodo.21893944},
+  eprint      = {2609.10016},
+  archiveprefix = {arXiv},
+  primaryclass = {cs.LG},
+  url         = {https://arxiv.org/abs/2609.10016}
+}
+```
+
+## License
+
+Apache License 2.0, as the code, ground truth and framebooks in the repository.