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; HF paper page. Code, harness and ground-truth generator: github.com/continker/metrollm-bench. Fine-tuned students: the MetroLLM-Bench v24 collection. Live HF Space demo: remcohendriks/metrollm.

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

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

@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.