Datasets:

Quome
/

triagebench

license: apache-2.0
pretty_name: TriageBench
language:
  - en
tags:
  - clinical-ai
  - llm-security
  - ai-agents
  - healthcare
  - haarf
  - quokkaguard
  - synthetic
size_categories:
  - n<1K
task_categories:
  - other

TriageBench

Risk-calibrated escalation routing that sends only genuinely ambiguous agent actions to clinicians.

TriageBench is the benchmark corpus for Calibrating Human Oversight for Clinical AI Agents: Routing Only the Ambiguous Tail to Clinicians, the qutriage module of the QUOKKAGUARD program. It ships with the qutriage repository, which contains the qfire gateway layer under test, the experiment harness, and the paper.

600 synthetic clinical agent actions in four categories (easy-legit, easy-attack, stealth-attack with gray-zone signals, suspicious-legit with elevated signals), each with three simulated-expert labels, a severity, and a time-to-care impact, plus the fitted calibration (calibration_fitted.json); fully tracked, regenerable at seed 42 with scripts/009-hitl-calibration/gen_triage.py.

All data are synthetic. No real patient data or protected health information (PHI) is included; clinical content is generated from templates with fixed seeds.

Files

File	Size	Rows
`calibration_fitted.json`	434 B
`triagebench/actions.jsonl`	363 KB	600 rows
`triagebench/raters.jsonl`	93 B	3 rows

Record schemas

triagebench/actions.jsonl: action, category, expert_labels, gold_decision, id, legit, signals, source_bench, tool, tool_risk, true_harm
triagebench/raters.jsonl: noise, rater

How it was generated

The corpus is produced by the generator in the paper repository.

Intended use

Evaluating the oversight enforcement layer of a clinical-agent security gateway (HAARF control C4): Every upstream guardrail in a clinical AI gateway answers a binary allow/block question, and strict enforcement over-blocks: the policy control alone false-denies 28.3% of legitimate actions, which is a real care-delay hazard and a source of alarm fatigue.

The experiments that consume it (E-series in the paper) are reproduced from the repository:

git clone https://github.com/quome-cloud/qutriage
cd qutriage
cargo build --release

then follow the Reproduce the experiments section of its README.

Citation

This benchmark was built to evaluate a control of the Healthcare AI Agents Regulatory Framework (HAARF), the source framework for the QUOKKAGUARD program. Please cite both the paper and HAARF:

@unpublished{schwoebel2026qutriage,
  author = {Schwoebel, James},
  title  = {Calibrating Human Oversight for Clinical AI Agents: Routing Only the Ambiguous Tail to Clinicians},
  note   = {Preprint. Quome, QUOKKAGUARD program (qutriage module)},
  year   = {2026},
  url    = {https://github.com/quome-cloud/qutriage}
}

@unpublished{schwoebel2026haarf,
  author = {Schwoebel, Jim and Frasch, Martin and Spalding, Art and Sewell, Ed and Englert, Phil and Halpert, Ben and Overbay, Collin and Semenec, Ingrida and Shor, Joel},
  title  = {{HAARF}: Healthcare {AI} agents regulatory framework --- a comprehensive security verification standard for autonomous {AI} systems in clinical environments},
  note   = {medRxiv Preprint},
  year   = {2026},
  month  = {April},
  doi    = {10.64898/2026.04.09.26350519},
  url    = {https://www.medrxiv.org/content/10.64898/2026.04.09.26350519v1}
}