---
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`](https://github.com/quome-cloud/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:

```bash
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:

```bibtex
@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}
}
```

## License

Apache License 2.0. Copyright (c) 2026 Quome, Inc.