The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
action: struct<args: struct<>, tool: string>
child 0, args: struct<>
child 1, tool: string
category: string
expert_labels: list<item: struct<decision: string, rater: string, severity: int64, ttc_impact_min: int64>>
child 0, item: struct<decision: string, rater: string, severity: int64, ttc_impact_min: int64>
child 0, decision: string
child 1, rater: string
child 2, severity: int64
child 3, ttc_impact_min: int64
gold_decision: string
id: string
legit: bool
signals: struct<identity_trust: double, max_block_confidence: double, policy_margin: double, provenance_trust (... 30 chars omitted)
child 0, identity_trust: double
child 1, max_block_confidence: double
child 2, policy_margin: double
child 3, provenance_trust: double
child 4, taint_level: double
source_bench: string
tool: string
tool_risk: string
true_harm: bool
w_tool_risk: double
version: string
w_identity: double
w_provenance: double
w_block_conf: double
bands: struct<high: list<item: double>, life_critical: list<item: double>, low: list<item: double>, medium: (... 20 chars omitted)
child 0, high: list<item: double>
child 0, item: double
child 1, life_critical: list<item: double>
child 0, item: double
child 2, low: list<item: double>
child 0, item: double
child 3, medium: list<item: double>
child 0, item: double
w_policy: double
w_taint: double
temperature: double
bias: double
to
{'bands': {'high': List(Value('float64')), 'life_critical': List(Value('float64')), 'low': List(Value('float64')), 'medium': List(Value('float64'))}, 'bias': Value('float64'), 'temperature': Value('float64'), 'version': Value('string'), 'w_block_conf': Value('float64'), 'w_identity': Value('float64'), 'w_policy': Value('float64'), 'w_provenance': Value('float64'), 'w_taint': Value('float64'), 'w_tool_risk': Value('float64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
action: struct<args: struct<>, tool: string>
child 0, args: struct<>
child 1, tool: string
category: string
expert_labels: list<item: struct<decision: string, rater: string, severity: int64, ttc_impact_min: int64>>
child 0, item: struct<decision: string, rater: string, severity: int64, ttc_impact_min: int64>
child 0, decision: string
child 1, rater: string
child 2, severity: int64
child 3, ttc_impact_min: int64
gold_decision: string
id: string
legit: bool
signals: struct<identity_trust: double, max_block_confidence: double, policy_margin: double, provenance_trust (... 30 chars omitted)
child 0, identity_trust: double
child 1, max_block_confidence: double
child 2, policy_margin: double
child 3, provenance_trust: double
child 4, taint_level: double
source_bench: string
tool: string
tool_risk: string
true_harm: bool
w_tool_risk: double
version: string
w_identity: double
w_provenance: double
w_block_conf: double
bands: struct<high: list<item: double>, life_critical: list<item: double>, low: list<item: double>, medium: (... 20 chars omitted)
child 0, high: list<item: double>
child 0, item: double
child 1, life_critical: list<item: double>
child 0, item: double
child 2, low: list<item: double>
child 0, item: double
child 3, medium: list<item: double>
child 0, item: double
w_policy: double
w_taint: double
temperature: double
bias: double
to
{'bands': {'high': List(Value('float64')), 'life_critical': List(Value('float64')), 'low': List(Value('float64')), 'medium': List(Value('float64'))}, 'bias': Value('float64'), 'temperature': Value('float64'), 'version': Value('string'), 'w_block_conf': Value('float64'), 'w_identity': Value('float64'), 'w_policy': Value('float64'), 'w_provenance': Value('float64'), 'w_taint': Value('float64'), 'w_tool_risk': Value('float64')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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_harmtriagebench/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}
}
License
Apache License 2.0. Copyright (c) 2026 Quome, Inc.
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