EuroExec: Frontier Language Models Fall Short of Expert Judgment on European Executive Decision Tasks
Paper • 2608.04549 • Published
Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
contributor_id: string
response_key: string
row_index: int64
state: string
is_critical: bool
row: int64
text: string
to
{'row': Value('int64'), 'text': Value('string'), 'is_critical': Value('bool')}
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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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
contributor_id: string
response_key: string
row_index: int64
state: string
is_critical: bool
row: int64
text: string
to
{'row': Value('int64'), 'text': Value('string'), 'is_critical': Value('bool')}
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.
AI Summit Barcelona 2026 — Rubric Agreement Workshop
Every evaluation filed during a live 80-minute workshop at AI Summit Barcelona on 22 September 2026, in which the room scored one expert-authored benchmark item exactly the way an annotator does it on EuroExec (arXiv:2608.04549), Sovrano's expert-graded benchmark of frontier models on European executive decisions.
Attendees read one executive task and its graded checklist, then blind-scored three frontier-model answers: five dimensions from 1 to 5, then Hit / Partial / Miss on every checklist row, then a ranking and a written rationale. Which model wrote which answer was hidden until the end, and the A/B/C labels were the same on every screen, so one attendee's numbers sit next to another's.
Sovrano's own evaluation of the same three answers is published alongside, which is what
makes this comparable: scorings.jsonl carries each attendee's Cohen's κ against that
expert key, over the same Hit / Partial / Miss calls. On EuroExec, two blind scorers below
κ 0.7 halt production of an item.
What's in it
| File | Rows | One row is |
|---|---|---|
scorings.jsonl |
16 | One attendee's finished evaluation, with their agreement against the expert key |
dimension_scores.jsonl |
240 | One attendee scoring one response on one dimension, 1–5 |
coverage.jsonl |
240 | One attendee's Hit / Partial / Miss call on one checklist row of one response |
responses.jsonl |
3 | One of the three answers under test, with the model that wrote it |
checklist.jsonl |
5 | One row of the graded checklist, and whether it is critical |
dimensions.jsonl |
5 | One of the five scoring dimensions, as it was shown |
golden.jsonl |
3 | Sovrano's expert evaluation of one response, with a note per checklist row |
The task all three answers respond to:
Act as a senior product strategist for a B2B subscription SaaS Platform providing healthcare referral tracking system using AI in a startup that just received seed funding of €1M. You have piloted the product among 50 hospitals in Germany for a one-time trial fee of €2500. From July onwards you would like to start billing all 50 customers €250 monthly as a subscription fee. However, your customer support team has reported that user reviews have been increasingly overwhelming and unsatisfactory and that there are at least 30% 1 star (very bad) ratings coming in. The board expects evidence-based recommendations before subscription billing begins. Your task is to create a product strategy report based on customer feedback and subscription launch readiness. Diagnose the business situation identifying at least 5 risks to customer journey, subscription sign ups and product adoption. Design a review analysis process capable of reviewing 50 reviews per day. The process must categorize per theme, its frequency, calculate severity and distinguish feature requests from defects. Define a measurement framework containing the KPIs and metrics you come up with. Do not invent perfect outcomes, explicitly identify uncertainties and additional data that should be collected before scaling.
Caveats that matter
- 37 unfinished evaluations are not here. A scoring is exported once the attendee submitted it, which the app only accepts when every dimension is scored, every checklist row is marked and a rationale is written. Partial work was left out rather than published as if it were a considered call.
- Scoring was not blind. The workshop app holds nothing back: the model behind each answer and the expert key are on the results screen from the start, and an attendee could read them before scoring. Most did not — the screens lead you through scoring first — but this data cannot be used as evidence about blind annotation, and the figures here should be read as what a room of non-specialists produced with the key available.
- The expert key is a small sample of one. It is Sovrano's grading of three answers against five rows. κ against it says how closely a reader applied the same checklist, not who was right.
- Attendees are not expert annotators. They are conference attendees working at speed, which is the point of the comparison with EuroExec, not a defect to correct for.
- Contributors are pseudonymous.
contributor_idis a random per-browser identifier. No personal data appears in this dataset and nothing in it can be traced back to a person. Attendees gave their details to the organisers on the way in; those were stored separately, were never linked to a contributor id, and are not part of this release.
Licence
CC BY 4.0. Please cite this dataset and EuroExec if you build on it.
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