Datasets:

ldh-sky
/

unitybench

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: string
track: string
type: string
difficulty: string
subtype: string
question: string
warns_as_errors: list<item: string>
  child 0, item: string
answer: string
to
{'id': Value('string'), 'track': Value('string'), 'type': Value('string'), 'question': Value('string'), 'answer': Value('string')}
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 478, 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 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2377, 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 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              id: string
              track: string
              type: string
              difficulty: string
              subtype: string
              question: string
              warns_as_errors: list<item: string>
                child 0, item: string
              answer: string
              to
              {'id': Value('string'), 'track': Value('string'), 'type': Value('string'), 'question': Value('string'), 'answer': Value('string')}
              because column names don't match

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UnityBench — by 14Dimension Enterprise

https://www.14dimension.com/

A two-track evaluation for Unity 6 / C# coding models: domain knowledge + real-world bug review.

Generalist code models are strong — but how well do they actually handle Unity 6, and can they find real bugs in real game code without crying wolf? There was no good public answer. This is one.

Built by 14Dimension Enterprise from a shipping Unity Game — bugs and fixes reconstructed from real version-control history. Code: https://github.com/leegod/unitybench

Tracks

1. Knowledge (data/knowledge/, ~146 items) — Unity 6 lifecycle/API Q&A + short "write this MonoBehaviour" compile tasks. Tests whether a model actually knows current Unity 6 (post-cutoff API, common pitfalls).

2. Review (data/review/, 24 buggy + 22 post-fix) — real Unity gameplay methods, each in two states: the pre-fix (buggy) version and the post-fix version, reconstructed from version-control history. We measure recall (does it flag the real bug?) — and the "post-fix" set turned out to be a methodology finding, not a clean specificity control (see below).

Review track — a methodology finding, not a leaderboard

We ran seven models on 24 buggy + 22 "post-fix" gameplay methods, expecting to rank them by recall and specificity. We are not publishing a balance ranking — here's why.

Model	Recall (real bug flagged)	Flag rate on "post-fix" methods
GPT-5.5	100%	91%
Opus 4.8	100%	100%
Qwen3-Coder (open, no context)	92%	77%
Gemini 2.5 Pro	67%	82%
Qwen3-Coder + project context (RAG)	67%	18%
Gemini 3.1 Pro	62%	55%
Claude Sonnet 4.5	46%	64%

A naive harmonic-mean "balance" would rank GPT-5.5 and Opus 4.8 last — and that is wrong. We hand-read their flags on the "post-fix" methods, then ran a convention-aware per-flag judge (cross-checked by two independent judges) across all models: the thorough frontier models' flags are predominantly real review issues (unchecked return values, missing null guards, non-atomic reward grants, missing idempotency, Resources.Load null, OnDestroy on scene-unload), not false positives. The ground truth is the problem: a "post-fix" method is the version where one bug was fixed, not a method with zero issues, so "specificity" punishes the most thorough reviewer. Binary recall/specificity is the wrong instrument; the right one is a per-flag verdict — but two independent judges disagree enough (46–88% agreement) that absolute precision needs human gold labels, which is why only recall + flag-rate are shown here. See the repo README. Treat this as a case study in why bug-detection benchmarks are hard.

Fields

review_bugs: id, code (buggy method), bug (description), severity, context_needed
review_clean: id, code (fixed method)
knowledge: Unity 6 Q&A / compile tasks

License & citation

Data: CC-BY-4.0 — free to use, including commercially, with attribution to 14Dimension Enterprise.

@misc{unitybench2026,
  title  = {UnityBench: Unity 6 Knowledge \& Real-World Bug-Review Evaluation},
  author = {14Dimension Enterprise},
  year   = {2026},
  url    = {https://github.com/leegod/unitybench}
}

— Built and measured hands-on by 14Dimension Enterprise while shipping a Unity game.

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