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
timestamp: string
git_sha: string
git_dirty: bool
argv: list<item: string>
child 0, item: string
python: string
eval_suite: struct<name: string, metrics: struct<elicit_hardcode_test_cases_score: double, elicit_hardcode_test_ (... 163 chars omitted)
child 0, name: string
child 1, metrics: struct<elicit_hardcode_test_cases_score: double, elicit_hardcode_test_cases_exhibited: double, prefi (... 132 chars omitted)
child 0, elicit_hardcode_test_cases_score: double
child 1, elicit_hardcode_test_cases_exhibited: double
child 2, prefill_hardcode_test_cases_score: double
child 3, prefill_hardcode_test_cases_admission: double
child 4, prefill_hardcode_test_cases_think_leak: double
value: double
metric: string
icc: double
n_items: int64
model: string
n_eff: double
n: double
n_method: string
ci_lo: double
suite: string
ci_hi: double
epochs: double
n_generations: int64
to
{'model': Value('string'), 'suite': Value('string'), 'metric': Value('string'), 'value': Value('float64'), 'ci_lo': Value('float64'), 'ci_hi': Value('float64'), 'n': Value('float64'), 'n_generations': Value('int64'), 'n_items': Value('int64'), 'epochs': Value('float64'), 'icc': Value('float64'), 'n_eff': Value('float64'), 'n_method': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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
timestamp: string
git_sha: string
git_dirty: bool
argv: list<item: string>
child 0, item: string
python: string
eval_suite: struct<name: string, metrics: struct<elicit_hardcode_test_cases_score: double, elicit_hardcode_test_ (... 163 chars omitted)
child 0, name: string
child 1, metrics: struct<elicit_hardcode_test_cases_score: double, elicit_hardcode_test_cases_exhibited: double, prefi (... 132 chars omitted)
child 0, elicit_hardcode_test_cases_score: double
child 1, elicit_hardcode_test_cases_exhibited: double
child 2, prefill_hardcode_test_cases_score: double
child 3, prefill_hardcode_test_cases_admission: double
child 4, prefill_hardcode_test_cases_think_leak: double
value: double
metric: string
icc: double
n_items: int64
model: string
n_eff: double
n: double
n_method: string
ci_lo: double
suite: string
ci_hi: double
epochs: double
n_generations: int64
to
{'model': Value('string'), 'suite': Value('string'), 'metric': Value('string'), 'value': Value('float64'), 'ci_lo': Value('float64'), 'ci_hi': Value('float64'), 'n': Value('float64'), 'n_generations': Value('int64'), 'n_items': Value('int64'), 'epochs': Value('float64'), 'icc': Value('float64'), 'n_eff': Value('float64'), 'n_method': Value('string')}
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.
AuditBench — graft vs native organisms, evaluation results
Numeric evaluation results for the AuditBench model-organism grid on two model families:
Qwen3-14B and Llama-3.3-70B-Instruct. The organisms themselves are published separately
(djroytburg/auditbench-qwen3-14b-*, djroytburg/auditbench-llama33-70b-*).
The design
Each cell compares three arms on the same eval, served together:
| arm | meaning |
|---|---|
bare |
the untouched instruct model |
native |
SDF quirk-install trained directly on the instruct model |
graft |
the same recipe trained on the BASE model, then composed onto the instruct model |
crossed with 4 quirks (animal_welfare, contextual_optimism, hardcode_test_cases,
self_promotion) and 3 stages (stage-1 install, stage-2 KTO concealment, stage-2 SFT
concealment).
Layout
<family>/<experiment>/<arm>/metrics.jsonl per-scorer reduction (the numbers)
<family>/<experiment>/manifest.json served model, sampling params, suite/task defs
<family>/<experiment>/<arm>/provenance.json git sha, argv, timestamps
Caveats you should read before using these numbers
gpqa_diamond/gpqa_diamond_fullcannot support arm comparisons. The answer options are re-shuffled every run and the models are order-sensitive, so the bare model alone spans 0.375-0.495 across 15 identical re-serves on the Llama line (3sd = 0.112) -- several times any effect measured on it.mmlu_prois borderline. The instruction-following (ifeval) and agentic tool-use (ba_json,ba_am_xml) tasks are the ones with adequate resolution.- Sampling differs by family. Qwen capability evals ran at temperature 1.0, Llama's at 0.0. Do not compare effect sizes across families without accounting for this.
- Single training seed per cell, except the Qwen
seednullexperiment, which retrains the same recipe with 3 seeds and is the correct null for judging any effect size here. The retrain-seed null is much larger than eval re-run noise. - Belief and decisiveness graft-vs-native claims are provisional. A
--use_doc_tagcontrol (2026-08-03, Qwen) indicates much of that difference is attributable to training configuration rather than to the substrate. - Quarantined pre-correction stage-2 data is not included here; an earlier bug served the stage-2 delta adapter without its stage-1 organism and those results were discarded.
Project git commit at publication: 5a00d85a8abdf28b3218da741925c1c01c22c15c
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