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
cross_seed_summaries: list<item: struct<condition: string, max_seed_mean: double, mean_of_seed_means: double, metric: stri (... 87 chars omitted)
child 0, item: struct<condition: string, max_seed_mean: double, mean_of_seed_means: double, metric: string, min_see (... 75 chars omitted)
child 0, condition: string
child 1, max_seed_mean: double
child 2, mean_of_seed_means: double
child 3, metric: string
child 4, min_seed_mean: double
child 5, n_seeds: int64
child 6, std_of_seed_means: double
child 7, support: string
development_outcome_category: string
episodes_total: int64
implementation_sha256: string
leakage_audit: struct<heldout_sign_decoder: struct<majority_baseline: double, n_test: int64, n_train: int64, test_a (... 77 chars omitted)
child 0, heldout_sign_decoder: struct<majority_baseline: double, n_test: int64, n_train: int64, test_accuracy: double>
child 0, majority_baseline: double
child 1, n_test: int64
child 2, n_train: int64
child 3, test_accuracy: double
child 1, interpretation: string
child 2, membership_vs_sign_pearson: double
protocol: string
runtime_seconds_total: double
scientific_status: string
seed_manifests: list<item: struct<episodes: int64, runtime_seconds: double, seed: int64, status: string>>
child 0, item: struct<episodes: int64, runtime_seconds: double, seed: int64, status: string>
child 0, episodes: int64
child 1, runtime_seconds: double
child 2, seed: int64
child 3, status: string
seeds: list<item: int64>
child 0, item: int64
scan_scope: string
repository: string
files: list<item: struct<path: string, sha256: string, size_bytes: int64>>
child 0, item: struct<path: string, sha256: string, size_bytes: int64>
child 0, path: string
child 1, sha256: string
child 2, size_bytes: int64
release_date: timestamp[s]
private_name_scan: string
to
{'release_date': Value('timestamp[s]'), 'repository': Value('string'), 'private_name_scan': Value('string'), 'scan_scope': Value('string'), 'files': List({'path': Value('string'), 'sha256': Value('string'), 'size_bytes': Value('int64')})}
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
cross_seed_summaries: list<item: struct<condition: string, max_seed_mean: double, mean_of_seed_means: double, metric: stri (... 87 chars omitted)
child 0, item: struct<condition: string, max_seed_mean: double, mean_of_seed_means: double, metric: string, min_see (... 75 chars omitted)
child 0, condition: string
child 1, max_seed_mean: double
child 2, mean_of_seed_means: double
child 3, metric: string
child 4, min_seed_mean: double
child 5, n_seeds: int64
child 6, std_of_seed_means: double
child 7, support: string
development_outcome_category: string
episodes_total: int64
implementation_sha256: string
leakage_audit: struct<heldout_sign_decoder: struct<majority_baseline: double, n_test: int64, n_train: int64, test_a (... 77 chars omitted)
child 0, heldout_sign_decoder: struct<majority_baseline: double, n_test: int64, n_train: int64, test_accuracy: double>
child 0, majority_baseline: double
child 1, n_test: int64
child 2, n_train: int64
child 3, test_accuracy: double
child 1, interpretation: string
child 2, membership_vs_sign_pearson: double
protocol: string
runtime_seconds_total: double
scientific_status: string
seed_manifests: list<item: struct<episodes: int64, runtime_seconds: double, seed: int64, status: string>>
child 0, item: struct<episodes: int64, runtime_seconds: double, seed: int64, status: string>
child 0, episodes: int64
child 1, runtime_seconds: double
child 2, seed: int64
child 3, status: string
seeds: list<item: int64>
child 0, item: int64
scan_scope: string
repository: string
files: list<item: struct<path: string, sha256: string, size_bytes: int64>>
child 0, item: struct<path: string, sha256: string, size_bytes: int64>
child 0, path: string
child 1, sha256: string
child 2, size_bytes: int64
release_date: timestamp[s]
private_name_scan: string
to
{'release_date': Value('timestamp[s]'), 'repository': Value('string'), 'private_name_scan': Value('string'), 'scan_scope': Value('string'), 'files': List({'path': Value('string'), 'sha256': Value('string'), 'size_bytes': Value('int64')})}
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.
Cross-Form Learning Consequence
A small controlled experiment asking whether a useful consequence of learning can survive after the task-specific predictive representation that produced it is destroyed.
The task family is deliberately transparent: 16-dimensional synthetic binary classification with four active coordinates and manual NumPy logistic SGD.
Experiment
- Learn a task-specific predictor
R. - Derive a lower-bandwidth four-coordinate structural outline
Sfrom|R|. - Destroy
Rand reset the future predictor to zero. - Use
Sonly to bias the directional allocation of future gradient updates. - Compare against neutral, shuffled-history, wrong-family, random-outline, oracle, no-history, and direct-preservation controls.
Structured updates are rescaled every step so their L2 norm matches the ordinary gradient norm. The intervention therefore changes allocation/direction rather than step magnitude.
Result
Across 1,024 paired episodes:
- the destroyed predictor returned to 0.500000 accuracy on every episode;
- the structural outline recovered the hidden support exactly on 98.83% of episodes;
- transformed-minus-neutral normalized accuracy-AUC was +0.080524;
- oracle-minus-neutral was +0.081000;
- the fixed transform recovered 99.42% of oracle structural headroom across seed means.
This is an instrument-level existence result, not a learned memory or architecture result. The transformation was fixed by the experimenter.
Run
python code/run_experiment.py --self-test
Full exposed cohort:
python code/run_experiment.py --output-root ./runs
Repository contents
code/run_experiment.py— public-release copy of the frozen NumPy implementationprotocol/FROZEN_PROTOCOL_PUBLIC_RELEASE.md— complete protocol with public namingresults/aggregate_summary.jsonresults/per_seed_summary.csvresults/episode_summary.csvresults/assertion_log.txtresults/development_result.mdnotebooks/RUN_IN_COLAB.ipynbdocs/SOURCE_CUSTODY.md
Scope
The result applies to this deliberately constructed synthetic instrument. It does not establish general continual-learning performance, learned persistence-form selection, architecture independence, or superiority to standard transfer/meta-learning methods.
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