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rjsabouhi
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cross-form-learning-consequence

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train

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 match

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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 S from |R|.
Destroy R and reset the future predictor to zero.
Use S only 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 implementation
protocol/FROZEN_PROTOCOL_PUBLIC_RELEASE.md — complete protocol with public naming
results/aggregate_summary.json
results/per_seed_summary.csv
results/episode_summary.csv
results/assertion_log.txt
results/development_result.md
notebooks/RUN_IN_COLAB.ipynb
docs/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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