The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
valid: bool
study_id: string
amendment_id: string
prompt_count: int64
law_count: int64
expected_methods_per_prompt: int64
scales: struct<4b: struct<clean_rows: int64, intervention_rows: int64, methods_per_prompt: int64, maximum_co (... 862 chars omitted)
child 0, 4b: struct<clean_rows: int64, intervention_rows: int64, methods_per_prompt: int64, maximum_control_dista (... 212 chars omitted)
child 0, clean_rows: int64
child 1, intervention_rows: int64
child 2, methods_per_prompt: int64
child 3, maximum_control_distance_error: double
child 4, maximum_direction_gram_error: double
child 5, clean_state_shape: list<item: int64>
child 0, item: int64
child 6, clean_logit_shape: list<item: int64>
child 0, item: int64
child 7, development_law_count: int64
child 8, target_development_law_overlap: list<item: null>
child 0, item: null
child 1, 12b: struct<clean_rows: int64, intervention_rows: int64, methods_per_prompt: int64, maximum_control_dista (... 212 chars omitted)
child 0, clean_rows: int64
child 1, intervention_rows: int64
child 2, methods_per_prompt: int64
child 3, maximum_control_distance_error: double
child 4, maximum_direction_gram_error: double
child 5, clean_state_shape: list<item: int64>
child 0, item: int64
child 6, clean_logit_shape: list<item: int64>
child 0, item: int64
child 7, development_law_count: int64
child 8, target_development_law_overlap: list<item: null>
child 0, item: null
child 2, 31b: struct<clean_rows: int64, intervention_rows: int64, methods_per_prompt: int64, maximum_control_dista (... 212 chars omitted)
child 0, clean_rows: int64
child 1, intervention_rows: int64
child 2, methods_per_prompt: int64
child 3, maximum_control_distance_error: double
child 4, maximum_direction_gram_error: double
child 5, clean_state_shape: list<item: int64>
child 0, item: int64
child 6, clean_logit_shape: list<item: int64>
child 0, item: int64
child 7, development_law_count: int64
child 8, target_development_law_overlap: list<item: null>
child 0, item: null
exact_sign_flip_checks: int64
portable_matches: list<item: struct<relative_path: string, sha256_equal: bool>>
child 0, item: struct<relative_path: string, sha256_equal: bool>
child 0, relative_path: string
child 1, sha256_equal: bool
n_hashed_inputs: int64
source_and_manuscript_hashes_unchanged: bool
semantic_disjointness_of_original_24: string
status: string
family_classification: string
to
{'exact_sign_flip_checks': Value('int64'), 'scales': {'4b': {'clean_rows': Value('int64'), 'intervention_rows': Value('int64'), 'law_count': Value('int64'), 'factor_cells_per_law': Value('int64'), 'methods_per_prompt': Value('int64'), 'max_reconstructed_margin_error': Value('float64'), 'max_control_distance_error': {'neutralize_control_': Value('float64'), 'counterfactual_control_': Value('float64')}, 'clean_strict_negative': Value('int64'), 'clean_ties': Value('int64'), 'development_direction': {'n_prompts': Value('int64'), 'n_laws': Value('int64'), 'positive_count': Value('int64'), 'max_direction_error': Value('float64'), 'coordinate_errors': {'midpoint_coordinate': Value('float64'), 'positive_coordinate': Value('float64'), 'negative_coordinate': Value('float64')}, 'max_basis_orthogonality_error': Value('float64'), 'actual_fitted_law_ids': List(Value('string')), 'original_split_labels_all_used': List(Value('string'))}, 'max_clean_logit_margin_error': Value('float64'), 'clean_state_shape': List(Value('int64')), 'clean_logit_shape': List(Value('int64')), 'max_original_law_table_error': Value('float64'), 'max_original_estimate_and_ci_error': Value('float64')}, '12b': {'clean_rows': Value('int64'), 'intervention_rows': Value('int64'), 'law_count': Value('int64'), 'factor_cells_per_law': Value('int64'), 'methods_per_prompt': Value('int64'), 'max_reconstructed_margin_error': Value('float64'), 'max_control_distance_error': {'neutralize_control_': Value('float64'), 'counterfactual_
...
t64'), 'max_original_estimate_and_ci_error': Value('float64')}, '31b': {'clean_rows': Value('int64'), 'intervention_rows': Value('int64'), 'law_count': Value('int64'), 'factor_cells_per_law': Value('int64'), 'methods_per_prompt': Value('int64'), 'max_reconstructed_margin_error': Value('float64'), 'max_control_distance_error': {'neutralize_control_': Value('float64'), 'counterfactual_control_': Value('float64')}, 'clean_strict_negative': Value('int64'), 'clean_ties': Value('int64'), 'development_direction': {'n_prompts': Value('int64'), 'n_laws': Value('int64'), 'positive_count': Value('int64'), 'max_direction_error': Value('float64'), 'coordinate_errors': {'midpoint_coordinate': Value('float64'), 'positive_coordinate': Value('float64'), 'negative_coordinate': Value('float64')}, 'max_basis_orthogonality_error': Value('float64'), 'actual_fitted_law_ids': List(Value('string')), 'original_split_labels_all_used': List(Value('string'))}, 'max_clean_logit_margin_error': Value('float64'), 'clean_state_shape': List(Value('int64')), 'clean_logit_shape': List(Value('int64')), 'max_original_law_table_error': Value('float64'), 'max_original_estimate_and_ci_error': Value('float64')}}, 'portable_matches': List({'relative_path': Value('string'), 'sha256_equal': Value('bool')}), 'status': Value('string'), 'source_and_manuscript_hashes_unchanged': Value('bool'), 'n_hashed_inputs': Value('int64'), 'semantic_disjointness_of_original_24': Value('string'), 'family_classification': 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 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
valid: bool
study_id: string
amendment_id: string
prompt_count: int64
law_count: int64
expected_methods_per_prompt: int64
scales: struct<4b: struct<clean_rows: int64, intervention_rows: int64, methods_per_prompt: int64, maximum_co (... 862 chars omitted)
child 0, 4b: struct<clean_rows: int64, intervention_rows: int64, methods_per_prompt: int64, maximum_control_dista (... 212 chars omitted)
child 0, clean_rows: int64
child 1, intervention_rows: int64
child 2, methods_per_prompt: int64
child 3, maximum_control_distance_error: double
child 4, maximum_direction_gram_error: double
child 5, clean_state_shape: list<item: int64>
child 0, item: int64
child 6, clean_logit_shape: list<item: int64>
child 0, item: int64
child 7, development_law_count: int64
child 8, target_development_law_overlap: list<item: null>
child 0, item: null
child 1, 12b: struct<clean_rows: int64, intervention_rows: int64, methods_per_prompt: int64, maximum_control_dista (... 212 chars omitted)
child 0, clean_rows: int64
child 1, intervention_rows: int64
child 2, methods_per_prompt: int64
child 3, maximum_control_distance_error: double
child 4, maximum_direction_gram_error: double
child 5, clean_state_shape: list<item: int64>
child 0, item: int64
child 6, clean_logit_shape: list<item: int64>
child 0, item: int64
child 7, development_law_count: int64
child 8, target_development_law_overlap: list<item: null>
child 0, item: null
child 2, 31b: struct<clean_rows: int64, intervention_rows: int64, methods_per_prompt: int64, maximum_control_dista (... 212 chars omitted)
child 0, clean_rows: int64
child 1, intervention_rows: int64
child 2, methods_per_prompt: int64
child 3, maximum_control_distance_error: double
child 4, maximum_direction_gram_error: double
child 5, clean_state_shape: list<item: int64>
child 0, item: int64
child 6, clean_logit_shape: list<item: int64>
child 0, item: int64
child 7, development_law_count: int64
child 8, target_development_law_overlap: list<item: null>
child 0, item: null
exact_sign_flip_checks: int64
portable_matches: list<item: struct<relative_path: string, sha256_equal: bool>>
child 0, item: struct<relative_path: string, sha256_equal: bool>
child 0, relative_path: string
child 1, sha256_equal: bool
n_hashed_inputs: int64
source_and_manuscript_hashes_unchanged: bool
semantic_disjointness_of_original_24: string
status: string
family_classification: string
to
{'exact_sign_flip_checks': Value('int64'), 'scales': {'4b': {'clean_rows': Value('int64'), 'intervention_rows': Value('int64'), 'law_count': Value('int64'), 'factor_cells_per_law': Value('int64'), 'methods_per_prompt': Value('int64'), 'max_reconstructed_margin_error': Value('float64'), 'max_control_distance_error': {'neutralize_control_': Value('float64'), 'counterfactual_control_': Value('float64')}, 'clean_strict_negative': Value('int64'), 'clean_ties': Value('int64'), 'development_direction': {'n_prompts': Value('int64'), 'n_laws': Value('int64'), 'positive_count': Value('int64'), 'max_direction_error': Value('float64'), 'coordinate_errors': {'midpoint_coordinate': Value('float64'), 'positive_coordinate': Value('float64'), 'negative_coordinate': Value('float64')}, 'max_basis_orthogonality_error': Value('float64'), 'actual_fitted_law_ids': List(Value('string')), 'original_split_labels_all_used': List(Value('string'))}, 'max_clean_logit_margin_error': Value('float64'), 'clean_state_shape': List(Value('int64')), 'clean_logit_shape': List(Value('int64')), 'max_original_law_table_error': Value('float64'), 'max_original_estimate_and_ci_error': Value('float64')}, '12b': {'clean_rows': Value('int64'), 'intervention_rows': Value('int64'), 'law_count': Value('int64'), 'factor_cells_per_law': Value('int64'), 'methods_per_prompt': Value('int64'), 'max_reconstructed_margin_error': Value('float64'), 'max_control_distance_error': {'neutralize_control_': Value('float64'), 'counterfactual_
...
t64'), 'max_original_estimate_and_ci_error': Value('float64')}, '31b': {'clean_rows': Value('int64'), 'intervention_rows': Value('int64'), 'law_count': Value('int64'), 'factor_cells_per_law': Value('int64'), 'methods_per_prompt': Value('int64'), 'max_reconstructed_margin_error': Value('float64'), 'max_control_distance_error': {'neutralize_control_': Value('float64'), 'counterfactual_control_': Value('float64')}, 'clean_strict_negative': Value('int64'), 'clean_ties': Value('int64'), 'development_direction': {'n_prompts': Value('int64'), 'n_laws': Value('int64'), 'positive_count': Value('int64'), 'max_direction_error': Value('float64'), 'coordinate_errors': {'midpoint_coordinate': Value('float64'), 'positive_coordinate': Value('float64'), 'negative_coordinate': Value('float64')}, 'max_basis_orthogonality_error': Value('float64'), 'actual_fitted_law_ids': List(Value('string')), 'original_split_labels_all_used': List(Value('string'))}, 'max_clean_logit_margin_error': Value('float64'), 'clean_state_shape': List(Value('int64')), 'clean_logit_shape': List(Value('int64')), 'max_original_law_table_error': Value('float64'), 'max_original_estimate_and_ci_error': Value('float64')}}, 'portable_matches': List({'relative_path': Value('string'), 'sha256_equal': Value('bool')}), 'status': Value('string'), 'source_and_manuscript_hashes_unchanged': Value('bool'), 'n_hashed_inputs': Value('int64'), 'semantic_disjointness_of_original_24': Value('string'), 'family_classification': 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.
Gemma materials-science interpretability research archive
Research records supporting Reading and Steering Materials Science-Mechanism Representations in an Open-Weight Language Model, Markus J. Buehler. Release identifier: paper-revision-2026-09-06.
This archive supplies the original observations, supporting state arrays, exact prompts, protocols, intervention records, statistics, analysis source, and generated research figures. It includes the original 4B readout and geometry studies; steering and patching; supplied-equation and equation-free relational experiments; 12B/31B scale extensions; and the three-scale coordinate intervention and retrospective Comment 3 overlap audit. Development and diagnostic records needed to understand those experiments are retained, including negative results and superseded controls. Not every archived development record is a confirmatory endpoint in the paper.
The archive preserves the original jlens_materials/ layout. MANIFEST.json and FILE_INVENTORY.csv enumerate the research-record files with their sizes, SHA-256 hashes, and inclusion basis. Release documentation and verification reports are separately identified; they were prepared after the experiments. RESULT_MAP.csv maps manuscript figures and tables to supporting records and reproduction commands. The final companion manuscript sources, figures, compact SI data, and clean/highlighted PDFs are under paper/; paper/SOURCE_MANIFEST.json identifies that package separately from the original data snapshot.
Download and verify
Use the full dataset commit hash cited by the revised paper/release index:
from huggingface_hub import snapshot_download
snapshot_download(repo_id="lamm-mit/gemma4-interpretability", repo_type="dataset",
revision="e5f5080bdd9ed99ef99b8f81b31f9e93f2de4075", local_dir="gemma4-interpretability")
The hash above retrieves the immutable original research-data snapshot cited in the paper. main additionally contains the final companion manuscript package and updated release documentation; the original research records are unchanged. From the downloaded directory run python verify_release.py. The checker verifies every original research-record file against the manifest. REPRODUCIBILITY.md describes analysis-only reproduction and its validation scope. Work in a copy when rerunning historical analysis scripts because they write derived outputs in place.
Protocol chronology and corrections
“Frozen,” “prespecified,” and historical filenames containing “preregistration” describe the authors' internal research records. They do not, by themselves, establish independently timestamped preregistration. The public GitHub repository was created on 22 July 2026, after the July experiments. This data release is later still. CHRONOLOGY.json and CHRONOLOGY.csv preserve recorded dates and status statements without treating editable timestamps or hashes as external timing evidence. Analyses developed after inspection of data remain post hoc even when a protocol preceded the subsequent calculation.
Original files are retained byte for byte, including historical terminology and original validator labels. For the causal-coordinate experiment, the original validator checked distinct law identifiers; a later audit found three repeated physical relations in the development and target cohorts. The corrected interpretation and sensitivity results are in jlens_materials/experiments/causal-relational-comment3-audit-2026-09-06/ and revised SI S7A/Table S45. Historical classifications in original files should be read alongside that correction. Superseded Gaussian control diagnostics are retained for provenance and excluded from the amended primary inference.
Related resources
- Public source repository
- Fitted Jacobian lenses: three 4B, three 12B, and one 31B paper fits.
- Original exploratory latent vectors
- Reusable prompt dataset
The Gemma base checkpoints and fitted lens weights are not duplicated here. They are unnecessary for recomputing analyses from the saved observations and states; rerunning model inference requires the exact checkpoint/lens revisions and applicable access conditions. The included Apache-2.0 LICENSE and NOTICE originate from the public source repository and cover its source code; upstream model and third-party resource terms remain applicable.
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