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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
attempts_per_seed: int64
connected_conditioning: bool
connectivity_reporting: string
construction_verification: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
context_selection: struct<allowed_training_sequence_fraction: double, audit_path: string, audit_sha256: string, guarant (... 373 chars omitted)
child 0, allowed_training_sequence_fraction: double
child 1, audit_path: string
child 2, audit_sha256: string
child 3, guaranteed_maximum_fraction: double
child 4, interpretation: string
child 5, node_capacity: int64
child 6, potentially_truncated_graphs: int64
child 7, sampling_max_length: int64
child 8, selected_context_length: int64
child 9, selection_inputs: string
child 10, selection_path: string
child 11, selection_sha256: string
child 12, status: string
child 13, tokenizer_max_length: int64
child 14, tokenizer_truncation_length: int64
child 15, training_graphs: int64
child 16, truncation: string
distribution_metrics: struct<metrics: list<item: string>, population: string, ratio: string>
child 0, metrics: list<item: string>
child 0, item: string
child 1, population: string
child 2, ratio: string
evaluation_arms: struct<42: struct<derivation: struct<continued_candidate_start: int64, continued_candidate_stop_excl (... 5378 chars omitted)
child 0, 42: struct<derivation: struct<continued_candidate_start: int64, continued_candidate_stop_exclusive: int6 (... 1718 chars omitted)
child 0,
...
hild 1, cross_coarse_pairs: int64
child 2, edge_seed: int64
child 3, expected_cross_coarse_edges: double
child 4, expected_edges: double
child 5, expected_graph_average_degree: double
child 6, expected_total_degree: double
child 7, fine_sizes: list<item: int64>
child 0, item: int64
child 8, fine_to_coarse: list<item: int64>
child 0, item: int64
child 9, generator: string
child 10, n: int64
child 11, n_coarse: int64
child 12, n_fine: int64
child 13, p_base_within_coarse: double
child 14, p_out: double
child 15, per_coarse: list<item: struct<A: int64, B: int64, coarse_id: int64, coarse_size: int64, expected_edges: double, (... 94 chars omitted)
child 0, item: struct<A: int64, B: int64, coarse_id: int64, coarse_size: int64, expected_edges: double, fine_sizes: (... 82 chars omitted)
child 0, A: int64
child 1, B: int64
child 2, coarse_id: int64
child 3, coarse_size: int64
child 4, expected_edges: double
child 5, fine_sizes: list<item: int64>
child 0, item: int64
child 6, flat_expected_edges: double
child 7, p_fine: double
child 8, p_middle: double
child 16, ratio: double
child 17, template: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
child 18, within_coarse_pairs: int64
fine_labels: list<item: int64>
child 0, item: int64
template_seed: int64
node_permutation_seed: int64
to
{'base_seed': Value('int64'), 'candidate_index': Value('int64'), 'coarse_labels': List(Value('int64')), 'derived_provenance': {'action': Value('string'), 'connected_conditioning': Value('bool'), 'source_arm_position': Value('int64'), 'source_arm_seed': Value('int64'), 'source_candidate_index': Value('int64'), 'source_protocol': Value('string')}, 'edge_seed': Value('int64'), 'edges': List(List(Value('int64'))), 'fine_labels': List(Value('int64')), 'fine_to_coarse': List(Value('int64')), 'metadata': {'coarse_sizes': List(Value('int64')), 'cross_coarse_pairs': Value('int64'), 'edge_seed': Value('int64'), 'expected_cross_coarse_edges': Value('float64'), 'expected_edges': Value('float64'), 'expected_graph_average_degree': Value('float64'), 'expected_total_degree': Value('float64'), 'fine_sizes': List(Value('int64')), 'fine_to_coarse': List(Value('int64')), 'generator': Value('string'), 'n': Value('int64'), 'n_coarse': Value('int64'), 'n_fine': Value('int64'), 'p_base_within_coarse': Value('float64'), 'p_out': Value('float64'), 'per_coarse': List({'A': Value('int64'), 'B': Value('int64'), 'coarse_id': Value('int64'), 'coarse_size': Value('int64'), 'expected_edges': Value('float64'), 'fine_sizes': List(Value('int64')), 'flat_expected_edges': Value('float64'), 'p_fine': Value('float64'), 'p_middle': Value('float64')}), 'ratio': Value('float64'), 'template': List(List(Value('int64'))), 'within_coarse_pairs': Value('int64')}, 'node_permutation_seed': Value('int64'), 'num_nodes': Value('int64'), 'template_seed': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1835, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
attempts_per_seed: int64
connected_conditioning: bool
connectivity_reporting: string
construction_verification: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
context_selection: struct<allowed_training_sequence_fraction: double, audit_path: string, audit_sha256: string, guarant (... 373 chars omitted)
child 0, allowed_training_sequence_fraction: double
child 1, audit_path: string
child 2, audit_sha256: string
child 3, guaranteed_maximum_fraction: double
child 4, interpretation: string
child 5, node_capacity: int64
child 6, potentially_truncated_graphs: int64
child 7, sampling_max_length: int64
child 8, selected_context_length: int64
child 9, selection_inputs: string
child 10, selection_path: string
child 11, selection_sha256: string
child 12, status: string
child 13, tokenizer_max_length: int64
child 14, tokenizer_truncation_length: int64
child 15, training_graphs: int64
child 16, truncation: string
distribution_metrics: struct<metrics: list<item: string>, population: string, ratio: string>
child 0, metrics: list<item: string>
child 0, item: string
child 1, population: string
child 2, ratio: string
evaluation_arms: struct<42: struct<derivation: struct<continued_candidate_start: int64, continued_candidate_stop_excl (... 5378 chars omitted)
child 0, 42: struct<derivation: struct<continued_candidate_start: int64, continued_candidate_stop_exclusive: int6 (... 1718 chars omitted)
child 0,
...
hild 1, cross_coarse_pairs: int64
child 2, edge_seed: int64
child 3, expected_cross_coarse_edges: double
child 4, expected_edges: double
child 5, expected_graph_average_degree: double
child 6, expected_total_degree: double
child 7, fine_sizes: list<item: int64>
child 0, item: int64
child 8, fine_to_coarse: list<item: int64>
child 0, item: int64
child 9, generator: string
child 10, n: int64
child 11, n_coarse: int64
child 12, n_fine: int64
child 13, p_base_within_coarse: double
child 14, p_out: double
child 15, per_coarse: list<item: struct<A: int64, B: int64, coarse_id: int64, coarse_size: int64, expected_edges: double, (... 94 chars omitted)
child 0, item: struct<A: int64, B: int64, coarse_id: int64, coarse_size: int64, expected_edges: double, fine_sizes: (... 82 chars omitted)
child 0, A: int64
child 1, B: int64
child 2, coarse_id: int64
child 3, coarse_size: int64
child 4, expected_edges: double
child 5, fine_sizes: list<item: int64>
child 0, item: int64
child 6, flat_expected_edges: double
child 7, p_fine: double
child 8, p_middle: double
child 16, ratio: double
child 17, template: list<item: list<item: int64>>
child 0, item: list<item: int64>
child 0, item: int64
child 18, within_coarse_pairs: int64
fine_labels: list<item: int64>
child 0, item: int64
template_seed: int64
node_permutation_seed: int64
to
{'base_seed': Value('int64'), 'candidate_index': Value('int64'), 'coarse_labels': List(Value('int64')), 'derived_provenance': {'action': Value('string'), 'connected_conditioning': Value('bool'), 'source_arm_position': Value('int64'), 'source_arm_seed': Value('int64'), 'source_candidate_index': Value('int64'), 'source_protocol': Value('string')}, 'edge_seed': Value('int64'), 'edges': List(List(Value('int64'))), 'fine_labels': List(Value('int64')), 'fine_to_coarse': List(Value('int64')), 'metadata': {'coarse_sizes': List(Value('int64')), 'cross_coarse_pairs': Value('int64'), 'edge_seed': Value('int64'), 'expected_cross_coarse_edges': Value('float64'), 'expected_edges': Value('float64'), 'expected_graph_average_degree': Value('float64'), 'expected_total_degree': Value('float64'), 'fine_sizes': List(Value('int64')), 'fine_to_coarse': List(Value('int64')), 'generator': Value('string'), 'n': Value('int64'), 'n_coarse': Value('int64'), 'n_fine': Value('int64'), 'p_base_within_coarse': Value('float64'), 'p_out': Value('float64'), 'per_coarse': List({'A': Value('int64'), 'B': Value('int64'), 'coarse_id': Value('int64'), 'coarse_size': Value('int64'), 'expected_edges': Value('float64'), 'fine_sizes': List(Value('int64')), 'flat_expected_edges': Value('float64'), 'p_fine': Value('float64'), 'p_middle': Value('float64')}), 'ratio': Value('float64'), 'template': List(List(Value('int64'))), 'within_coarse_pairs': Value('int64')}, 'node_permutation_seed': Value('int64'), 'num_nodes': Value('int64'), 'template_seed': Value('int64')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
base_seed int64 | candidate_index int64 | coarse_labels list | derived_provenance dict | edge_seed int64 | edges list | fine_labels list | fine_to_coarse list | metadata dict | node_permutation_seed int64 | num_nodes int64 | template_seed int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|
3,340,189,808 | 1 | [
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"expected_total_degree": 1538.46,
"fine_sizes": [
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3,340,189,808 | 2 | [
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"action": "retained_connected_source_reference",
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"source_candidate_index": 2,
"source_protocol": "rsbm_spectre128_v1"
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"fine_sizes": [
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"action": "retained_connected_source_reference",
"connected_conditioning": true,
"source_arm_position": 5,
"source_arm_seed": 42,
"source_candidate_index": 5,
"source_protocol": "rsbm_spectre128_v1"
} | 2,249,163,639 | [
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End of preview.
MOSAIC NeurIPS 2026 — data
Paper-scoped scientific backup, organized as:
molecular/<dataset>/<variant>/...
non-molecular/<dataset>/<variant>/...
metadata/...
Datasets: GuacaMol (guacamol), MOSES (moses), COCONUT (coconut2), GEOM-Drugs disjoint split (geom-drugs), Macrocycle (macrocycle), Proteins (proteins), Planar (planar), SBM (sbm), SSBM (ssbm), and RSBM R=4 original/connected populations (rsbm-r4-original, rsbm-r4-connected).
Variants HAC-R, HAC-FR, MC-R, MC-FR, PH-R, and PH-FR denote the paper's HDT variants. SENT, HSENT-HAC-R, HSENT-MC-R, and HDTC-R retain their distinct meanings. Shared data/references are stored once under shared; HAC, MC, PH, HDTC, and Louvain in the data repository identify a reusable cache family, not an additional trained model.
Names and settings
Checkpoint names follow:
<dataset>_<variant>_<backbone>_steps<N>_batch<B>_ctx<L>_lr<rate>[_tau<T>]_seed<S>[_online|_precomputed]_last.ckpt
For example:
molecular/guacamol/HAC-FR/guacamol_HAC-FR_gpt2-xs_steps100000_batch256_ctx512_lr0.0006_tau4_seed42_last.ckpt
batch is the effective global batch including devices and gradient accumulation. tau is the HAC community-size threshold and is omitted for methods that do not use HAC. seed in the run name is the training seed; evaluation seeds are separate subdirectories. Full settings and exact paper locations appear in metadata/neurips2026_run_catalog.json; frozen training configurations are in the results repository alongside each run. Different context lengths, thresholds, schedules and model sizes remain distinguishable.
Data paths also identify the cached population, split/shard, coarsening family and threshold where applicable. Results are grouped by run, evaluation protocol, motif and evaluation seed. Cross-dataset figure/source metadata lives under metadata/.
Scope and integrity
This backup follows manuscript revision 559f541889ed6156d5af3e801055f25bc74cc3d1. It is not an archive of every exploratory experiment. Only LAST checkpoints are included; optimizer and training state are retained. Conditional results are motif-conditioned only. Historical HAC-FR-Top PGD files accidentally included in the previous selection are explicitly excluded because that variant is absent from this paper revision.
All scientific payloads are preserved byte-for-byte. Standardized names apply to repository paths. Frozen configurations, checkpoint fields and provenance may still contain original experiment labels or paths; changing those would alter the research artifacts. The manifest maps each standard path back to its exact original path and records its SHA256. Historical Git commits are retained; the previous layout is removed from the current repository tree after verification.
Restore
Download neurips2026_backup_index.json from the results repository and restore_neurips_backup.py from any of the three repositories. Install huggingface_hub, then run:
python restore_neurips_backup.py --index neurips2026_backup_index.json --destination /path/to/backup
The default produces <destination>/<data|checkpoints|results>/<standard repository path>. To reconstruct the original code-compatible project tree, add --layout original. Original absolute paths inside some frozen manifests may require an equivalent project-root alias. Use --category data|checkpoints|results and repeatable --path <standard-or-original-path> to select a subset. Downloads are revision-pinned and checksum-verified; identical existing files are skipped and different existing files are never overwritten.
Repositories: data, checkpoints, results.
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