The dataset viewer is not available for this subset.
Exception: ValueError
Message: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('json', {}), NamedSplit('test'): (None, {})}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 268, in get_dataset_config_info
builder = load_dataset_builder(
path,
...<6 lines>...
**config_kwargs,
)
File "/src/services/worker/src/worker/utils.py", line 447, in safe_load_dataset_builder
dataset_module = dataset_module_factory(
repo_dir,
revision=revision,
download_config=download_config,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1182, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1157, in dataset_module_factory
).get_module()
~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 647, in get_module
module_name, default_builder_kwargs = infer_module_for_data_files(
~~~~~~~~~~~~~~~~~~~~~~~~~~~^
data_files=data_files,
^^^^^^^^^^^^^^^^^^^^^^
path=self.name,
^^^^^^^^^^^^^^^
download_config=self.download_config,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 307, in infer_module_for_data_files
raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}")
ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('json', {}), NamedSplit('test'): (None, {})}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
MOSAIC NeurIPS 2026 — results
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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