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
address_digest: string
binary_values: struct<bytes: int64, path: string, sha256: string>
child 0, bytes: int64
child 1, path: string
child 2, sha256: string
format: string
state_count: int64
state_dimensions: int64
support_threshold: int64
stream_ordinal: int64
canonicals: list<item: struct<address_digest: string, canonical_id: string, encoding_id: string, versions: list< (... 303 chars omitted)
child 0, item: struct<address_digest: string, canonical_id: string, encoding_id: string, versions: list<item: struc (... 291 chars omitted)
child 0, address_digest: string
child 1, canonical_id: string
child 2, encoding_id: string
child 3, versions: list<item: struct<admitted_at_stream_ordinal: int64, canonical_id: string, encoding_id: string, occu (... 206 chars omitted)
child 0, item: struct<admitted_at_stream_ordinal: int64, canonical_id: string, encoding_id: string, occurrence_ids: (... 194 chars omitted)
child 0, admitted_at_stream_ordinal: int64
child 1, canonical_id: string
child 2, encoding_id: string
child 3, occurrence_ids: list<item: string>
child 0, item: string
child 4, predecessor_version_id: string
child 5, sequenced_address: struct<address_digest: string, state_count: int64, state_dimensions: int64>
child 0, address_digest: string
child 1, state_count: int64
child 2, state_dimensions: int64
child 6, support_threshold: int64
child 7, version_id: string
source_episodes_embedded: bool
canonical_count: int64
observed_episode_ids: list<item: string>
child 0, item: string
to
{'canonical_count': Value('int64'), 'canonicals': List({'address_digest': Value('string'), 'canonical_id': Value('string'), 'encoding_id': Value('string'), 'versions': List({'admitted_at_stream_ordinal': Value('int64'), 'canonical_id': Value('string'), 'encoding_id': Value('string'), 'occurrence_ids': List(Value('string')), 'predecessor_version_id': Value('string'), 'sequenced_address': {'address_digest': Value('string'), 'state_count': Value('int64'), 'state_dimensions': Value('int64')}, 'support_threshold': Value('int64'), 'version_id': Value('string')})}), 'format': Value('string'), 'observed_episode_ids': List(Value('string')), 'source_episodes_embedded': Value('bool'), 'stream_ordinal': Value('int64'), 'support_threshold': Value('int64')}
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
address_digest: string
binary_values: struct<bytes: int64, path: string, sha256: string>
child 0, bytes: int64
child 1, path: string
child 2, sha256: string
format: string
state_count: int64
state_dimensions: int64
support_threshold: int64
stream_ordinal: int64
canonicals: list<item: struct<address_digest: string, canonical_id: string, encoding_id: string, versions: list< (... 303 chars omitted)
child 0, item: struct<address_digest: string, canonical_id: string, encoding_id: string, versions: list<item: struc (... 291 chars omitted)
child 0, address_digest: string
child 1, canonical_id: string
child 2, encoding_id: string
child 3, versions: list<item: struct<admitted_at_stream_ordinal: int64, canonical_id: string, encoding_id: string, occu (... 206 chars omitted)
child 0, item: struct<admitted_at_stream_ordinal: int64, canonical_id: string, encoding_id: string, occurrence_ids: (... 194 chars omitted)
child 0, admitted_at_stream_ordinal: int64
child 1, canonical_id: string
child 2, encoding_id: string
child 3, occurrence_ids: list<item: string>
child 0, item: string
child 4, predecessor_version_id: string
child 5, sequenced_address: struct<address_digest: string, state_count: int64, state_dimensions: int64>
child 0, address_digest: string
child 1, state_count: int64
child 2, state_dimensions: int64
child 6, support_threshold: int64
child 7, version_id: string
source_episodes_embedded: bool
canonical_count: int64
observed_episode_ids: list<item: string>
child 0, item: string
to
{'canonical_count': Value('int64'), 'canonicals': List({'address_digest': Value('string'), 'canonical_id': Value('string'), 'encoding_id': Value('string'), 'versions': List({'admitted_at_stream_ordinal': Value('int64'), 'canonical_id': Value('string'), 'encoding_id': Value('string'), 'occurrence_ids': List(Value('string')), 'predecessor_version_id': Value('string'), 'sequenced_address': {'address_digest': Value('string'), 'state_count': Value('int64'), 'state_dimensions': Value('int64')}, 'support_threshold': Value('int64'), 'version_id': Value('string')})}), 'format': Value('string'), 'observed_episode_ids': List(Value('string')), 'source_episodes_embedded': Value('bool'), 'stream_ordinal': Value('int64'), 'support_threshold': 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.
Embedding Exact Recurrence
Complete evidence for the exact repeated-episode consolidation trial in
hydra-dynamix/embedding-exact-recurrance.
Contents
- 30 independently encoded episode occurrences;
- three exact episodes repeated five times each;
- 15 interleaved one-off novel episodes;
- uncompressed input, token,
H,N, andB = A_eevidence; - persistent collision-safe episode indexes;
- support-threshold 4 and 5 canonical catalogs;
- complete results, findings, and independent audit;
full-data-manifest.jsonwith SHA-256 and byte size for every uploaded file.
Result
Both occurrence-support thresholds created exactly one canonical identity for each repeated (encoding_id, A_e) and promoted none of the novel episodes. Every repeated query returned all five immutable source occurrences, and every canonical hydrated and replayed exactly after restart.
Threshold 4 admitted on the fourth occurrence and retained immutable support-4 and support-5 versions. Threshold 5 admitted on the fifth occurrence and retained one support-5 version.
Frozen representation
model: Qwen/Qwen3-Embedding-0.6B
revision: 97b0c614be4d77ee51c0cef4e5f07c00f9eb65b3
normalization: row-minmax-v1
representation threshold: 0.8
consolidation occurrence-support thresholds: 4 and 5
The texts are synthetic. Source episodes remain directly addressable; canonical records contain bindings rather than replacing or deleting evidence.
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