Datasets:
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: TypeError
Message: Couldn't cast array of type
struct<score: double>
to
{'snapshots': List({'t': Value('int64'), 'p': Value('int64'), 'm': Value('float64'), 'c': Value('float64'), 'x': Value('null')}), 'analysis': {'temporalDensity': Value('float64'), 'valueStability': Value('float64'), 'coverageScore': Value('int64'), 'avgIntervalMs': Value('int64'), 'totalSnapshots': Value('int64')}}
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 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2109, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2059, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1854, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<score: double>
to
{'snapshots': List({'t': Value('int64'), 'p': Value('int64'), 'm': Value('float64'), 'c': Value('float64'), 'x': Value('null')}), 'analysis': {'temporalDensity': Value('float64'), 'valueStability': Value('float64'), 'coverageScore': Value('int64'), 'avgIntervalMs': Value('int64'), 'totalSnapshots': Value('int64')}}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.
CPS-0001 Benchmark Dataset
The Continuity Lab · v1.0-RC · CC0
Benchmark data, test vectors, and engine performance metrics for CPS-0001 (Continuity Protocol Standard).
Contents
| File | Description |
|---|---|
data/engine-status.json |
Evidence engine pass rates and run counts |
data/test-vectors-valid/ |
3 valid Continuity Receipts (single-engine, multi-engine, agent-trace) |
data/test-vectors-invalid/ |
3 invalid receipts (expired, tampered-evidence, broken-chain) |
Engine Performance
| Engine | Pass Rate | N |
|---|---|---|
| EE-001 Presence Entropy Score | 100% floor | — |
| EE-002 Event-Level Causal Coupling | 58% | 316 |
| EE-003 Gyroscope Challenge | 60% | 150 |
| VS-001 Dual-Engine Verification Session | 93% | 60 |
Known Limitations
Documented in FD-002: individual evidence engines exhibit bounded reliability. CPS-0001 treats evidence composition as a first-class design principle.
Citation
@misc{cps-0001-benchmark-v1,
title = {{CPS-0001 Benchmark Dataset v1.0-RC}},
author = {{The Continuity Lab}},
year = {2026},
url = {https://github.com/myshapeprotocol/myshape-protocol/tree/master/datasets/cps-0001-benchmark}
}
License
CC0 1.0 Universal — public domain dedication.
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