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<generation: struct<type: string, minimum: int64>, failure_class: struct<type: string, enum: list<item: string>>, analysis: struct<type: string>, inner_prompt_patch: struct<type: string>, hyperparams: struct<type: string, required: list<item: string>, properties: struct<temperature: struct<type: string, minimum: double, maximum: double>, top_p: struct<type: string, minimum: double, maximum: double>, logit_bias: struct<type: string>>>, worm_note: struct<type: string>>
to
{'temperature': {'type': Value('string'), 'minimum': Value('float64'), 'maximum': Value('float64'), 'default': Value('float64')}, 'top_p': {'type': Value('string'), 'minimum': Value('float64'), 'maximum': Value('float64'), 'default': Value('float64')}, 'logit_bias': {'type': Value('string'), 'description': Value('string'), 'additionalProperties': {'type': Value('string'), 'minimum': Value('int64'), 'maximum': Value('int64')}}, 'max_tokens': {'type': Value('string'), 'minimum': Value('int64'), 'default': 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 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<generation: struct<type: string, minimum: int64>, failure_class: struct<type: string, enum: list<item: string>>, analysis: struct<type: string>, inner_prompt_patch: struct<type: string>, hyperparams: struct<type: string, required: list<item: string>, properties: struct<temperature: struct<type: string, minimum: double, maximum: double>, top_p: struct<type: string, minimum: double, maximum: double>, logit_bias: struct<type: string>>>, worm_note: struct<type: string>>
to
{'temperature': {'type': Value('string'), 'minimum': Value('float64'), 'maximum': Value('float64'), 'default': Value('float64')}, 'top_p': {'type': Value('string'), 'minimum': Value('float64'), 'maximum': Value('float64'), 'default': Value('float64')}, 'logit_bias': {'type': Value('string'), 'description': Value('string'), 'additionalProperties': {'type': Value('string'), 'minimum': Value('int64'), 'maximum': Value('int64')}}, 'max_tokens': {'type': Value('string'), 'minimum': Value('int64'), 'default': 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.
Twin-O-Matic (TOM)
Recursive self-improvement loop with WORM-sealed audit trail.
Ahmad Ali Parr · SnapKitty Collective · 2026
What It Does
TOM implements a two-loop recursive optimization architecture:
- Outer Loop (Architect): analyzes telemetry, rewrites prompts and hyperparameters
- Inner Loop (Worker): executes under gate constraints, reports results
- Assert Gate: validates outputs before promotion to the outer loop
- WORM Chain: every generation is sealed to an append-only audit trail
The outer loop rewrites the inner loop. The inner loop cannot modify the outer loop. The WORM chain ensures no rewrite is ever lost or fabricated.
Gate Taxonomy
| Gate | Function |
|---|---|
| Assert gate | JSON schema validation of output |
| Temperature gate | 0.0–2.0 adjustment based on failure class |
| Logit bias gate | Per-token suppression/boost |
| Lesson register | Compressed state, max 50 entries |
Connection to Gates Normalization
The logit bias gate implements the Gates Normalization insight directly:
G_P(D_M) = softmax(logits_M + b_P)
b_P = -∞ for grammar violations (zero probability)
b_P = dynamic bias from telemetry otherwise
The gate does not filter outputs. It gates the probability distribution before sampling — the constraint is structural, not post-hoc.
Theoretical foundation: Gates Normalization Constraint
Unified Theory
Part of the Sovereign Stack: 10.5281/zenodo.21816366
Repository
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