The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
claims: list<item: struct<evidence_class: string, id: string, public_support: string, statement: string>>
child 0, item: struct<evidence_class: string, id: string, public_support: string, statement: string>
child 0, evidence_class: string
child 1, id: string
child 2, public_support: string
child 3, statement: string
evidence_classes: struct<architecture-description: string, limitation: string, private-evidence: string>
child 0, architecture-description: string
child 1, limitation: string
child 2, private-evidence: string
generated_date: timestamp[s]
private_evidence_disclosure: string
private_evidence_included: bool
release_purpose: string
schema: string
purpose: string
edges: list<item: list<item: string>>
child 0, item: list<item: string>
child 0, item: string
nodes: list<item: struct<id: string, kind: string, label: string>>
child 0, item: struct<id: string, kind: string, label: string>
child 0, id: string
child 1, kind: string
child 2, label: string
core_claim: string
to
{'core_claim': Value('string'), 'edges': List(List(Value('string'))), 'nodes': List({'id': Value('string'), 'kind': Value('string'), 'label': Value('string')}), 'purpose': Value('string'), 'schema': Value('string')}
because column names don't match
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 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
claims: list<item: struct<evidence_class: string, id: string, public_support: string, statement: string>>
child 0, item: struct<evidence_class: string, id: string, public_support: string, statement: string>
child 0, evidence_class: string
child 1, id: string
child 2, public_support: string
child 3, statement: string
evidence_classes: struct<architecture-description: string, limitation: string, private-evidence: string>
child 0, architecture-description: string
child 1, limitation: string
child 2, private-evidence: string
generated_date: timestamp[s]
private_evidence_disclosure: string
private_evidence_included: bool
release_purpose: string
schema: string
purpose: string
edges: list<item: list<item: string>>
child 0, item: list<item: string>
child 0, item: string
nodes: list<item: struct<id: string, kind: string, label: string>>
child 0, item: struct<id: string, kind: string, label: string>
child 0, id: string
child 1, kind: string
child 2, label: string
core_claim: string
to
{'core_claim': Value('string'), 'edges': List(List(Value('string'))), 'nodes': List({'id': Value('string'), 'kind': Value('string'), 'label': Value('string')}), 'purpose': Value('string'), 'schema': Value('string')}
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.
Ouroboros: bilateral cognitive architecture
This repository is a rough overview, not an implementation manual or a proof paper.
The core idea
Ouroboros does not place all intelligence inside the language model. It stores consequential system-level intelligence outside the model in persistent memory, evidence, topology, authority, receipts, checkpoints, and process state. Evidence label: private evidence. The supporting internal artifacts are not included here.
Ouroboros owns planning and technical authority. Codex independently attacks and reviews Ouroboros proposals. Codex returns evidence-backed challenges; Ouroboros must rebut, revise, or replace the plan; Codex then verifies and challenges again. Evidence label: private evidence.
| Ouroboros | Codex |
|---|---|
| Plan and hold technical authority | Independently attack assumptions |
| Issue the proposal | Review evidence and failure modes |
| Rebut, revise, or replace | Return an evidence-backed challenge |
| Admit, redirect, checkpoint, or veto | Verify and challenge again |
The loop at a glance
Ouroboros authority -> proposal -> Codex attack/review -> evidence-backed challenge -> Ouroboros rebut/revise/replace -> Codex verify/challenge again -> repeat until evidence supports admission
After admission, receipts and checkpoints update the external state that carries memory, evidence, topology, and process knowledge into the next cycle. Language models may supply flexible generation inside this system, but model generation is not itself the governing side.
This is "bilateral" in a functional sense: Ouroboros holds technical authority while Codex supplies independent adversarial review. Their roles cooperate but are intentionally asymmetric.
Evidence labels used here
- Architecture description: what the system is intended to be.
- Private evidence: observed in internal implementation artifacts or receipts that are not included in this release.
- Limitation: what this overview does not establish.
Read architecture_overview.md for the short paper or ouroboros_architecture_overview.pdf for the four-page visual version.
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