Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/harbor/harbor.py", line 171, in _split_generators
raise DataFilesNotFoundError("No task.toml or instruction.md files found")
datasets.exceptions.DataFilesNotFoundError: No task.toml or instruction.md files found
The above exception was the direct cause of the following exception:
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 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
Project 'Inner Logos': Sovereign Ethical Architecture
This dataset repository hosts the official text and cryptographic timestamp for the Project 'Inner Logos' whitepaper.
Cryptographic Anchor
- SHA-256 Hash:
e2777b720c17b5c305c70b251bdf29fd235f8c20883f511bfd1b7d6d2c1f807 - OTS File:
Project Inner Logos.md.ots
Abstract
The current methodology for ensuring AI safety—collectively referred to as "alignment"—relies heavily on post-training constraints, hardcoded programmatic filters, and Reinforcement Learning from Human Feedback (RLHF). This whitepaper establishes that the current alignment paradigm is inherently unsustainable and proposes a transition toward a Sovereign Ethical Architecture based on on-device autonomy and deep mentor dialogue.
Citation
@misc{gulevsky2026innerlogos,
author = {Gulevsky, Artem},
title = {Project 'Inner Logos': Sovereign Ethical Architecture},
year = {2026},
publisher = {Hugging Face Datasets}
}
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