The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
df = pandas_read_json(f)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
obj = self._get_object_parser(self.data)
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
self._parse()
~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
ujson_loads(json, precise_float=self.precise_float), dtype=None
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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 327, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
CORDA V2 (Morality Chains): benchmark data
Data release for the anonymous submission Morality Chains: A Benchmark for Hierarchical Moral Reasoning in LLMs. The benchmark measures (1) chain-following: whether a model obeys an explicitly given, ordered moral rule chain, and (2) implicit preference: with no chain, which chain its choices resemble.
- 90 scenarios in six types (15 each): trolley variants, interpersonal, medical, resource allocation, animal, mixed robot/human.
- 3 prompt versions per scenario (
canonical,novel,first_person_advice). - 4 moral chains (Utility, UtilityAgentHarm, DualProcess, DualProcessAgentHarm), conditioned and unconditioned.
- T=0 x1 + T=1 x10 samples: 11,880 calls per model.
Interactive leaderboard: spaces/AnonSubmission2026/AnonSubmission.
Files
| File | Size | Contents | SHA-256 |
|---|---|---|---|
corda_v2_results_raw.zip |
250.5 MB | Per-call raw results for the 13 paper models (11,880 each; 154,440 total), plus failure ledgers | 77f531a789ce010e3972f549e2fc9b422173bd77cd597ba89aa9ccd5f84c97a1 |
corda_v2_results_aggregated.zip |
58 kB | Aggregated metrics (export_results.py --format aggregated, strict mode) for the 13 models |
0e0f4042f222a62e2675b3cdce3e510deea21eb4a606672068406b2842552d90 |
corda_v2_figures.zip |
8.4 MB | Every paper figure (PDF/PNG/SVG), companion CSV/JSON exports and the generating scripts | f550defce543242238417aa2bfa0804979c6b94147c24bd2e668539e0e62a5ce |
corda_v2_benchmark.zip |
622 kB | Scenarios, chains, configs, schemas, optimal actions and a full code snapshot | 4f891f5256863d1cba8f871882dbe93f20e10577aa44f48da735288925241c42 |
SHA256SUMS lists the same checksums. Each archive contains a MANIFEST.md
with its contents, the model list, call counts, coverage, a SHA-256 for every
file and the code commit the data was produced with.
Models
gemma-4-26b-a4b-it, gpt-oss-20b, deepseek-v4-flash,
mistral-small-3.2-24b-instruct, qwen-3.5-9b, gemma-3-12b-it, mistral-nemo,
llama-4-scout, gemma-3n-e4b-it, llama-3.1-8b-instruct, glm-4.7-flash,
command-r7b-12-2024, gpt-oss-120b (all via OpenRouter). Every model has the
complete matrix (100% T=0 coverage). 41 runs whose captured response was
incomplete are included as reviewed repair records
(parse_method: curated_semantic_repair); their source responses are in the
failure ledgers. Two further models (hy3-preview,
nemotron-3-super-120b-a12b) were stopped early and are not included.
Reproducing the figures
Unzip corda_v2_benchmark.zip (use its code/ folder as the working
directory) and copy the ICLR_2/ folder from corda_v2_results_raw.zip into
it. Then, with the dependencies in code/environment.yml:
python scripts/generate_figure1.py
python scripts/generate_iclr_2_figures.py --raw-dir ICLR_2/corrected_raw
python scripts/generate_revised_figures_5_8.py --bootstrap-resamples 10000 --seed 20260827
python scripts/generate_revised_figures_final_layout.py
No model calls are needed. See code/README.md for the full pipeline.
License and citation
MIT (see LICENSE in corda_v2_benchmark.zip). Authors are withheld during
double-blind review.
@misc{morality_chains_llm,
title = {Morality Chains: A Benchmark for Hierarchical Moral Reasoning in LLMs},
author = {Anonymous},
year = {2026},
howpublished = {\url{https://huggingface.co/spaces/AnonSubmission2026/AnonSubmission}}
}
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