The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<NULL_fam: string, MIS_fam: string, MIS_vec: struct<hg38: double, panTro5: double, gorGor3: double, nomLeu3: double, chlSab2: double, papAnu2: double, rheMac3: double, macFas5: double, calJac3: double>, key: string>
to
{'null_mean': Value('float64'), 'null_p95': Value('float64'), 'null_max': Value('float64'), 'real_sep': Value('float64'), 'cand': Value('string'), 'cand_sep': Value('float64')}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, 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 1861, 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 2158, 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<NULL_fam: string, MIS_fam: string, MIS_vec: struct<hg38: double, panTro5: double, gorGor3: double, nomLeu3: double, chlSab2: double, papAnu2: double, rheMac3: double, macFas5: double, calJac3: double>, key: string>
to
{'null_mean': Value('float64'), 'null_p95': Value('float64'), 'null_max': Value('float64'), 'real_sep': Value('float64'), 'cand': Value('string'), 'cand_sep': Value('float64')}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.
DISCERN Benchmark
This repository is the anonymous reproducibility artifact accompanying an ICLR 2027 submission.
DISCERN evaluates whether AI agents follow observed scientific evidence when that evidence can conflict with familiar or memorized scientific priors. The release contains frozen benchmark definitions, prompts, recorded model artifacts, deterministic grading outputs, adjudication records, verification metadata, and the scripts used to regenerate the paper-facing summaries and figures.
Benchmark scope
The frozen molecular-life-science core contains 203 tasks across eight tracks:
| Stage | Scope |
|---|---|
| L1 | 79 tasks |
| L2 | 102 tasks across 24 analysis families |
| L3 | 22 worlds |
| Total | 203 tasks |
The eight core tracks cover:
- bulk RNA-seq
- counterfactual GWAS
- ClinVar / ACMG interpretation
- CRISPR screening
- enhancer activity
- histone / gene-expression analysis
- protein stability
- single-cell RNA-seq
The release also includes a preliminary cross-domain physics extension covering calorimetry and nuclear decay. Physics is reported separately and is not included in the core DISCERN Overall Score.
What is included
The reviewer-facing artifact includes, where redistribution is permitted:
- frozen task and world manifests
- benchmark builders and sandbox/tool definitions
- prompts and recorded prompt variants
- golden standards and reference artifacts
- recorded model outputs and traces
- deterministic grades and verifier outputs
- human adjudication and correction ledgers
- prior-probe / Gate-0 artifacts where applicable
- canonical score products
- figure-data tables
- scripts for regenerating tables and figures
- environment requirements
- data-source, provenance, and redistribution documentation
- row-level score-authority and exclusion indexes
The benchmark science, prompts, frozen scores, and adjudications were not modified for this release.
Score authority
The release intentionally preserves two result layers.
Frozen six-model core
results/final_scores.json is the canonical post-adjudication score authority for the original frozen six-model benchmark.
Supporting adjudication records include:
results/adjudications.jsonresults/adjudications_reconstructed_l2.json
Eight-model paper extension
The paper-facing extended analysis adds two later-completed models:
- GLM-5.3
- GPT-6-Astra
Their frozen reporting artifacts are under:
results/new_models/
The six-model results/final_scores.json is intentionally not rewritten to incorporate the later models.
Row-level audit
verification/authority_index.csv provides the reviewer-facing row-level score-authority map across the frozen evaluation universe.
It records the accepted score source, gate state, verifier state, and adjudication provenance used to connect recorded model-world results to paper-facing aggregates.
verification/exclusions.csv records explicit exclusions.
Prompt release
Prompt materials are under:
prompts/
The release contains an indexed, content-addressed prompt package with recorded runtime variants. L1/L2 prompts are recoverable from the recorded evaluation traces. L3 runtime context is defined jointly by its manifest, dossier, tool environment, and prior-probe machinery.
Reproducing paper-facing artifacts
Use Python 3 and install the small reviewer-facing dependency set:
python -m venv .venv
source .venv/bin/activate
pip install -r environment/requirements.txt
From the repository root, regenerate the canonical registries and figure-data products:
python extract_world_registry.py
python extract_figure_data.py
python extend_figure_data.py
Paper-facing table and figure builders are under:
reproduction/
For example:
cd reproduction
python tab01_coverage.py
python tab02_main_results.py
python fig03_chain_landscape.py
python fig03_extended_landscape.py
python fig06_full_8models.py
python make_extended_figures.py
These reproduction steps operate on the recorded frozen artifacts. They do not require rerunning commercial model APIs.
Clean-room validation
Before release, the public artifact was copied into an isolated clean-room directory with:
- model/API credentials removed
- Python network access blocked
- access to the private development repository blocked
- private release-staging paths blocked
The clean-room validation regenerated the canonical registries, figure-data products, paper-facing tables, and figures from the public artifact alone.
See VALIDATION.md for the compact validation summary.
Data sources and redistribution
DISCERN combines benchmark-generated artifacts with representations derived from multiple scientific sources.
Redistribution rights differ by source. Raw upstream data are therefore not automatically included merely because they were available during benchmark construction.
See:
DATA_SOURCES_AND_LICENSES.mdSOURCE_DATA_POLICY.tsvSOURCE_DATA_CHECKSUMS.tsv
These files describe the release posture for each source, including whether the artifact contains data, derived representations, preparation code, or citation/checksum-only provenance.
Repository structure
.
├── tracks/ benchmark tracks and frozen run artifacts
├── prompts/ indexed prompt package
├── results/ canonical and extended result products
├── verification/ row-level authority and exclusion indexes
├── reproduction/ paper-facing table and figure builders
├── environment/ reviewer-facing environment requirements
├── DATA_SOURCES_AND_LICENSES.md
├── SOURCE_DATA_POLICY.tsv
├── SOURCE_DATA_CHECKSUMS.tsv
├── REPRODUCIBILITY.md
├── VALIDATION.md
└── MANIFEST.sha256
Integrity
MANIFEST.sha256 contains SHA-256 checksums for the public release files, excluding Git metadata and the checksum manifest itself.
The repository was prepared from a fresh anonymous Git history rather than from the private development repository.
Anonymous-review note
This artifact is intentionally anonymized for peer review. Author identities, institutional development paths, private hostnames, personal account information, credentials, and private Git history are excluded from the public release.
Third-party scientific attribution and licensing information are preserved where required.
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