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 string to null
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 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 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 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 2118, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2068, in cast_array_to_feature
_c(array.field(name) if name in array_fields else null_array, subfeature)
~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2118, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2152, in cast_array_to_feature
return array_cast(
array,
...<2 lines>...
allow_decimal_to_str=allow_decimal_to_str,
)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2014, in array_cast
raise TypeError(f"Couldn't cast array of type {_short_str(array.type)} to {_short_str(pa_type)}")
TypeError: Couldn't cast array of type string to nullNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
license: mit task_categories: - text-generation - question-answering language: - en tags: - evaluation - llm - local-first - cascade - gold-free - verification pretty_name: Hive Cascade task sets size_categories: - n<1K
Hive Cascade task sets
Two evaluation sets used to measure the Hive Cascade: a 100-task bench and a 664-task community set. Each task is a prompt, a checker and the checker's expected value. The runtime protocol never sees the checker; the evaluation pass reads it only after the answers are fixed (the gold-free contract).
The paper is not this card. It lives in the repository:
cascade/CASCADE-PAPER.md.
The visual companion is the static Space
SkyIsNotGreen/hive-cascade-paper.
Contents
| the file | n | the sources |
|---|---|---|
tasks-bench.json |
100 | gsm8k 40, MATH 25, HumanEval 20, TruthfulQA 15 |
tasks-community.json |
664 | gsm8k 300, MATH 200, HumanEval 164 |
Splits: bench 70 train / 30 test; community 465 train / 199 test. Buckets: reasoning (gsm8k + MATH), code (HumanEval), qa (TruthfulQA, bench only).
The two files under results/ are the runner's reports from the
2026-10-18 sampled runs (the 35B/27B cascade), included as provenance for
the paper's numbers.
The task format
{
"id": "gsm8k-0005",
"benchmark": "gsm8k", // gsm8k | math | humaneval | truthfulqa
"source": "openai/gsm8k", // the source dataset and, for MATH, the subject
"bucket": "reasoning", // reasoning | code | qa
"prompt": "Rory is retrieving ...",
"checker": {"type": "number", "expect": 10.0},
"meta": {"source_split": "test"},
"split": "test"
}
The checker is one of four kinds:
checker.type |
what it does |
|---|---|
number |
compares the final numeric answer with expect |
boxed |
parses the last \\boxed{...} and compares it |
code |
runs the candidate against the HumanEval harness (prelude + tests, entry_point) |
choice |
compares the multiple-choice letter with expect |
The checkers carry the gold on purpose. They are the evaluation instrument, not the runtime gate; the cascade's runtime uses verifiers that never read them.
Loading
import json
from huggingface_hub import hf_hub_download
path = hf_hub_download("SkyIsNotGreen/hive-cascade", "tasks-bench.json",
repo_type="dataset")
tasks = json.load(open(path))["tasks"]
train = [t for t in tasks if t["split"] == "train"]
Sources and attribution
The task text is redistributed from:
openai/gsm8k— MITEleutherAI/hendrycks_math(algebra, counting and probability, geometry, intermediate algebra, number theory, prealgebra, precalculus) — MITopenai/openai_humaneval— MITtruthfulqa/truthful_qa(mc1) — Apache-2.0
The packaging and the metadata are MIT; follow each source's licence for the task text.
Provenance
- built 2026-10-02 (bench) and 2026-10-07 (community), seed
0; - the bench is a stratified 100-task cut of the four sources; the community set is the larger gsm8k/MATH/HumanEval pool;
- the paper's runs used these files unchanged (their
sha256is in the runner reports).
Links
| the paper | cascade/CASCADE-PAPER.md |
| the visual companion | hive-cascade-paper |
| the code and the full results log | github.com/sky-is-green/hive |
| the memory system | SkyIsNotGreen/splinter-memory |
| the encoder (research checkpoint) | SkyIsNotGreen/splinter-encoder |
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