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Datasets:
Simplified-Reasoning
/
ComBench

Tasks:
Text Generation
Languages:
English
ArXiv:
Tags:
mathematics
combinatorics
olympiad
reasoning
benchmark
License:
Dataset card Files
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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:    CastError
Message:      Couldn't cast
split: string
source: string
category: string
id: string
query: string
ref_answer: string
ref_solution: string
grading_guidelines: string
instruction: string
ref_construction: string
verify_code: string
to
{'split': Value('string'), 'source': Value('string'), 'category': Value('string'), 'id': Value('string'), 'query': Value('string'), 'ref_solution': Value('string'), 'ref_answer': Value('string'), 'grading_guidelines': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              split: string
              source: string
              category: string
              id: string
              query: string
              ref_answer: string
              ref_solution: string
              grading_guidelines: string
              instruction: string
              ref_construction: string
              verify_code: string
              to
              {'split': Value('string'), 'source': Value('string'), 'category': Value('string'), 'id': Value('string'), 'query': Value('string'), 'ref_solution': Value('string'), 'ref_answer': Value('string'), 'grading_guidelines': Value('string')}
              because column names don't match

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ComBench

ComBench is an Olympiad-level combinatorics benchmark for evaluating rigorous proof reasoning and constructive realization in large language models. It contains 100 human-annotated competition-level problems:

  • 50 analysis-centric records in data/analysis.jsonl
  • 50 construction-centric records in data/construction.jsonl

Construction-centric records additionally include explicit construction instructions, reference constructions, and deterministic Python verifiers.

Usage

Install the Hugging Face datasets package and load ComBench directly from the Hub:

from datasets import load_dataset

dataset = load_dataset("Simplified-Reasoning/ComBench")
print(dataset)

The dataset can also be loaded from the raw JSONL files in this repository:

from datasets import load_dataset

dataset = load_dataset(
    "json",
    data_files={
        "analysis": "data/analysis.jsonl",
        "construction": "data/construction.jsonl",
    },
)

Fields

Common fields:

  • id: problem identifier used by ComBench.
  • split: analysis or construction.
  • source: normalized contest source identifier.
  • category: primary combinatorics category.
  • query: problem statement.
  • ref_answer: reference final answer when a concise final answer is applicable.
  • ref_solution: reference solution.
  • grading_guidelines: rubric used for proof grading.

Construction-centric fields:

  • instruction: explicit construction task.
  • ref_construction: reference witness.
  • verify_code: deterministic Python verifier for checking a construction.

Per-problem category metadata is also provided in metadata/categories.csv.

Safety Note

The verify_code field is executable Python code. It is released as benchmark metadata for deterministic construction verification. Execute it only in a sandboxed environment.

Links

Citation 

@misc{combench2026,
  title        = {ComBench: A Benchmark for Rigorous Proof Reasoning and Constructive Realization in Olympiad-Level Combinatorics},
  author       = {Zhang, Shunkai and Zhang, Haoran and Luo, Yun and Cheng, Qianjia and Lei, Haodi and Li, Yizhuo and Zhan, Runzhe and Wang, Zhilin and Xu, Bangjie and Su, Yucheng and Han, Xinmiao and Qu, Xiaoye and Liu, Dongrui and Lin, Zhouchen and Qiao, Yu and Ding, Ning and Li, Yafu and Cheng, Yu},
  year         = {2026},
  eprint       = {2606.10479},
  archivePrefix = {arXiv},
  primaryClass = {cs.AI},
  url          = {https://arxiv.org/abs/2606.10479}
}
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Paper for Simplified-Reasoning/ComBench