The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: ArrowTypeError
Message: ("Expected bytes, got a 'dict' object", 'Conversion failed for column 0 with type object')
Traceback: Traceback (most recent call last):
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
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Missing a name for object member. in row 0
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 331, in _generate_tables
pa_table = pa.Table.from_pandas(df, preserve_index=False)
File "pyarrow/table.pxi", line 4768, in pyarrow.lib.Table.from_pandas
File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 651, in dataframe_to_arrays
arrays = [convert_column(c, f)
~~~~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 639, in convert_column
raise e
File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 633, in convert_column
result = pa.array(col, type=type_, from_pandas=True, safe=safe)
File "pyarrow/array.pxi", line 390, in pyarrow.lib.array
File "pyarrow/array.pxi", line 91, in pyarrow.lib._ndarray_to_array
check_status(NdarrayToArrow(pool, values, mask, from_pandas,
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'dict' object", 'Conversion failed for column 0 with type object')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.
This repository contains code and data required to reproduce the main results for the paper titled—"KisMATH: Do LLMs Have Knowledge of Implicit Structures in Mathematical Reasoning?", accepted to the Transactions of the Association for Computational Linguistics (TACL), 14:1308–1328.
Citation
If you find any material from this repository helpful, please cite our paper.
@article{saha-etal-2026-kismath,
title = {{K}is{MATH}: Do {LLM}s Have Knowledge of Implicit Structures in Mathematical Reasoning?},
author = {Saha, Soumadeep and Chaturvedi, Akshay and Saha, Saptarshi and Garain, Utpal and Asher, Nicholas},
journal = {Transactions of the Association for Computational Linguistics},
volume = {14},
year = {2026},
address = {Cambridge, MA},
publisher = {MIT Press},
url = {https://aclanthology.org/2026.tacl-1.59/},
doi = {10.1162/tacl.a.729},
pages = {1308--1328}
}
Abstract
Chain-of-thought (CoT) traces have been shown to improve performance of large language models on a plethora of reasoning tasks, yet there is no consensus on the mechanism by which this boost is achieved. To shed more light on this, we introduce Causal CoT Graphs (CCGraphs), which are directed acyclic graphs automatically extracted from reasoning traces that model finegrained causal dependencies in language-model outputs.
A collection of 1671 mathematical reasoning problems from MATH500, GSM8K, and AIME, together with their associated CCGraphs, has been compiled into our dataset—KisMATH. Our detailed empirical analysis with 15 open-weight LLMs shows that (i) reasoning nodes in the CCGraphs_ are causal contributors to the final answer, which we argue is constitutive of reasoning; and (ii) LLMs emphasize the reasoning paths captured by the CCGraphs, indicating that the models internally realize structures similar to our graphs. KisMATH enables controlled, graph-aligned interventions and opens avenues for further investigation into the role of CoT in LLM reasoning."
Usage
The repository is structured as follows:
.
├── AIME
│ ├── graph.json
│ └── path.json
├── GSM8K
│ ├── graph.json
│ └── path.json
├── MATH500
│ ├── graph.json
│ └── path.json
└── README.md (this file)
The graph.json file corresponds to the CCGraphs serialized and saved
using the networkx library. The path.json file corresponds to
R paths, and for each path an ordered list of prefixes and the target span
is provided.
Authors / Contributors
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