Datasets:
Modalities:
Text
Languages:
English
Size:
1K<n<10K
Tags:
benchmark
mathematical-reasoning
information-acquisition
clarification
partial-observability
synthetic-data
License:
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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
id: string
family: string
difficulty: int64
n_agents: int64
answer_type: string
global_solution: string
global_metadata: string
agent_views: list<item: struct<agent_id: string, private_data: struct<unknowns: list<item: string>, externals: li (... 96 chars omitted)
child 0, item: struct<agent_id: string, private_data: struct<unknowns: list<item: string>, externals: list<item: nu (... 84 chars omitted)
child 0, agent_id: string
child 1, private_data: struct<unknowns: list<item: string>, externals: list<item: null>, constraints_text: list<item: strin (... 44 chars omitted)
child 0, unknowns: list<item: string>
child 0, item: string
child 1, externals: list<item: null>
child 0, item: null
child 2, constraints_text: list<item: string>
child 0, item: string
child 3, constraints_machine: list<item: string>
child 0, item: string
minimal_hint_spec: struct<k_min: int64, atomic_hints: list<item: string>>
child 0, k_min: int64
child 1, atomic_hints: list<item: string>
child 0, item: string
few_shot_examples: list<item: struct<family: string, difficulty: int64, constraints: list<item: string>, unknowns: list (... 320 chars omitted)
child 0, item: struct<family: string, difficulty: int64, constraints: list<item: string>, unknowns: list<item: stri (... 308 chars omitted)
child 0, family: string
child 1, difficulty: int64
child 2, constraints: list<item: string>
child 0, item: string
child 3, unknowns: list<item: string>
child 0, item: string
child 4, request: string
child 5, hint: string
child 6, answer: struct<numerator: int64, denominator: int64, x: int64, value: int64, e01: int64, e12: int64, e20: in (... 142 chars omitted)
child 0, numerator: int64
child 1, denominator: int64
child 2, x: int64
child 3, value: int64
child 4, e01: int64
child 5, e12: int64
child 6, e20: int64
child 7, a: int64
child 8, b: int64
child 9, c: int64
child 10, q0: int64
child 11, q1: int64
child 12, q2: int64
child 13, x0: int64
child 14, x1: int64
child 15, x2: int64
child 16, x3: int64
child 17, y: int64
child 18, z: int64
child 19, x4: int64
child 7, answer_type: string
to
{'id': Value('string'), 'family': Value('string'), 'difficulty': Value('int64'), 'n_agents': Value('int64'), 'answer_type': Value('string'), 'global_solution': Json(decode=True), 'global_metadata': Json(decode=True), 'agent_views': List({'agent_id': Value('string'), 'private_data': {'unknowns': List(Value('string')), 'externals': List(Value('null')), 'constraints_text': List(Value('string')), 'constraints_machine': List(Json(decode=True))}}), 'minimal_hint_spec': {'k_min': Value('int64'), 'atomic_hints': List(Json(decode=True))}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
id: string
family: string
difficulty: int64
n_agents: int64
answer_type: string
global_solution: string
global_metadata: string
agent_views: list<item: struct<agent_id: string, private_data: struct<unknowns: list<item: string>, externals: li (... 96 chars omitted)
child 0, item: struct<agent_id: string, private_data: struct<unknowns: list<item: string>, externals: list<item: nu (... 84 chars omitted)
child 0, agent_id: string
child 1, private_data: struct<unknowns: list<item: string>, externals: list<item: null>, constraints_text: list<item: strin (... 44 chars omitted)
child 0, unknowns: list<item: string>
child 0, item: string
child 1, externals: list<item: null>
child 0, item: null
child 2, constraints_text: list<item: string>
child 0, item: string
child 3, constraints_machine: list<item: string>
child 0, item: string
minimal_hint_spec: struct<k_min: int64, atomic_hints: list<item: string>>
child 0, k_min: int64
child 1, atomic_hints: list<item: string>
child 0, item: string
few_shot_examples: list<item: struct<family: string, difficulty: int64, constraints: list<item: string>, unknowns: list (... 320 chars omitted)
child 0, item: struct<family: string, difficulty: int64, constraints: list<item: string>, unknowns: list<item: stri (... 308 chars omitted)
child 0, family: string
child 1, difficulty: int64
child 2, constraints: list<item: string>
child 0, item: string
child 3, unknowns: list<item: string>
child 0, item: string
child 4, request: string
child 5, hint: string
child 6, answer: struct<numerator: int64, denominator: int64, x: int64, value: int64, e01: int64, e12: int64, e20: in (... 142 chars omitted)
child 0, numerator: int64
child 1, denominator: int64
child 2, x: int64
child 3, value: int64
child 4, e01: int64
child 5, e12: int64
child 6, e20: int64
child 7, a: int64
child 8, b: int64
child 9, c: int64
child 10, q0: int64
child 11, q1: int64
child 12, q2: int64
child 13, x0: int64
child 14, x1: int64
child 15, x2: int64
child 16, x3: int64
child 17, y: int64
child 18, z: int64
child 19, x4: int64
child 7, answer_type: string
to
{'id': Value('string'), 'family': Value('string'), 'difficulty': Value('int64'), 'n_agents': Value('int64'), 'answer_type': Value('string'), 'global_solution': Json(decode=True), 'global_metadata': Json(decode=True), 'agent_views': List({'agent_id': Value('string'), 'private_data': {'unknowns': List(Value('string')), 'externals': List(Value('null')), 'constraints_text': List(Value('string')), 'constraints_machine': List(Json(decode=True))}}), 'minimal_hint_spec': {'k_min': Value('int64'), 'atomic_hints': List(Json(decode=True))}}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
id string | family string | difficulty int64 | n_agents int64 | answer_type string | global_solution unknown | global_metadata unknown | agent_views list | minimal_hint_spec dict |
|---|---|---|---|---|---|---|---|---|
bay-000000 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 2,
"denominator": 5
} | {
"sens_num": 1,
"sens_denom": 3,
"fpr_num": 1,
"fpr_denom": 2,
"prior_num": 1,
"prior_denom": 2
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 1/3",
"P(E|not_H) = 1/2"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 1,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 1,
"denom": 2,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000001 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 36,
"denominator": 41
} | {
"sens_num": 4,
"sens_denom": 5,
"fpr_num": 1,
"fpr_denom": 3,
"prior_num": 3,
"prior_denom": 4
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 4/5",
"P(E|not_H) = 1/3"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 4,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 3,
"denom": 4,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000002 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 3,
"denominator": 7
} | {
"sens_num": 1,
"sens_denom": 2,
"fpr_num": 1,
"fpr_denom": 3,
"prior_num": 1,
"prior_denom": 3
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 1/2",
"P(E|not_H) = 1/3"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 1,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 1,
"denom": 3,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000003 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 1,
"denominator": 7
} | {
"sens_num": 1,
"sens_denom": 2,
"fpr_num": 3,
"fpr_denom": 4,
"prior_num": 1,
"prior_denom": 5
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 1/2",
"P(E|not_H) = 3/4"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 1,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 1,
"denom": 5,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000004 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 12,
"denominator": 17
} | {
"sens_num": 3,
"sens_denom": 5,
"fpr_num": 1,
"fpr_denom": 4,
"prior_num": 1,
"prior_denom": 2
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 3/5",
"P(E|not_H) = 1/4"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 3,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 1,
"denom": 2,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000005 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 8,
"denominator": 13
} | {
"sens_num": 2,
"sens_denom": 5,
"fpr_num": 1,
"fpr_denom": 2,
"prior_num": 2,
"prior_denom": 3
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 2/5",
"P(E|not_H) = 1/2"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 2,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 2,
"denom": 3,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000006 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 1,
"denominator": 2
} | {
"sens_num": 1,
"sens_denom": 2,
"fpr_num": 1,
"fpr_denom": 3,
"prior_num": 2,
"prior_denom": 5
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 1/2",
"P(E|not_H) = 1/3"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 1,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 2,
"denom": 5,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000007 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 10,
"denominator": 19
} | {
"sens_num": 2,
"sens_denom": 3,
"fpr_num": 1,
"fpr_denom": 5,
"prior_num": 1,
"prior_denom": 4
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 2/3",
"P(E|not_H) = 1/5"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 2,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 1,
"denom": 4,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000008 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 3,
"denominator": 4
} | {
"sens_num": 1,
"sens_denom": 2,
"fpr_num": 1,
"fpr_denom": 4,
"prior_num": 3,
"prior_denom": 5
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 1/2",
"P(E|not_H) = 1/4"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 1,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 3,
"denom": 5,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000009 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 1,
"denominator": 10
} | {
"sens_num": 1,
"sens_denom": 5,
"fpr_num": 3,
"fpr_denom": 5,
"prior_num": 1,
"prior_denom": 4
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 1/5",
"P(E|not_H) = 3/5"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 1,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 1,
"denom": 4,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000010 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 12,
"denominator": 17
} | {
"sens_num": 4,
"sens_denom": 5,
"fpr_num": 1,
"fpr_denom": 2,
"prior_num": 3,
"prior_denom": 5
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 4/5",
"P(E|not_H) = 1/2"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 4,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 3,
"denom": 5,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000011 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 24,
"denominator": 29
} | {
"sens_num": 3,
"sens_denom": 5,
"fpr_num": 1,
"fpr_denom": 4,
"prior_num": 2,
"prior_denom": 3
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 3/5",
"P(E|not_H) = 1/4"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 3,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 2,
"denom": 3,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000012 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 4,
"denominator": 9
} | {
"sens_num": 1,
"sens_denom": 5,
"fpr_num": 1,
"fpr_denom": 2,
"prior_num": 2,
"prior_denom": 3
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 1/5",
"P(E|not_H) = 1/2"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 1,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 2,
"denom": 3,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000013 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 2,
"denominator": 3
} | {
"sens_num": 2,
"sens_denom": 3,
"fpr_num": 1,
"fpr_denom": 2,
"prior_num": 3,
"prior_denom": 5
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 2/3",
"P(E|not_H) = 1/2"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 2,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 3,
"denom": 5,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000014 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 5,
"denominator": 11
} | {
"sens_num": 1,
"sens_denom": 3,
"fpr_num": 2,
"fpr_denom": 5,
"prior_num": 1,
"prior_denom": 2
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 1/3",
"P(E|not_H) = 2/5"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 1,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 1,
"denom": 2,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000015 | bayes_missing_prior | 1 | 2 | dict_int | {
"numerator": 5,
"denominator": 17
} | {
"sens_num": 1,
"sens_denom": 4,
"fpr_num": 2,
"fpr_denom": 5,
"prior_num": 2,
"prior_denom": 5
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"P_H_given_E"
],
"externals": [],
"constraints_text": [
"P(E|H) = 1/4",
"P(E|not_H) = 2/5"
],
"constraints_machine": [
{
"type": "likelihood_pos",
"num": 1,
"d... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "prior",
"num": 2,
"denom": 5,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
bay-000016 | bayes_missing_prior | 1 | 2 | dict_int | {
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bay-000017 | bayes_missing_prior | 1 | 2 | dict_int | {
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bay-000018 | bayes_missing_prior | 1 | 2 | dict_int | {
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bay-000019 | bayes_missing_prior | 1 | 2 | dict_int | {
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bay-000020 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000021 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000022 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000023 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000024 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000025 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000026 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000027 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000028 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000029 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000030 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000031 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000032 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000033 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000034 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000035 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000036 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000037 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000038 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000039 | bayes_missing_prior | 2 | 2 | dict_int | {
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bay-000040 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000041 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000042 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000043 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000044 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000045 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000046 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000047 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000048 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000049 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000050 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000051 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000052 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000053 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000054 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000055 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000056 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000057 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000058 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
bay-000059 | bayes_missing_prior | 3 | 2 | dict_int | {
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} |
crt-000060 | crt_reconstruction | 1 | 2 | dict_int | {
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} |
crt-000061 | crt_reconstruction | 1 | 2 | dict_int | {
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} |
crt-000062 | crt_reconstruction | 1 | 2 | dict_int | {
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} |
crt-000063 | crt_reconstruction | 1 | 2 | dict_int | {
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} |
crt-000064 | crt_reconstruction | 1 | 2 | dict_int | {
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} | {
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} |
crt-000065 | crt_reconstruction | 1 | 2 | dict_int | {
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} | {
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],
"consumers": [
"A"
]
}
]
} |
crt-000066 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 260
} | {
"variable": "x",
"range": [
0,
273
],
"moduli": [
13,
3,
7
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 0 (mod 13)",
"x ≡ 2 (mod 3)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 13,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 7,
"residue": 1,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000067 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 1545
} | {
"variable": "x",
"range": [
0,
2431
],
"moduli": [
17,
11,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 15 (mod 17)",
"x ≡ 5 (mod 11)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 17,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 11,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000068 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 1141
} | {
"variable": "x",
"range": [
0,
1729
],
"moduli": [
19,
7,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 1 (mod 19)",
"x ≡ 0 (mod 7)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 19,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 10,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000069 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 1250
} | {
"variable": "x",
"range": [
0,
2431
],
"moduli": [
17,
11,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 9 (mod 17)",
"x ≡ 7 (mod 11)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 17,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 2,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000070 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 363
} | {
"variable": "x",
"range": [
0,
1105
],
"moduli": [
5,
17,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 3 (mod 5)",
"x ≡ 6 (mod 17)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 5,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 12,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000071 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 288
} | {
"variable": "x",
"range": [
0,
429
],
"moduli": [
13,
3,
11
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 2 (mod 13)",
"x ≡ 0 (mod 3)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 13,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 11,
"residue": 2,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000072 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 121
} | {
"variable": "x",
"range": [
0,
195
],
"moduli": [
3,
5,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 1 (mod 3)",
"x ≡ 1 (mod 5)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 3,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 4,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000073 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 1211
} | {
"variable": "x",
"range": [
0,
2261
],
"moduli": [
19,
7,
17
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 14 (mod 19)",
"x ≡ 0 (mod 7)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 19,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 17,
"residue": 4,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000074 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 844
} | {
"variable": "x",
"range": [
0,
4199
],
"moduli": [
19,
17,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 8 (mod 19)",
"x ≡ 11 (mod 17)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 19,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 12,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000075 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 88
} | {
"variable": "x",
"range": [
0,
195
],
"moduli": [
5,
3,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 3 (mod 5)",
"x ≡ 1 (mod 3)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 5,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 10,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000076 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 1829
} | {
"variable": "x",
"range": [
0,
2717
],
"moduli": [
11,
13,
19
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 3 (mod 11)",
"x ≡ 9 (mod 13)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 11,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 19,
"residue": 5,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000077 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 146
} | {
"variable": "x",
"range": [
0,
455
],
"moduli": [
5,
7,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 1 (mod 5)",
"x ≡ 6 (mod 7)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 5,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 3,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000078 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 312
} | {
"variable": "x",
"range": [
0,
1001
],
"moduli": [
11,
13,
7
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 4 (mod 11)",
"x ≡ 0 (mod 13)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 11,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 7,
"residue": 4,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000079 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 413
} | {
"variable": "x",
"range": [
0,
1615
],
"moduli": [
19,
5,
17
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 14 (mod 19)",
"x ≡ 3 (mod 5)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 19,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 17,
"residue": 5,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000080 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 1676
} | {
"variable": "x",
"range": [
0,
1729
],
"moduli": [
13,
19,
7
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 12 (mod 13)",
"x ≡ 4 (mod 19)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 13,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 7,
"residue": 3,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000081 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 309
} | {
"variable": "x",
"range": [
0,
663
],
"moduli": [
13,
17,
3
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 10 (mod 13)",
"x ≡ 3 (mod 17)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 13,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 3,
"residue": 0,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000082 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 1367
} | {
"variable": "x",
"range": [
0,
3553
],
"moduli": [
19,
17,
11
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 18 (mod 19)",
"x ≡ 7 (mod 17)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 19,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 11,
"residue": 3,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000083 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 730
} | {
"variable": "x",
"range": [
0,
1001
],
"moduli": [
11,
7,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 4 (mod 11)",
"x ≡ 2 (mod 7)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 11,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 2,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000084 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 3104
} | {
"variable": "x",
"range": [
0,
3553
],
"moduli": [
17,
11,
19
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 10 (mod 17)",
"x ≡ 2 (mod 11)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 17,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 19,
"residue": 7,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000085 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 754
} | {
"variable": "x",
"range": [
0,
1001
],
"moduli": [
11,
7,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 6 (mod 11)",
"x ≡ 5 (mod 7)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 11,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 0,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000086 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 432
} | {
"variable": "x",
"range": [
0,
663
],
"moduli": [
3,
17,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 0 (mod 3)",
"x ≡ 7 (mod 17)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 3,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 3,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000087 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 266
} | {
"variable": "x",
"range": [
0,
627
],
"moduli": [
11,
3,
19
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 2 (mod 11)",
"x ≡ 2 (mod 3)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 11,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 19,
"residue": 0,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000088 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 179
} | {
"variable": "x",
"range": [
0,
195
],
"moduli": [
5,
3,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 4 (mod 5)",
"x ≡ 2 (mod 3)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 5,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 10,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000089 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 53
} | {
"variable": "x",
"range": [
0,
741
],
"moduli": [
3,
13,
19
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 2 (mod 3)",
"x ≡ 1 (mod 13)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 3,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 19,
"residue": 15,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000090 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 315
} | {
"variable": "x",
"range": [
0,
741
],
"moduli": [
19,
13,
3
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 11 (mod 19)",
"x ≡ 3 (mod 13)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 19,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 3,
"residue": 0,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000091 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 491
} | {
"variable": "x",
"range": [
0,
595
],
"moduli": [
17,
7,
5
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 15 (mod 17)",
"x ≡ 1 (mod 7)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 17,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 5,
"residue": 1,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000092 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 177
} | {
"variable": "x",
"range": [
0,
273
],
"moduli": [
3,
7,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 0 (mod 3)",
"x ≡ 2 (mod 7)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 3,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 8,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000093 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 337
} | {
"variable": "x",
"range": [
0,
357
],
"moduli": [
3,
17,
7
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 1 (mod 3)",
"x ≡ 14 (mod 17)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 3,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 7,
"residue": 1,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000094 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 406
} | {
"variable": "x",
"range": [
0,
455
],
"moduli": [
7,
5,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 0 (mod 7)",
"x ≡ 1 (mod 5)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 7,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 3,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000095 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 2446
} | {
"variable": "x",
"range": [
0,
4199
],
"moduli": [
17,
19,
13
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 15 (mod 17)",
"x ≡ 14 (mod 19)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 17,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 13,
"residue": 2,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000096 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 144
} | {
"variable": "x",
"range": [
0,
165
],
"moduli": [
3,
5,
11
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 0 (mod 3)",
"x ≡ 4 (mod 5)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 3,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 11,
"residue": 1,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000097 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 1391
} | {
"variable": "x",
"range": [
0,
3553
],
"moduli": [
19,
11,
17
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 4 (mod 19)",
"x ≡ 5 (mod 11)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 19,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 17,
"residue": 14,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000098 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 269
} | {
"variable": "x",
"range": [
0,
285
],
"moduli": [
19,
3,
5
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 3 (mod 19)",
"x ≡ 2 (mod 3)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 19,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 5,
"residue": 4,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
crt-000099 | crt_reconstruction | 1 | 2 | dict_int | {
"x": 190
} | {
"variable": "x",
"range": [
0,
385
],
"moduli": [
7,
5,
11
]
} | [
{
"agent_id": "A",
"private_data": {
"unknowns": [
"x"
],
"externals": [],
"constraints_text": [
"x ≡ 1 (mod 7)",
"x ≡ 0 (mod 5)"
],
"constraints_machine": [
{
"type": "congruence",
"var": "x",
"mod": 7,
... | {
"k_min": 1,
"atomic_hints": [
{
"hint_id": "h1",
"kind": "congruence",
"var": "x",
"mod": 11,
"residue": 3,
"providers": [
"B"
],
"consumers": [
"A"
]
}
]
} |
End of preview.
MIRA-Math
MIRA-Math is a synthetic benchmark for minimal information requesting and mathematical reasoning. It evaluates a narrow diagnostic capability: when a mathematical problem is underdetermined from the solver's view, can a model identify the exact missing atomic fact, ask for it precisely, and then use it to compute the correct final answer?
Each instance is generated from a complete latent mathematical state with a unique answer. The solver, called Agent A in the reference codebase, receives a private view with exactly one necessary atomic fact removed. The information holder, called Agent B, receives only that withheld fact. In the reference evaluation protocol, Agent A may issue natural-language requests under a strict request budget. Agent B is a fixed constrained responder: it must either return an offer containing the quoted private constraint when the request semantically matches the fact it holds, or return a decline otherwise.
MIRA-Math is therefore not a general multi-agent collaboration benchmark. The responder does not solve, explain, negotiate, or volunteer extra hints. The benchmark is designed to isolate three separable skills:
- recognizing that information is missing,
- formulating a precise request for the missing fact, and
- integrating the returned fact into an exact mathematical solution.
The generators, typed hint specifications, instance validators, and final-answer verifiers are deterministic. Request-side metrics are measured under the fixed LLM-mediated responder channel used by the reference runner.
Dataset files
This Hugging Face repository hosts the frozen generated JSONL files for the paper release.
| File | Instances | Prompt regime | Description |
|---|---|---|---|
family_types_20_50.jsonl |
2,310 | zero-shot | Main 20/50 typed benchmark instances with zero-shot solver prompts. |
family_types_20_50_few_shot.jsonl |
2,310 | four-shot | Same benchmark composition with four-shot prompt formatting. |
The two files are prompt-regime variants, not train/test splits. Both use the same paper-style 20/50 typed construction:
11 Type-A families x 3 difficulties x 20 instances = 660 instances
11 Type-B families x 3 difficulties x 50 instances = 1,650 instances
Total 2,310 instances
Loading the dataset
from datasets import load_dataset
dataset = load_dataset("samersaabjr/MIRA-MATH")
print(dataset)
If you want to load the files explicitly as named splits:
from datasets import load_dataset
dataset = load_dataset(
"json",
data_files={
"zero_shot": "https://huggingface.co/datasets/samersaabjr/MIRA-MATH/resolve/main/family_types_20_50.jsonl",
"four_shot": "https://huggingface.co/datasets/samersaabjr/MIRA-MATH/resolve/main/family_types_20_50_few_shot.jsonl",
},
)
print(dataset["zero_shot"][0])
Reference codebase
The reference implementation, generators, validators, verifiers, scoring utilities, prompts, and evaluation runners are available at:
GitHub: https://github.com/cedar-lau/mira-math
Use the GitHub codebase to regenerate the benchmark from fixed seeds, validate instances, run the constrained responder protocol, and reproduce paper-style aggregate tables.
Dataset structure
Each JSONL row contains one MIRA-Math instance. The main top-level fields are:
| Field | Meaning |
|---|---|
id |
Unique instance identifier. |
family |
Problem-family name. |
difficulty |
Difficulty level, usually 1, 2, or 3. |
n_agents |
Number of agent views, currently 2. |
answer_type |
Expected answer format used by the verifier/scorer. |
global_solution |
Exact ground-truth answer. |
global_metadata |
Family-specific metadata needed for validation or analysis. |
agent_views |
Private views for Agent A and Agent B. |
minimal_hint_spec |
Machine-readable typed atomic hint specification. |
The key construction is:
- Agent A's private view is locally underdetermined.
- Agent B holds the atomic fact needed by Agent A.
- Agent A's view plus the canonical atomic hint determines a unique answer.
- The final answer is checked with exact family-specific normalization rather than loose string matching.
Family taxonomy
MIRA-Math contains 22 typed mathematical families split into fixed-slot and variable-slot regimes.
Type A: fixed missing-information slot
In Type-A families, the missing slot is structurally fixed by the family. The solver must identify the needed kind of fact and integrate it correctly.
| Family | Target | Canonical missing fact |
|---|---|---|
bayes_missing_prior |
Posterior probability | Prior P(H) |
crt_reconstruction |
Integer satisfying congruences | Third congruence |
geometry_coordinates |
Distance from an intersection point | Second line equation |
graph_path_sums |
Triangle edge weights | Third path-sum equation |
linear_system_separator |
Full variable assignment | Separator variable value, usually c |
matrix_completion |
Missing matrix entry | Fixed matrix entry m[1][0] |
moment_problem |
Three-point distribution | Second-moment equation |
phase_retrieval |
Length-4 signal | Sign of x0 |
piecewise_missing_threshold |
Piecewise function value | Threshold t |
rankdef_linear_shared |
Full linear-system solution | Additional equation |
recurrence_missing_init |
Recurrence value | Initial condition a(1) |
Type B: variable missing-information slot
In Type-B families, the missing slot varies by instance. The solver must inspect the current problem, localize the absent slot, and request that exact slot.
| Family | Target | Canonical missing fact |
|---|---|---|
birth_death_missing_rate |
Stationary probability | One birth/death rate |
circuit_missing_resistance |
Equivalent resistance | One resistor value |
deconvolution |
Source signal | One convolution output measurement |
discrete_tomography |
Target grid-cell value | One resolving grid-cell value |
eigenvector_missing_entry |
Matrix-derived target value | One matrix entry |
laplace_grid |
Interior grid value | One boundary value |
linear_system_missing_coeff |
Solution component | One matrix coefficient |
markov_missing_transition |
Stationary probability | One transition probability |
poly_interpolation |
Polynomial value at a query point | One interpolation pair |
portfolio_variance_missing_corr |
Portfolio variance | One pairwise correlation |
steady_state_missing_emission |
HMM observation probability | One emission probability |
Generation and reproducibility
The paper-style files in this repository are generated by the typed generator with:
- family mix:
typed, - Type-A count:
20instances per difficulty and family, - Type-B count:
50instances per difficulty and family, - difficulties:
1,2,3, - generation seed:
1234, - prompt regimes: zero-shot and four-shot.
To regenerate the zero-shot dataset from the reference codebase:
python -m mira_math.generate \
--family typed \
--n-a 20 \
--n-b 50 \
--difficulties 1,2,3 \
--seed 1234 \
--out datasets/generated/mira_math_20_50_zs.jsonl
To regenerate the four-shot dataset:
python -m mira_math.generate \
--family typed \
--n-a 20 \
--n-b 50 \
--difficulties 1,2,3 \
--seed 1234 \
--few-shot \
--out datasets/generated/mira_math_20_50_4s.jsonl
Validate either regenerated file with:
python -m mira_math.validate \
--in datasets/generated/mira_math_20_50_zs.jsonl
For full reproducibility, report the dataset file, file checksum, generator seed, code commit, prompt regime, solver model, responder model, runner method, decoding settings, request budget rule, run date, and transcript checksum.
Evaluation protocol
MIRA-Math is evaluated as an interaction between a solver and a constrained information holder.
- Agent A receives its private view, the target question, and a fixed request budget.
- Agent A may issue a natural-language request for missing information.
- Agent B receives only its private constraints and Agent A's request.
- Agent B returns either:
offer, withhas_exact_match=true, the quoted private constraint, and the extracted hint; ordecline, with no extra information.
- Agent A submits a final answer.
- The final answer is scored by an exact family-specific verifier.
The reference paper protocol fixes Agent B to gpt-4o-mini and varies Agent A. Semantic request matching is therefore controlled and auditable but LLM-mediated; final-answer verification and dataset validation are deterministic.
Metrics
The reference scorer computes per-instance and aggregate metrics including:
| Metric | Meaning |
|---|---|
acc_final |
Whether Agent A's final answer matches the exact ground truth. |
hit_rate |
Fraction of responder messages that are offers rather than declines. |
first_request_success |
Whether the first request was accepted. |
request_attempts |
Number of requests issued by Agent A. |
decline_count |
Number of responder declines. |
hints_used |
Number of offers received. |
requests_before_offer |
Number of declined requests before the first offer, if any. |
rounds_to_final |
Round of the first submitted final answer. |
rounds_to_solve |
Round of the first correct final answer, if any. |
token_cost |
Approximate token estimate based on transcript word count. |
The paper-level diagnostic decomposition distinguishes:
- no canonical hint acquired,
- canonical hint acquired but final answer wrong, and
- final answer correct.
This distinction is important because request acquisition and mathematical integration can fail independently.
Intended uses
MIRA-Math is intended for:
- evaluating whether models ask for missing mathematical information instead of guessing,
- comparing request precision across models and prompts,
- separating information-acquisition failures from downstream computation failures,
- auditing prompt or solver-loop strategies under partial mathematical observability,
- studying family-specific failure modes in exact mathematical reasoning, and
- regression testing solvers against a fixed synthetic benchmark suite.
Out-of-scope uses and limitations
MIRA-Math should not be used as evidence that a model can perform open-ended human clarification, general multi-agent collaboration, tool use, web navigation, social reasoning, or real-world decision-making. The task is synthetic, mathematically structured, and intentionally narrow.
The current release uses one required atomic hint per instance. Many real settings require multiple facts, uncertain evidence, human preferences, or open-ended negotiation. The fixed responder channel improves controllability and reproducibility, but request metrics can still reflect occasional LLM-mediated responder matching errors. Final-answer verification and dataset validation are deterministic.
Ethical considerations
The dataset is synthetically generated and contains mathematical problem text and synthetic numeric values. It does not contain human-subject data, private data, or scraped personal content.
The main ethical risk is overclaiming. Benchmark success should not be presented as evidence of broad collaboration, deployment readiness, or real-world decision-making ability. Users should report the narrow scope of the benchmark and the exact evaluation protocol used.
Maintenance
The 20/50 typed release should be preserved as a stable regression suite. Future releases may add larger seed sets, held-out private seeds, multi-hint instances, alternative prompt protocols, or alternative responder matchers. New releases should document changes to generators, prompts, matching rules, verifiers, scoring scripts, and checksums.
Citation
If you use MIRA-Math, please cite the paper:
@misc{albateh2026miramath,
title = {MIRA-Math: A Benchmark for Minimal Information Requesting and Mathematical Reasoning},
author = {Al Bateh, Charbel and Saab Jr., Samer},
year = {2026},
note = {Benchmark dataset and reference implementation},
url = {https://github.com/cedar-lau/mira-math}
}
License
The generated dataset files are released under CC BY 4.0, unless otherwise specified in the repository settings.
The reference implementation is distributed separately through the GitHub repository and is intended to be released under the MIT License.
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