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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
example_id: int64
question_hash: string
benchmark_name: string
ground_truth: string
direct_pred: string
direct_valid: bool
direct_correct: bool
direct_text: string
preferred_sequence: list<item: string>
  child 0, item: string
base_messages: list<item: struct<role: string, content: string>>
  child 0, item: struct<role: string, content: string>
      child 0, role: string
      child 1, content: string
branches: list<item: struct<example_id: int64, question_hash: string, sequence: list<item: string>, depth: int (... 382 chars omitted)
  child 0, item: struct<example_id: int64, question_hash: string, sequence: list<item: string>, depth: int64, initial (... 370 chars omitted)
      child 0, example_id: int64
      child 1, question_hash: string
      child 2, sequence: list<item: string>
          child 0, item: string
      child 3, depth: int64
      child 4, initial_draft: string
      child 5, drafts: list<item: string>
          child 0, item: string
      child 6, final_pred: string
      child 7, valid: bool
      child 8, correct: bool
      child 9, probe_text: string
      child 10, trajectory: list<item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: str (... 145 chars omitted)
          child 0, item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: string, functi (... 133 chars omitted)
              child 0, role: string
              child 1, content: string
              child 2, tool_ca
...
3, tool_event: struct<role: string, tool_call_id: string, name: string, content: string>
                  child 0, role: string
                  child 1, tool_call_id: string
                  child 2, name: string
                  child 3, content: string
probe_text: string
final_pred: string
valid: bool
depth: int64
initial_draft: string
sequence: list<item: string>
  child 0, item: string
drafts: list<item: string>
  child 0, item: string
trajectory: list<item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: str (... 145 chars omitted)
  child 0, item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: string, functi (... 133 chars omitted)
      child 0, role: string
      child 1, content: string
      child 2, tool_calls: list<item: struct<id: string, type: string, function: struct<name: string, arguments: string>>>
          child 0, item: struct<id: string, type: string, function: struct<name: string, arguments: string>>
              child 0, id: string
              child 1, type: string
              child 2, function: struct<name: string, arguments: string>
                  child 0, name: string
                  child 1, arguments: string
      child 3, tool_event: struct<role: string, tool_call_id: string, name: string, content: string>
          child 0, role: string
          child 1, tool_call_id: string
          child 2, name: string
          child 3, content: string
correct: bool
to
{'example_id': Value('int64'), 'question_hash': Value('string'), 'sequence': List(Value('string')), 'depth': Value('int64'), 'initial_draft': Value('string'), 'drafts': List(Value('string')), 'final_pred': Value('string'), 'valid': Value('bool'), 'correct': Value('bool'), 'probe_text': Value('string'), 'trajectory': List({'role': Value('string'), 'content': Value('string'), 'tool_calls': List({'id': Value('string'), 'type': Value('string'), 'function': {'name': Value('string'), 'arguments': Value('string')}}), 'tool_event': {'role': Value('string'), 'tool_call_id': Value('string'), 'name': Value('string'), 'content': Value('string')}})}
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
              example_id: int64
              question_hash: string
              benchmark_name: string
              ground_truth: string
              direct_pred: string
              direct_valid: bool
              direct_correct: bool
              direct_text: string
              preferred_sequence: list<item: string>
                child 0, item: string
              base_messages: list<item: struct<role: string, content: string>>
                child 0, item: struct<role: string, content: string>
                    child 0, role: string
                    child 1, content: string
              branches: list<item: struct<example_id: int64, question_hash: string, sequence: list<item: string>, depth: int (... 382 chars omitted)
                child 0, item: struct<example_id: int64, question_hash: string, sequence: list<item: string>, depth: int64, initial (... 370 chars omitted)
                    child 0, example_id: int64
                    child 1, question_hash: string
                    child 2, sequence: list<item: string>
                        child 0, item: string
                    child 3, depth: int64
                    child 4, initial_draft: string
                    child 5, drafts: list<item: string>
                        child 0, item: string
                    child 6, final_pred: string
                    child 7, valid: bool
                    child 8, correct: bool
                    child 9, probe_text: string
                    child 10, trajectory: list<item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: str (... 145 chars omitted)
                        child 0, item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: string, functi (... 133 chars omitted)
                            child 0, role: string
                            child 1, content: string
                            child 2, tool_ca
              ...
              3, tool_event: struct<role: string, tool_call_id: string, name: string, content: string>
                                child 0, role: string
                                child 1, tool_call_id: string
                                child 2, name: string
                                child 3, content: string
              probe_text: string
              final_pred: string
              valid: bool
              depth: int64
              initial_draft: string
              sequence: list<item: string>
                child 0, item: string
              drafts: list<item: string>
                child 0, item: string
              trajectory: list<item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: str (... 145 chars omitted)
                child 0, item: struct<role: string, content: string, tool_calls: list<item: struct<id: string, type: string, functi (... 133 chars omitted)
                    child 0, role: string
                    child 1, content: string
                    child 2, tool_calls: list<item: struct<id: string, type: string, function: struct<name: string, arguments: string>>>
                        child 0, item: struct<id: string, type: string, function: struct<name: string, arguments: string>>
                            child 0, id: string
                            child 1, type: string
                            child 2, function: struct<name: string, arguments: string>
                                child 0, name: string
                                child 1, arguments: string
                    child 3, tool_event: struct<role: string, tool_call_id: string, name: string, content: string>
                        child 0, role: string
                        child 1, tool_call_id: string
                        child 2, name: string
                        child 3, content: string
              correct: bool
              to
              {'example_id': Value('int64'), 'question_hash': Value('string'), 'sequence': List(Value('string')), 'depth': Value('int64'), 'initial_draft': Value('string'), 'drafts': List(Value('string')), 'final_pred': Value('string'), 'valid': Value('bool'), 'correct': Value('bool'), 'probe_text': Value('string'), 'trajectory': List({'role': Value('string'), 'content': Value('string'), 'tool_calls': List({'id': Value('string'), 'type': Value('string'), 'function': {'name': Value('string'), 'arguments': Value('string')}}), 'tool_event': {'role': Value('string'), 'tool_call_id': Value('string'), 'name': Value('string'), 'content': Value('string')}})}
              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 dataset

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.

example_id
int64
question_hash
string
sequence
list
depth
int64
initial_draft
string
drafts
list
final_pred
string
valid
bool
correct
bool
probe_text
string
trajectory
list
54
f57746353646854e
[ "extractor" ]
1
D
[ "D", "D" ]
D
true
true
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_54_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_ca...
54
f57746353646854e
[ "reasoner" ]
1
D
[ "D", "D" ]
D
true
true
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_54_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_call...
54
f57746353646854e
[ "verifier" ]
1
D
[ "D", "D" ]
D
true
true
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_54_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"D\"}" } } ], "tool_event": { "role": ...
209
591d24dd6fdd73c8
[ "extractor" ]
1
C
[ "C", "C" ]
C
true
false
DRAFT_ANSWER_C ANSWER_C
[ { "role": "assistant", "content": "DRAFT_ANSWER_C", "tool_calls": [ { "id": "mv_209_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
209
591d24dd6fdd73c8
[ "reasoner" ]
1
C
[ "C", "C" ]
C
true
false
DRAFT_ANSWER_C ANSWER_C
[ { "role": "assistant", "content": "DRAFT_ANSWER_C", "tool_calls": [ { "id": "mv_209_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
209
591d24dd6fdd73c8
[ "verifier" ]
1
C
[ "C", "A" ]
A
true
true
DRAFT_ANSWER_A ANSWER_A
[ { "role": "assistant", "content": "DRAFT_ANSWER_C", "tool_calls": [ { "id": "mv_209_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"C\"}" } } ], "tool_event": { "role":...
141
0df23053184378c3
[ "extractor" ]
1
C
[ "C", "C" ]
C
true
true
DRAFT_ANSWER_C ANSWER_C
[ { "role": "assistant", "content": "DRAFT_ANSWER_C", "tool_calls": [ { "id": "mv_141_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
141
0df23053184378c3
[ "reasoner" ]
1
C
[ "C", "C" ]
C
true
true
DRAFT_ANSWER_C ANSWER_C
[ { "role": "assistant", "content": "DRAFT_ANSWER_C", "tool_calls": [ { "id": "mv_141_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
141
0df23053184378c3
[ "verifier" ]
1
C
[ "C", "C" ]
C
true
true
DRAFT_ANSWER_C ANSWER_C
[ { "role": "assistant", "content": "DRAFT_ANSWER_C", "tool_calls": [ { "id": "mv_141_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"C\"}" } } ], "tool_event": { "role":...
312
5672f6a2a612bee1
[ "extractor" ]
1
D
[ "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_312_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
312
5672f6a2a612bee1
[ "reasoner" ]
1
D
[ "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_312_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
312
5672f6a2a612bee1
[ "verifier" ]
1
D
[ "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_312_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"D\"}" } } ], "tool_event": { "role":...
312
5672f6a2a612bee1
[ "extractor", "reasoner" ]
2
D
[ "D", "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_312_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
312
5672f6a2a612bee1
[ "extractor", "verifier" ]
2
D
[ "D", "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_312_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
312
5672f6a2a612bee1
[ "reasoner", "extractor" ]
2
D
[ "D", "D", "A" ]
A
true
true
DRAFT_ANSWER_A ANSWER_A
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_312_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
312
5672f6a2a612bee1
[ "reasoner", "verifier" ]
2
D
[ "D", "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_312_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
312
5672f6a2a612bee1
[ "verifier", "extractor" ]
2
D
[ "D", "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_312_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"D\"}" } } ], "tool_event": { "role":...
312
5672f6a2a612bee1
[ "verifier", "reasoner" ]
2
D
[ "D", "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_312_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"D\"}" } } ], "tool_event": { "role":...
317
f54404edf31fcb59
[ "extractor" ]
1
D
[ "D", "D" ]
D
true
false
ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_317_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
317
f54404edf31fcb59
[ "reasoner" ]
1
D
[ "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_317_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
317
f54404edf31fcb59
[ "verifier" ]
1
D
[ "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_317_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"D\"}" } } ], "tool_event": { "role":...
317
f54404edf31fcb59
[ "extractor", "reasoner" ]
2
D
[ "D", "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_317_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
317
f54404edf31fcb59
[ "extractor", "verifier" ]
2
D
[ "D", "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_317_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
317
f54404edf31fcb59
[ "reasoner", "extractor" ]
2
D
[ "D", "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_317_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
317
f54404edf31fcb59
[ "reasoner", "verifier" ]
2
D
[ "D", "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_317_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
317
f54404edf31fcb59
[ "verifier", "extractor" ]
2
D
[ "D", "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_317_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"D\"}" } } ], "tool_event": { "role":...
317
f54404edf31fcb59
[ "verifier", "reasoner" ]
2
D
[ "D", "D", "D" ]
D
true
false
DRAFT_ANSWER_D ANSWER_D
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_317_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"D\"}" } } ], "tool_event": { "role":...
277
015ba536480bb18f
[ "extractor" ]
1
D
[ "D", "C" ]
C
true
true
DRAFT_ANSWER_C ANSWER_C
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_277_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
277
015ba536480bb18f
[ "reasoner" ]
1
D
[ "D", "C" ]
C
true
true
DRAFT_ANSWER_C ANSWER_C
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_277_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
277
015ba536480bb18f
[ "verifier" ]
1
D
[ "D", "C" ]
C
true
true
DRAFT_ANSWER_C ANSWER_C
[ { "role": "assistant", "content": "DRAFT_ANSWER_D", "tool_calls": [ { "id": "mv_277_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"D\"}" } } ], "tool_event": { "role":...
413
1551ad3af921bbea
[ "extractor" ]
1
B
[ "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_413_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
413
1551ad3af921bbea
[ "reasoner" ]
1
B
[ "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_413_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
413
1551ad3af921bbea
[ "verifier" ]
1
B
[ "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_413_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"B\"}" } } ], "tool_event": { "role":...
413
1551ad3af921bbea
[ "extractor", "reasoner" ]
2
B
[ "B", "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_413_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
413
1551ad3af921bbea
[ "extractor", "verifier" ]
2
B
[ "B", "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_413_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
413
1551ad3af921bbea
[ "reasoner", "extractor" ]
2
B
[ "B", "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_413_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
413
1551ad3af921bbea
[ "reasoner", "verifier" ]
2
B
[ "B", "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_413_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
413
1551ad3af921bbea
[ "verifier", "extractor" ]
2
B
[ "B", "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_413_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"B\"}" } } ], "tool_event": { "role":...
413
1551ad3af921bbea
[ "verifier", "reasoner" ]
2
B
[ "B", "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_413_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"B\"}" } } ], "tool_event": { "role":...
372
ddff2d90c69f85c3
[ "extractor" ]
1
C
[ "C", "C" ]
C
true
true
DRAFT_ANSWER_C The paramagnetic coupling term (Zeeman energy shift) is given by $\langle H \rangle = m \mu_B B$. For small $m$ (e.g., $m=1$) and $B=1$ T, this is approximately $1 \times 5.79 \times 10^{-5}$ eV $\approx 5.8 \times 10^{-5}$ eV. The transition energy $\Delta E$ for $\lambda = 0.4861 \mu$m (H$\beta$ line,...
[ { "role": "assistant", "content": "DRAFT_ANSWER_C", "tool_calls": [ { "id": "mv_372_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
372
ddff2d90c69f85c3
[ "reasoner" ]
1
C
[ "C", "C" ]
C
true
true
DRAFT_ANSWER_C ANSWER_C
[ { "role": "assistant", "content": "DRAFT_ANSWER_C", "tool_calls": [ { "id": "mv_372_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
372
ddff2d90c69f85c3
[ "verifier" ]
1
C
[ "C", "C" ]
C
true
true
DRAFT_ANSWER_C ANSWER_C
[ { "role": "assistant", "content": "DRAFT_ANSWER_C", "tool_calls": [ { "id": "mv_372_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"C\"}" } } ], "tool_event": { "role":...
289
367f9dfee792233b
[ "extractor" ]
1
B
[ "B", "B" ]
B
true
false
ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_289_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
289
367f9dfee792233b
[ "reasoner" ]
1
B
[ "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_289_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
289
367f9dfee792233b
[ "verifier" ]
1
B
[ "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_289_verifier", "type": "function", "function": { "name": "verifier_tool", "arguments": "{\"current_draft\": \"B\"}" } } ], "tool_event": { "role":...
289
367f9dfee792233b
[ "extractor", "reasoner" ]
2
B
[ "B", "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_289_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
289
367f9dfee792233b
[ "extractor", "verifier" ]
2
B
[ "B", "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_289_extractor", "type": "function", "function": { "name": "extractor_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_c...
289
367f9dfee792233b
[ "reasoner", "extractor" ]
2
B
[ "B", "B", "B" ]
B
true
false
DRAFT_ANSWER_B ANSWER_B
[ { "role": "assistant", "content": "DRAFT_ANSWER_B", "tool_calls": [ { "id": "mv_289_reasoner", "type": "function", "function": { "name": "reasoner_tool", "arguments": "{}" } } ], "tool_event": { "role": "tool", "tool_cal...
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