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opticoder-benchmark-results

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Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
benchmark: string
model: string
n_cases: int64
code_execution_rate: double
feasibility_rate: double
objective_match_rate: double
constraint_correctness: double
parameter_binding_accuracy: double
per_type: struct<transportation: struct<n: double, code_execution_rate: double, feasibility_rate: double, obje (... 653 chars omitted)
  child 0, transportation: struct<n: double, code_execution_rate: double, feasibility_rate: double, objective_match_rate: doubl (... 70 chars omitted)
      child 0, n: double
      child 1, code_execution_rate: double
      child 2, feasibility_rate: double
      child 3, objective_match_rate: double
      child 4, constraint_correctness: double
      child 5, parameter_binding_accuracy: double
  child 1, assignment: struct<n: double, code_execution_rate: double, feasibility_rate: double, objective_match_rate: doubl (... 70 chars omitted)
      child 0, n: double
      child 1, code_execution_rate: double
      child 2, feasibility_rate: double
      child 3, objective_match_rate: double
      child 4, constraint_correctness: double
      child 5, parameter_binding_accuracy: double
  child 2, knapsack: struct<n: double, code_execution_rate: double, feasibility_rate: double, objective_match_rate: doubl (... 70 chars omitted)
      child 0, n: double
      child 1, code_execution_rate: double
      child 2, feasibility_rate: double
      child 3, objective_match_rate: double
      child 4, constraint_correctness: double
      child 5, parameter_binding_accuracy: double
  child 3, production_planning: struct<n: double, code_execution_rate: double, feasibility_rate: double, objective_match_rate: doubl (... 70 chars omitted)
      child 0, n: double
      child 1, code_execution_rate: double
      child 2, feasibility_rate: double
      child 3, objective_match_rate: double
      child 4, constraint_correctness: double
      child 5, parameter_binding_accuracy: double
cases: list<item: struct<case_id: string, problem_type: string, code_executed: bool, feasible: bool, object (... 167 chars omitted)
  child 0, item: struct<case_id: string, problem_type: string, code_executed: bool, feasible: bool, objective_match:  (... 155 chars omitted)
      child 0, case_id: string
      child 1, problem_type: string
      child 2, code_executed: bool
      child 3, feasible: bool
      child 4, objective_match: bool
      child 5, constraint_correct: bool
      child 6, binding_accuracy: double
      child 7, predicted_objective: double
      child 8, ground_truth_objective: double
      child 9, solve_time_sec: double
      child 10, error: null
reference_benchmark: string
metrics: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, constrai (... 59 chars omitted)
  child 0, code_execution_rate: double
  child 1, feasibility_rate: double
  child 2, objective_match_rate: double
  child 3, constraint_correctness: double
  child 4, parameter_binding_accuracy: double
to
{'benchmark': Value('string'), 'reference_benchmark': Value('string'), 'model': Value('string'), 'n_cases': Value('int64'), 'metrics': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'constraint_correctness': Value('float64'), 'parameter_binding_accuracy': Value('float64')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, 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 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/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/python3.14/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
              benchmark: string
              model: string
              n_cases: int64
              code_execution_rate: double
              feasibility_rate: double
              objective_match_rate: double
              constraint_correctness: double
              parameter_binding_accuracy: double
              per_type: struct<transportation: struct<n: double, code_execution_rate: double, feasibility_rate: double, obje (... 653 chars omitted)
                child 0, transportation: struct<n: double, code_execution_rate: double, feasibility_rate: double, objective_match_rate: doubl (... 70 chars omitted)
                    child 0, n: double
                    child 1, code_execution_rate: double
                    child 2, feasibility_rate: double
                    child 3, objective_match_rate: double
                    child 4, constraint_correctness: double
                    child 5, parameter_binding_accuracy: double
                child 1, assignment: struct<n: double, code_execution_rate: double, feasibility_rate: double, objective_match_rate: doubl (... 70 chars omitted)
                    child 0, n: double
                    child 1, code_execution_rate: double
                    child 2, feasibility_rate: double
                    child 3, objective_match_rate: double
                    child 4, constraint_correctness: double
                    child 5, parameter_binding_accuracy: double
                child 2, knapsack: struct<n: double, code_execution_rate: double, feasibility_rate: double, objective_match_rate: doubl (... 70 chars omitted)
                    child 0, n: double
                    child 1, code_execution_rate: double
                    child 2, feasibility_rate: double
                    child 3, objective_match_rate: double
                    child 4, constraint_correctness: double
                    child 5, parameter_binding_accuracy: double
                child 3, production_planning: struct<n: double, code_execution_rate: double, feasibility_rate: double, objective_match_rate: doubl (... 70 chars omitted)
                    child 0, n: double
                    child 1, code_execution_rate: double
                    child 2, feasibility_rate: double
                    child 3, objective_match_rate: double
                    child 4, constraint_correctness: double
                    child 5, parameter_binding_accuracy: double
              cases: list<item: struct<case_id: string, problem_type: string, code_executed: bool, feasible: bool, object (... 167 chars omitted)
                child 0, item: struct<case_id: string, problem_type: string, code_executed: bool, feasible: bool, objective_match:  (... 155 chars omitted)
                    child 0, case_id: string
                    child 1, problem_type: string
                    child 2, code_executed: bool
                    child 3, feasible: bool
                    child 4, objective_match: bool
                    child 5, constraint_correct: bool
                    child 6, binding_accuracy: double
                    child 7, predicted_objective: double
                    child 8, ground_truth_objective: double
                    child 9, solve_time_sec: double
                    child 10, error: null
              reference_benchmark: string
              metrics: struct<code_execution_rate: double, feasibility_rate: double, objective_match_rate: double, constrai (... 59 chars omitted)
                child 0, code_execution_rate: double
                child 1, feasibility_rate: double
                child 2, objective_match_rate: double
                child 3, constraint_correctness: double
                child 4, parameter_binding_accuracy: double
              to
              {'benchmark': Value('string'), 'reference_benchmark': Value('string'), 'model': Value('string'), 'n_cases': Value('int64'), 'metrics': {'code_execution_rate': Value('float64'), 'feasibility_rate': Value('float64'), 'objective_match_rate': Value('float64'), 'constraint_correctness': Value('float64'), 'parameter_binding_accuracy': Value('float64')}}
              because column names don't match

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OptiCoder Benchmark Results

Execution-based evaluation results for the OptiCoder text-to-MILP agent.

Metrics

Metric	Description
`code_execution_rate`	Share of cases where generated code executes
`feasibility_rate`	Share of cases with feasible solver output
`objective_match_rate`	Share within 5% of ground-truth objective
`constraint_correctness`	Share with no post-solve constraint violations
`parameter_binding_accuracy`	Share with correct parameter extraction

Reference Benchmark

ZhiqiGao/Text2Opt-Bench

Reproduce on HF Jobs

hf jobs uv run scripts/run_benchmark_job.py \
  --flavor cpu-basic --secrets HF_TOKEN=$HF_TOKEN --detach

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