Datasets:

hranesscom
/

sponge-research-evaluations

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
contract: string
studyId: string
projectUrl: string
benchmark: struct<name: string, url: string, revision: string, datasetSha256: string, selectedRubricSha256: str (... 4 chars omitted)
  child 0, name: string
  child 1, url: string
  child 2, revision: string
  child 3, datasetSha256: string
  child 4, selectedRubricSha256: string
evaluator: struct<model: string, route: string, reasoning: string, maximumCompletionTokens: int64, criterionBat (... 216 chars omitted)
  child 0, model: string
  child 1, route: string
  child 2, reasoning: string
  child 3, maximumCompletionTokens: int64
  child 4, criterionBatchSize: int64
  child 5, configurationSha256: string
  child 6, spongeTransportSourceRevision: string
  child 7, spongeRepository: string
  child 8, privateCalibrationRunnerSha256: string
  child 9, promptTemplateTextSha256: string
  child 10, promptTemplateFileSha256: string
generation: struct<model: string, harnessSha256: string, manifestSha256: string, generationFileSha256: string>
  child 0, model: string
  child 1, harnessSha256: string
  child 2, manifestSha256: string
  child 3, generationFileSha256: string
calibrationPlanSha256: string
independentAuditSha256: string
methodDraftedBy: string
methodReviewStatus: string
generationStyleVersion: string
methodReviewedBy: string
means: struct<coverage-v1: double, coverage-refresh-v1: double>
  child 0, coverage-v1: double
  child 1, coverage-refresh-v1: double
aggregation: string
analysisQualified: bool
missingJudgments: in
...
124 chars omitted)
      child 0, caseId: string
      child 1, armId: string
      child 2, status: string
      child 3, criteria: int64
      child 4, satisfied: int64
      child 5, blocked: int64
      child 6, score: double
      child 7, reportSha256: string
      child 8, gradingCalls: int64
      child 9, gradingCostMicros: int64
      child 10, originalGenerationElapsedMs: int64
calibrationElapsedMs: null
calibrationElapsedReason: string
unrunGradingCalls: int64
comparisonScope: string
priorAdaptedEvaluation: struct<protocolId: string, analysisQualified: bool, plannedBatches: int64, scoredBatches: int64, fai (... 87 chars omitted)
  child 0, protocolId: string
  child 1, analysisQualified: bool
  child 2, plannedBatches: int64
  child 3, scoredBatches: int64
  child 4, failedBatches: int64
  child 5, unrunBatches: int64
  child 6, completeReports: int64
  child 7, pairedComparison: null
cost: struct<currency: string, gradingMicros: int64, canaryMicros: int64, totalNewMicros: int64, verifiedC (... 48 chars omitted)
  child 0, currency: string
  child 1, gradingMicros: int64
  child 2, canaryMicros: int64
  child 3, totalNewMicros: int64
  child 4, verifiedCalls: int64
  child 5, unknownCosts: int64
  child 6, scope: string
assignedReports: int64
failedGradingCalls: int64
exposure: string
date: timestamp[s]
gradingCalls: int64
assignedCases: list<item: string>
  child 0, item: string
meanDifference: double
completeReports: int64
protocolId: string
failedReports: int64
to
{'contract': Value('string'), 'studyId': Value('string'), 'date': Value('timestamp[s]'), 'protocolId': Value('string'), 'comparisonScope': Value('string'), 'analysisQualified': Value('bool'), 'assignedCases': List(Value('string')), 'exposure': Value('string'), 'assignedReports': Value('int64'), 'completeReports': Value('int64'), 'failedReports': Value('int64'), 'missingJudgments': Value('int64'), 'gradingCalls': Value('int64'), 'failedGradingCalls': Value('int64'), 'unrunGradingCalls': Value('int64'), 'rows': List({'caseId': Value('string'), 'armId': Value('string'), 'status': Value('string'), 'criteria': Value('int64'), 'satisfied': Value('int64'), 'blocked': Value('int64'), 'score': Value('float64'), 'reportSha256': Value('string'), 'gradingCalls': Value('int64'), 'gradingCostMicros': Value('int64'), 'originalGenerationElapsedMs': Value('int64')}), 'aggregation': Value('string'), 'means': {'coverage-v1': Value('float64'), 'coverage-refresh-v1': Value('float64')}, 'meanDifference': Value('float64'), 'cost': {'currency': Value('string'), 'gradingMicros': Value('int64'), 'canaryMicros': Value('int64'), 'totalNewMicros': Value('int64'), 'verifiedCalls': Value('int64'), 'unknownCosts': Value('int64'), 'scope': Value('string')}, 'calibrationElapsedMs': Value('null'), 'calibrationElapsedReason': Value('string'), 'priorAdaptedEvaluation': {'protocolId': Value('string'), 'analysisQualified': Value('bool'), 'plannedBatches': Value('int64'), 'scoredBatches': Value('int64'), 'failedBatches': Value('int64'), 'unrunBatches': Value('int64'), 'completeReports': Value('int64'), 'pairedComparison': Value('null')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      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 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              contract: string
              studyId: string
              projectUrl: string
              benchmark: struct<name: string, url: string, revision: string, datasetSha256: string, selectedRubricSha256: str (... 4 chars omitted)
                child 0, name: string
                child 1, url: string
                child 2, revision: string
                child 3, datasetSha256: string
                child 4, selectedRubricSha256: string
              evaluator: struct<model: string, route: string, reasoning: string, maximumCompletionTokens: int64, criterionBat (... 216 chars omitted)
                child 0, model: string
                child 1, route: string
                child 2, reasoning: string
                child 3, maximumCompletionTokens: int64
                child 4, criterionBatchSize: int64
                child 5, configurationSha256: string
                child 6, spongeTransportSourceRevision: string
                child 7, spongeRepository: string
                child 8, privateCalibrationRunnerSha256: string
                child 9, promptTemplateTextSha256: string
                child 10, promptTemplateFileSha256: string
              generation: struct<model: string, harnessSha256: string, manifestSha256: string, generationFileSha256: string>
                child 0, model: string
                child 1, harnessSha256: string
                child 2, manifestSha256: string
                child 3, generationFileSha256: string
              calibrationPlanSha256: string
              independentAuditSha256: string
              methodDraftedBy: string
              methodReviewStatus: string
              generationStyleVersion: string
              methodReviewedBy: string
              means: struct<coverage-v1: double, coverage-refresh-v1: double>
                child 0, coverage-v1: double
                child 1, coverage-refresh-v1: double
              aggregation: string
              analysisQualified: bool
              missingJudgments: in
              ...
              124 chars omitted)
                    child 0, caseId: string
                    child 1, armId: string
                    child 2, status: string
                    child 3, criteria: int64
                    child 4, satisfied: int64
                    child 5, blocked: int64
                    child 6, score: double
                    child 7, reportSha256: string
                    child 8, gradingCalls: int64
                    child 9, gradingCostMicros: int64
                    child 10, originalGenerationElapsedMs: int64
              calibrationElapsedMs: null
              calibrationElapsedReason: string
              unrunGradingCalls: int64
              comparisonScope: string
              priorAdaptedEvaluation: struct<protocolId: string, analysisQualified: bool, plannedBatches: int64, scoredBatches: int64, fai (... 87 chars omitted)
                child 0, protocolId: string
                child 1, analysisQualified: bool
                child 2, plannedBatches: int64
                child 3, scoredBatches: int64
                child 4, failedBatches: int64
                child 5, unrunBatches: int64
                child 6, completeReports: int64
                child 7, pairedComparison: null
              cost: struct<currency: string, gradingMicros: int64, canaryMicros: int64, totalNewMicros: int64, verifiedC (... 48 chars omitted)
                child 0, currency: string
                child 1, gradingMicros: int64
                child 2, canaryMicros: int64
                child 3, totalNewMicros: int64
                child 4, verifiedCalls: int64
                child 5, unknownCosts: int64
                child 6, scope: string
              assignedReports: int64
              failedGradingCalls: int64
              exposure: string
              date: timestamp[s]
              gradingCalls: int64
              assignedCases: list<item: string>
                child 0, item: string
              meanDifference: double
              completeReports: int64
              protocolId: string
              failedReports: int64
              to
              {'contract': Value('string'), 'studyId': Value('string'), 'date': Value('timestamp[s]'), 'protocolId': Value('string'), 'comparisonScope': Value('string'), 'analysisQualified': Value('bool'), 'assignedCases': List(Value('string')), 'exposure': Value('string'), 'assignedReports': Value('int64'), 'completeReports': Value('int64'), 'failedReports': Value('int64'), 'missingJudgments': Value('int64'), 'gradingCalls': Value('int64'), 'failedGradingCalls': Value('int64'), 'unrunGradingCalls': Value('int64'), 'rows': List({'caseId': Value('string'), 'armId': Value('string'), 'status': Value('string'), 'criteria': Value('int64'), 'satisfied': Value('int64'), 'blocked': Value('int64'), 'score': Value('float64'), 'reportSha256': Value('string'), 'gradingCalls': Value('int64'), 'gradingCostMicros': Value('int64'), 'originalGenerationElapsedMs': Value('int64')}), 'aggregation': Value('string'), 'means': {'coverage-v1': Value('float64'), 'coverage-refresh-v1': Value('float64')}, 'meanDifference': Value('float64'), 'cost': {'currency': Value('string'), 'gradingMicros': Value('int64'), 'canaryMicros': Value('int64'), 'totalNewMicros': Value('int64'), 'verifiedCalls': Value('int64'), 'unknownCosts': Value('int64'), 'scope': Value('string')}, 'calibrationElapsedMs': Value('null'), 'calibrationElapsedReason': Value('string'), 'priorAdaptedEvaluation': {'protocolId': Value('string'), 'analysisQualified': Value('bool'), 'plannedBatches': Value('int64'), 'scoredBatches': Value('int64'), 'failedBatches': Value('int64'), 'unrunBatches': Value('int64'), 'completeReports': Value('int64'), 'pairedComparison': Value('null')}}
              because column names don't match

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.

Sponge research evaluations

This archive contains selected numeric research evaluations from Sponge V2. Each study includes its method, source identities, accounting scope and limits. Study IDs identify fixed results; corrections receive separate records.

Study	Comparison	Result	Scope
DRB-II development calibration, 27 September 2026	Revision versus revision with more retrieved evidence	Mean criterion satisfaction: 20.28% versus 32.33%	Two previously exposed tasks, four retained reports, one official-format evaluation per report

The calibration compares Sponge variants using a partial retained source corpus. It supports a descriptive result on those two tasks. Performance on unseen tasks and comparisons with external research systems require separate studies.

Files contain authored methods, numeric results and source hashes. Research reports, retrieved documents, benchmark questions and rubrics, provider responses, and account records are excluded. The earlier incomplete adapted evaluation is recorded alongside the separate calibration.

See each study's metrics.json, provenance.json and rights.md for its data, source identities and attribution. The authored export is licensed under CC BY 4.0.

Downloads last month: 35

Total file size:

13.9 kB