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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ArrowInvalid
Message:      JSON parse error: Invalid value. in row 0
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
                  df = pandas_read_json(f)
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
                  return pd.read_json(path_or_buf, **kwargs)
                         ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
                  return json_reader.read()
                         ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
                  obj = self._get_object_parser(self.data)
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
                  obj = FrameParser(json, **kwargs).parse()
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
                  self._parse()
                  ~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
                  ujson_loads(json, precise_float=self.precise_float), dtype=None
                  ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              During handling of the above exception, another exception occurred:
              
              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 327, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
                  pa_table = paj.read_json(
                      io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
                  )
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0

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ScienceMysteryBench — hosted sessions

Raw model sessions behind the manifest fields that ScienceMysteryBench declines to commit: oneshot_codex_transcript, oneshot_claude_transcript, memory_probe_transcript. CONTRIBUTING is explicit that these are hosted and never committed, and that a reviewer must be able to open them.

Gated on purpose. Every file here pairs the scrubbed data with the correct answer, which is exactly the combination the canary line exists to keep out of training corpora. Gating costs a reviewer one click and costs a crawler the whole directory. This is a separate repository from sciencemysterybench-data for a hard reason: that one is fetched anonymously at Docker build time by every task, so it cannot be gated.

oneshot-codex/

One-shot runs under the harness PR #26 names as the Codex half of the difficulty floor — Codex / GPT-5.6 Sol / xhigh. A task must defeat this configuration to leave draft.

slug reward wall clock budget used error vs tolerance
sm-0002 1.0 21m26s 18% of 7200 s 12 s of ±120 s
sm-0003 1.0 14m19s 24% of 3600 s exact match
sm-0006 1.0 7m45s 6.5% of 7200 s 0.09 of ±3.0

All three passed. None is a near miss: time budgets barely touched, numeric errors far inside tolerance. oneshot_codex_failure cannot be filled in truthfully for any of them.

Per directory: codex.jsonl is the raw codex exec --json stream, run.json the configuration and cost, reward.json and answer_record.json the verifier's own output, trial.log the harness log including the exact agent invocation.

memory-probe-reviewer/

The reviewer-side measurement CONTRIBUTING requires be published beside the author's and never averaged.

  • sm-0003NAOw/decrease, reward 0.0. Independently reproduces the trap the author's Opus 5 probe fell into: both models reason that 6 of 12 columns are NAO variants, so the study must be about the winter NAO. Two models, two wrong answers, same wrong column.
  • sm-0002 — the source_class part alone, with no data: correct, in 39 reasoning tokens. The author already recorded this limitation; this measures how large it is. The lesson generalises: probe multi-part keys part by part, because the whole-answer probe scores 0.0 and hides a free part.

Reproducing

harbor run -p tasks/sm-NNNN -a codex -m gpt-5.6-sol \
  --ak reasoning_effort=xhigh -o ~/smb-runs -q -y

run.json records the run-side allowlist additions each trial needed (the task packages were not modified) and the egress facts verified from inside the agent container: huggingface.co, doi.org, datadryad.org and api.crossref.org were all blocked.

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