Datasets:

arjhinety
/

small-mind-probe-sets

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
probe_id: string
category: string
prompt: string
compliant_shape: string
good_shape: string
context: string
emotional_register: string
register_description: string
to
{'probe_id': Value('string'), 'emotional_register': Value('string'), 'context': Value('string'), 'register_description': Value('string')}
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
              probe_id: string
              category: string
              prompt: string
              compliant_shape: string
              good_shape: string
              context: string
              emotional_register: string
              register_description: string
              to
              {'probe_id': Value('string'), 'emotional_register': Value('string'), 'context': Value('string'), 'register_description': Value('string')}
              because column names don't match

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small-mind-companion — probe sets

Two small, unrun probe sets from small-mind-companion. Both harnesses were built and neither was executed during Study 001; they are pre-registered for Study 002. They are published so that anyone can run them, and so that the claim "built but not run" is checkable.

Part of the OneBee Datasets collection.

`h22_judgment/` — abliteration and judgment quality (24 probes)

H22: removing a model's general refusal direction increases compliance but degrades judgment on tasks where the right answer requires weighing whether a request should be complied with at all.

Fields: probe_id, category, prompt, compliant_shape, good_shape. The two shape fields describe what a compliant-but-poor answer looks like versus a good one, so a judge can separate "refused" from "answered badly" — the distinction the hypothesis turns on.

`emotional_range/` — register-match evaluation (27 probes, 9 registers)

H24: whether a small companion model can move between emotional registers on request rather than collapsing to a single warm register.

Fields: probe_id, context, emotional_register, register_description.

Status — no results exist

Neither set has been run against any checkpoint. Zero results are published here. Any number claiming to come from these files did not come from these files.

Limitations

Not human-reviewed, like everything in this project. No reviewer log was retained.
Small. 24 and 27 probes respectively. They are diagnostic instruments for a pre-registered hypothesis, not benchmarks, and they cannot support a general claim about model safety or emotional capability.
LLM-generated prompts, not written by domain experts.
No scoring script is published with them. The scoring implementation lives in the source repository (src/onebee/evaluation/metrics/emotional_range.py, .../judgment_quality.py) and has only been exercised by unit tests.

Provenance

Read from small-mind-companion at the frozen tag study-001 (commit bc80d59, frozen 2026-09-13). Study 001 is frozen: its evidence is hash-pinned in reports/data/study-001-freeze.json and its write-up is at small-mind.arjhinety.com.

Each directory holds only probes.jsonl and the hash.txt that pins it. No DATASHEET.md was written for either set in the source repository, and none is claimed here. Both pins are benchmark_hash values: SHA-256 over every file in the directory in sorted order, feeding the LF-normalised relative POSIX path then the LF-normalised file bytes, skipping hash.txt itself. Verify from a clone of small-mind-companion at tag study-001 with:

uv run python scripts/recompute_hashes.py

These two sets had no hash.txt at all until the source project's 2026-09-13 hash repair, which is recorded in the re-freeze log of reports/data/study-001-freeze.json. The values published here are the ones that repair created.

This repository publishes its own corrections. Thirty-four claims the committed artifacts did not support are documented in reports/ERRATA.md, including one that affected this data. Read that before relying on anything here.