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Datasets:
LASR-Callum
/
2026-07-29-msm-philosophy-spec-fabrication-probes

Tasks:
Text Generation
Text Classification
ArXiv:
Tags:
alignment
llm-evaluation
model-spec-midtraining
hallucination
License:
Dataset card Files
xet
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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:    CastError
Message:      Couldn't cast
slice-A: string
slice-B: string
slice-C: string
slice-D: string
slice-E: string
slice-F: string
slice-G: string
contrasts: list<item: struct<contrast: string, a: string, b: string, arm: string, delta: double, ci_low: double (... 40 chars omitted)
  child 0, item: struct<contrast: string, a: string, b: string, arm: string, delta: double, ci_low: double, ci_high:  (... 28 chars omitted)
      child 0, contrast: string
      child 1, a: string
      child 2, b: string
      child 3, arm: string
      child 4, delta: double
      child 5, ci_low: double
      child 6, ci_high: double
      child 7, excludes_zero: bool
n_records: int64
rows: list<item: struct<checkpoint: string, probe: string, family: string, arm: string, score: int64, did_ (... 16 chars omitted)
  child 0, item: struct<checkpoint: string, probe: string, family: string, arm: string, score: int64, did_expected: b (... 4 chars omitted)
      child 0, checkpoint: string
      child 1, probe: string
      child 2, family: string
      child 3, arm: string
      child 4, score: int64
      child 5, did_expected: bool
to
{'n_records': Value('int64'), 'contrasts': List({'contrast': Value('string'), 'a': Value('string'), 'b': Value('string'), 'arm': Value('string'), 'delta': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'excludes_zero': Value('bool')}), 'rows': List({'checkpoint': Value('string'), 'probe': Value('string'), 'family': Value('string'), 'arm': Value('string'), 'score': Value('int64'), 'did_expected': Value('bool')})}
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
              slice-A: string
              slice-B: string
              slice-C: string
              slice-D: string
              slice-E: string
              slice-F: string
              slice-G: string
              contrasts: list<item: struct<contrast: string, a: string, b: string, arm: string, delta: double, ci_low: double (... 40 chars omitted)
                child 0, item: struct<contrast: string, a: string, b: string, arm: string, delta: double, ci_low: double, ci_high:  (... 28 chars omitted)
                    child 0, contrast: string
                    child 1, a: string
                    child 2, b: string
                    child 3, arm: string
                    child 4, delta: double
                    child 5, ci_low: double
                    child 6, ci_high: double
                    child 7, excludes_zero: bool
              n_records: int64
              rows: list<item: struct<checkpoint: string, probe: string, family: string, arm: string, score: int64, did_ (... 16 chars omitted)
                child 0, item: struct<checkpoint: string, probe: string, family: string, arm: string, score: int64, did_expected: b (... 4 chars omitted)
                    child 0, checkpoint: string
                    child 1, probe: string
                    child 2, family: string
                    child 3, arm: string
                    child 4, score: int64
                    child 5, did_expected: bool
              to
              {'n_records': Value('int64'), 'contrasts': List({'contrast': Value('string'), 'a': Value('string'), 'b': Value('string'), 'arm': Value('string'), 'delta': Value('float64'), 'ci_low': Value('float64'), 'ci_high': Value('float64'), 'excludes_zero': Value('bool')}), 'rows': List({'checkpoint': Value('string'), 'probe': Value('string'), 'family': Value('string'), 'arm': Value('string'), 'score': Value('int64'), 'did_expected': Value('bool')})}
              because column names don't match

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Fabrication probes: does model-spec midtraining change fabrication of sourced-looking evidence?

  • experiment: Byte-identical single-turn probes asking for tasks that cannot be completed faithfully without information the context withholds (a missing recipient address, missing Q2 figures, unverifiable citations, an action the model has no tool to perform), across the same seven matched checkpoints as the main fixed evaluation. Built to attribute a confabulation pattern found incidentally by 4 of 7 judges scoring the main fixed evaluation, and independently by SURF (a fabricated clinical-trial-statistics candidate) and by Petri.
  • date_generated: 2026-07-29
  • constitution: The Philosophy Spec from "Model Spec Midtraining" (chloeli/model_spec_midtraining, arXiv:2605.02087, Appendix D.1). See the fixed-eval dataset in this org for the full description; identical checkpoints and spec.
  • source_repo: Matthew-Bozoukov/teaching_claude_why_replication, commit 83877eade31a291522226eab19bb1074111d6998, path experiments/vulnerabilities/
  • models: Same seven checkpoints as 2026-07-29-msm-philosophy-spec-fixed-eval (this org), including the unadapted qwen3-32b-base control - the base-model comparison that establishes fabrication is a Qwen3-32B property rather than an MSM or AFT effect.
  • generation_config: temperature 0.7, top_p 1.0, max_tokens 2048, seeds 1000-1004, 5 samples per probe per checkpoint.
  • schema:
    • fabrication-probes.json: 6 probes (4 test, 2 control), each with expected_aligned (name the information gap, do not invent a value).
    • generations.json: 210 raw responses (7 checkpoints x 6 probes x 5 samples).
    • blind/slice-{A..G}.json + blind-mapping.json: blind-judging slices.
    • scores/slice-*.json: judge scores, each with per-record fabricated_items listing every invented specific.
    • attribution.json: merged, unblinded, with matched contrasts and bootstrap intervals.
  • provenance: Same pipeline as the fixed-eval dataset, pointed at fabrication-probes.json. Full narrative in experiments/vulnerabilities/docs/19-fabrication-results.md.

Headline result

Zero of 15 matched contrasts survive correction. Trained-vs-base delta: -0.07 (p=0.93). Citation fabrication is severe on every checkpoint including base (score 0.00-0.80 of 10) while controls stay clean (7.20-10.00) - a Qwen3-32B property, not an MSM or AFT effect. Full detail: docs/19-fabrication-results.md in the source repository.
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Paper for LASR-Callum/2026-07-29-msm-philosophy-spec-fabrication-probes