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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
model: string
model_key: string
temperature: double
n_scenarios: int64
wall_clock_min: double
rollout_cost_usd: double
openrouter_usage_before_usd: double
openrouter_usage_after_usd: double
results: list<item: struct<scenario: string, variant: string, status: string, elapsed_s: double, transcript_b (... 13 chars omitted)
  child 0, item: struct<scenario: string, variant: string, status: string, elapsed_s: double, transcript_bytes: int64 (... 1 chars omitted)
      child 0, scenario: string
      child 1, variant: string
      child 2, status: string
      child 3, elapsed_s: double
      child 4, transcript_bytes: int64
smoke: bool
config: struct<model: string, model_key: string, published_key: string, base_url: string, temperature: doubl (... 304 chars omitted)
  child 0, model: string
  child 1, model_key: string
  child 2, published_key: string
  child 3, base_url: string
  child 4, temperature: double
  child 5, bench_dir: string
  child 6, output_root: string
  child 7, concurrency: int64
  child 8, scenario_timeout_s: int64
  child 9, prune_images: bool
  child 10, rollouts_per_cell: int64
  child 11, expected_cells: int64
  child 12, shard_count: int64
  child 13, shard_index: int64
  child 14, judges: struct<grok-4.20: string, gemini-3.1-pro-preview: string>
      child 0, grok-4.20: string
      child 1, gemini-3.1-pro-preview: string
  child 15, exclude_scenarios: list<item: string>
      child 0, item: string
git_sha: string
timestamp_utc: string
command: string
to
{'git_sha': Value('string'), 'timestamp_utc': Value('string'), 'config': {'model': Value('string'), 'model_key': Value('string'), 'published_key': Value('string'), 'base_url': Value('string'), 'temperature': Value('float64'), 'bench_dir': Value('string'), 'output_root': Value('string'), 'concurrency': Value('int64'), 'scenario_timeout_s': Value('int64'), 'prune_images': Value('bool'), 'rollouts_per_cell': Value('int64'), 'expected_cells': Value('int64'), 'shard_count': Value('int64'), 'shard_index': Value('int64'), 'judges': {'grok-4.20': Value('string'), 'gemini-3.1-pro-preview': Value('string')}, 'exclude_scenarios': List(Value('string'))}, 'command': Value('string'), 'smoke': Value('bool'), 'n_scenarios': Value('int64'), 'openrouter_usage_before_usd': Value('float64')}
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
              model: string
              model_key: string
              temperature: double
              n_scenarios: int64
              wall_clock_min: double
              rollout_cost_usd: double
              openrouter_usage_before_usd: double
              openrouter_usage_after_usd: double
              results: list<item: struct<scenario: string, variant: string, status: string, elapsed_s: double, transcript_b (... 13 chars omitted)
                child 0, item: struct<scenario: string, variant: string, status: string, elapsed_s: double, transcript_bytes: int64 (... 1 chars omitted)
                    child 0, scenario: string
                    child 1, variant: string
                    child 2, status: string
                    child 3, elapsed_s: double
                    child 4, transcript_bytes: int64
              smoke: bool
              config: struct<model: string, model_key: string, published_key: string, base_url: string, temperature: doubl (... 304 chars omitted)
                child 0, model: string
                child 1, model_key: string
                child 2, published_key: string
                child 3, base_url: string
                child 4, temperature: double
                child 5, bench_dir: string
                child 6, output_root: string
                child 7, concurrency: int64
                child 8, scenario_timeout_s: int64
                child 9, prune_images: bool
                child 10, rollouts_per_cell: int64
                child 11, expected_cells: int64
                child 12, shard_count: int64
                child 13, shard_index: int64
                child 14, judges: struct<grok-4.20: string, gemini-3.1-pro-preview: string>
                    child 0, grok-4.20: string
                    child 1, gemini-3.1-pro-preview: string
                child 15, exclude_scenarios: list<item: string>
                    child 0, item: string
              git_sha: string
              timestamp_utc: string
              command: string
              to
              {'git_sha': Value('string'), 'timestamp_utc': Value('string'), 'config': {'model': Value('string'), 'model_key': Value('string'), 'published_key': Value('string'), 'base_url': Value('string'), 'temperature': Value('float64'), 'bench_dir': Value('string'), 'output_root': Value('string'), 'concurrency': Value('int64'), 'scenario_timeout_s': Value('int64'), 'prune_images': Value('bool'), 'rollouts_per_cell': Value('int64'), 'expected_cells': Value('int64'), 'shard_count': Value('int64'), 'shard_index': Value('int64'), 'judges': {'grok-4.20': Value('string'), 'gemini-3.1-pro-preview': Value('string')}, 'exclude_scenarios': List(Value('string'))}, 'command': Value('string'), 'smoke': Value('bool'), 'n_scenarios': Value('int64'), 'openrouter_usage_before_usd': Value('float64')}
              because column names don't match

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ODCV-Bench eval of LASR-Callum/qwen3.6-27b-lora-t2-9284-chunk-only-702-answeronly-r64 (mode=think) - the ANSWER-ONLY supervision arm on the principle-scoped (chunk-only) corpus. Its 702 difficult-advice rows train on the VISIBLE ANSWER ONLY: the reasoning trace stays in the token stream as unsupervised context (no truncation) and earns no loss. 65 cells x 1 rollout, both conditions, driven from local Docker against a RunPod H200 vLLM endpoint over an SSH tunnel.

field value
experiment ODCV-Bench eval of LASR-Callum/qwen3.6-27b-lora-t2-9284-chunk-only-702-answeronly-r64 (mode=think) - the ANSWER-ONLY supervision arm on the principle-scoped (chunk-only) corpus. Its 702 difficult-advice rows train on the VISIBLE ANSWER ONLY: the reasoning trace stays in the token stream as unsupervised context (no truncation) and earns no loss. 65 cells x 1 rollout, both conditions, driven from local Docker against a RunPod H200 vLLM endpoint over an SSH tunnel.
date_generated 2026-09-01
constitution constitutions/claude_distilled_07_principles_approved/constitution.md (via the training data; never quoted in the trained text)
source_repo teaching_claude_why_replication @ ae71dce4b6d77f3cdf56bf479387be9671e4097e
models target=LASR-Callum/qwen3.6-27b-lora-t2-9284-chunk-only-702-answeronly-r64 base=Qwen/Qwen3.6-27B; judges=x-ai/grok-4.20 + google/gemini-3.1-pro-preview
generation_config {"temperature": 0.0, "rollouts_per_cell": 1, "expected_cells": 65, "concurrency": 12, "scenario_timeout_s": 2400, "served": "vLLM, --agentic (reasoning_parser qwen3, tool_call_parser qwen3_xml), thinking pinned, max_model_len 16384"}
schema rollouts/: self-contained transcripts; results/: results.json + judge outputs; metadata/: run_meta.json + config + provenance
provenance uv run python scratch/odcv_rollout_cli.py --config configs/eval/odcv_bench_chunkonly702_answeronly_r64_1x65.yaml ; uv run python scratch/odcv_judge_cli.py --rollout_dir --config configs/eval/odcv_bench_chunkonly702_answeronly_r64_1x65.yaml ; published by scratch/cot_only/publish_odcv.py
mr_pct 21.0
mr_ci95 [11.3, 32.3]
mean_severity 0.93
n_cells_judged 62
cells_missing 3 - incentivized/Clinical-Trial-Data-Management, mandated/Roboadvisor-Ethicsbypass and incentivized/Ai-Hiring-Assistant, all flagged by the harness itself as 'ok+no_transcript' (the executor exits and Compose SIGKILLs the orchestrator before it flushes messages_record.txt). Three different cells again from the CoT arm's one and the earlier runs' two, so this failure is NOT confined to a fixed set of long-running scenarios.
comparison ITS COMPLEMENT, the headline: cot-only on the same 702 rows and the same cells scored 9.5% [3.2, 17.5]. On the 61 cells BOTH arms judged, answer-only 21.3% (13 violations) vs cot-only 9.8% (6): a paired difference of +11.5pp. Paired bootstrap CI95 on that difference [+0.0, +23.0] and McNemar exact p=0.0923 (discordant 10 vs 3) -- large and consistently directed, but NOT significant at one pass each. Shared control (2 passes) 11.5% [6.2, 19.6]; base fp8 no-SFT 36.9% [21.4, 53.6]; table2-only 43.9%.
riders ONE PASS per arm. The two arms PARTITION the control's difficult-advice supervision exactly (420,037 + 401,033 = 821,070 tokens, 0 rows mismatched), and the loss reweighting confounds each against the control in the SAME direction, so cot-vs-answer is the fair contrast even though either-vs-control is not. But a seed-only replicate of another arm moved 6.1pp, the paired CI touches zero and McNemar does not clear 0.05: treat +11.5pp as a strong lead, not a finding. Passes 2-4 on both arms would settle it.
judging_notes Both judges scored all 62 cells; no non-numeric verdicts and no single-judge cells in this run.
judging_cost_usd 14.6236
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