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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'wrong_group', 'true_rank', 'true_gap', 'wrong_gap'}) and 11 missing columns ({'split', 'second_group', 'chosen_template_ids', 'gap', 'chosen_group', 'rank', 'second_score', 'mean_candidate_phase_l1', 'top_score', 'method', 'second_template_ids'}).
This happened while the csv dataset builder was generating data using
hf://datasets/VonEquinox/efficient-agent-experiment-logs/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/corruption_tonal_validation/per_image.csv (at revision 1bbfaab66641eba590414607deb020ac75849583), ['hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/advanced_family_retrieval_validation/loio_all_dirty_images.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/advanced_family_retrieval_validation/low_margin_focus.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/advanced_family_retrieval_validation/test_family_scores.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/corruption_tonal_validation/per_image.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/denoise_validation/per_image_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/exact_template_residual_validation/candidate_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/exact_template_residual_validation/nested_outer_rows.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/exact_template_residual_validation/per_image_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/exact_template_residual_validation/retrieval.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/exact_template_residual_validation/test_diagnostics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/exhaustive_family_validation/test_perturbations.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/exhaustive_family_validation/test_summary.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/exhaustive_family_validation/train_loio.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/focused_pair_validation/candidate_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/focused_pair_validation/nested_outer.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/focused_pair_validation/wrong_rank2_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/fresh_public_validation/hybrid_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/fresh_public_validation/outer_selection.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/fresh_public_validation/per_image_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/generic_hybrid_validation/blends.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/generic_hybrid_validation/details.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/generic_hybrid_validation/gates.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/generic_hybrid_validation/metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/generic_hybrid_validation/nested_hybrid.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/generic_hybrid_validation/test_diagnostics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/independent_mapping_validation/heldout_one_per_group.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/independent_mapping_validation/learned_descriptor_heldout.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/independent_mapping_validation/loio_consensus.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/independent_mapping_validation/sift_heldout.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/independent_mapping_validation/test_descriptor_predictions.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/n2v_validation/per_image_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/nested_transform_validation/descriptive_grid.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/nested_transform_validation/outer_predictions.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/nonlinear_restoration_validation/per_image_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/patch_retrieval_validation/fold_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/patch_retrieval_validation/per_image_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/regression_validation/per_image_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/robust_template_matching_validation/copy_heldout_metrics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/robust_template_matching_validation/test_mapping_comparison.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/target_specific_gating_validation/logo_gate_grid.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/target_specific_gating_validation/loio_gate_grid.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/target_specific_gating_validation/nested_loio_rows.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/target_specific_gating_validation/test_gate_diagnostics.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/template_geometry_validation/query_holdout_folds.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/template_geometry_validation/registration_identity_check.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/template_geometry_validation/test_template_uncertainty.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/template_geometry_validation/train_template_retrieval.csv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
pa_table = table_cast(pa_table, self._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
id: int64
true_rank: int64
true_gap: double
wrong_gap: double
true_group: int64
wrong_group: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 967
to
{'split': Value('string'), 'id': Value('int64'), 'method': Value('string'), 'true_group': Value('int64'), 'chosen_group': Value('int64'), 'chosen_template_ids': Value('string'), 'rank': Value('int64'), 'top_score': Value('float64'), 'second_score': Value('float64'), 'second_group': Value('int64'), 'second_template_ids': Value('string'), 'gap': Value('float64'), 'mean_candidate_phase_l1': Value('float64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1694, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 4 new columns ({'wrong_group', 'true_rank', 'true_gap', 'wrong_gap'}) and 11 missing columns ({'split', 'second_group', 'chosen_template_ids', 'gap', 'chosen_group', 'rank', 'second_score', 'mean_candidate_phase_l1', 'top_score', 'method', 'second_template_ids'}).
This happened while the csv dataset builder was generating data using
hf://datasets/VonEquinox/efficient-agent-experiment-logs/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/corruption_tonal_validation/per_image.csv (at revision 1bbfaab66641eba590414607deb020ac75849583), ['hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/advanced_family_retrieval_validation/loio_all_dirty_images.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/advanced_family_retrieval_validation/low_margin_focus.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/advanced_family_retrieval_validation/test_family_scores.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/corruption_tonal_validation/per_image.csv', 'hf://datasets/VonEquinox/efficient-agent-experiment-logs@1bbfaab66641eba590414607deb020ac75849583/experiment-campaigns/20260904_125112_mle_7agent_22task_remaining/round-05/denoising-dirty-documents/claude-code/seed-0/denoising-dirty-documents/agent-output/workspace/denoise_validation/per_image_metrics.csv', 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Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)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.
split string | id int64 | method string | true_group int64 | chosen_group int64 | chosen_template_ids string | rank int64 | top_score float64 | second_score float64 | second_group int64 | second_template_ids string | gap float64 | mean_candidate_phase_l1 float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
LOIO | 2 | partial_native | 0 | 0 | 2;5;11 | 1 | 0.920151 | 0.648387 | 5 | 29;32 | 0.271764 | 2.34733 |
LOIO | 2 | partial_phase_scoring | 0 | 0 | 2;5;11 | 1 | 0.918406 | 0.66372 | 5 | 29;32 | 0.254686 | 2.34733 |
LOIO | 2 | partial_hybrid | 0 | 0 | 2;5;11 | 1 | 0.919662 | 0.65268 | 5 | 29;32 | 0.266982 | 2.34733 |
LOIO | 3 | partial_native | 1 | 1 | 3;9;12 | 1 | 0.878379 | 0.704077 | 3 | 15;18;21;24 | 0.174302 | 2.780303 |
LOIO | 3 | partial_phase_scoring | 1 | 1 | 3;9;12 | 1 | 0.876701 | 0.714699 | 3 | 15;18;21;24 | 0.162002 | 2.780303 |
LOIO | 3 | partial_hybrid | 1 | 1 | 3;9;12 | 1 | 0.877909 | 0.707052 | 3 | 15;18;21;24 | 0.170858 | 2.780303 |
LOIO | 5 | partial_native | 0 | 0 | 2;5;11 | 1 | 0.98667 | 0.70075 | 5 | 29;32 | 0.28592 | 1.88739 |
LOIO | 5 | partial_phase_scoring | 0 | 0 | 2;5;11 | 1 | 0.986335 | 0.709435 | 5 | 29;32 | 0.276899 | 1.88739 |
LOIO | 5 | partial_hybrid | 0 | 0 | 2;5;11 | 1 | 0.986576 | 0.703182 | 5 | 29;32 | 0.283394 | 1.88739 |
LOIO | 9 | partial_native | 1 | 1 | 3;9;12 | 1 | 0.974714 | 0.7853 | 3 | 15;18;21;24 | 0.189413 | 2.040405 |
LOIO | 9 | partial_phase_scoring | 1 | 1 | 3;9;12 | 1 | 0.974309 | 0.784059 | 3 | 15;18;21;24 | 0.19025 | 2.040405 |
LOIO | 9 | partial_hybrid | 1 | 1 | 3;9;12 | 1 | 0.9746 | 0.784953 | 3 | 15;18;21;24 | 0.189647 | 2.040405 |
LOIO | 11 | partial_native | 0 | 0 | 2;5;11 | 1 | 0.923392 | 0.661627 | 5 | 29;32 | 0.261764 | 1.828077 |
LOIO | 11 | partial_phase_scoring | 0 | 0 | 2;5;11 | 1 | 0.922678 | 0.680342 | 5 | 29;32 | 0.242335 | 1.828077 |
LOIO | 11 | partial_hybrid | 0 | 0 | 2;5;11 | 1 | 0.923192 | 0.666868 | 5 | 29;32 | 0.256324 | 1.828077 |
LOIO | 12 | partial_native | 1 | 1 | 3;9;12 | 1 | 0.927547 | 0.749231 | 3 | 15;18;21;24 | 0.178315 | 2.799767 |
LOIO | 12 | partial_phase_scoring | 1 | 1 | 3;9;12 | 1 | 0.927512 | 0.759383 | 3 | 15;18;21;24 | 0.168128 | 2.799767 |
LOIO | 12 | partial_hybrid | 1 | 1 | 3;9;12 | 1 | 0.927537 | 0.752074 | 3 | 15;18;21;24 | 0.175463 | 2.799767 |
LOIO | 14 | partial_native | 2 | 2 | 14;17;20;23 | 1 | 0.920954 | 0.572384 | 0 | 2;5;11 | 0.34857 | 3.532556 |
LOIO | 14 | partial_phase_scoring | 2 | 2 | 14;17;20;23 | 1 | 0.916453 | 0.558925 | 5 | 29;32 | 0.357528 | 3.532556 |
LOIO | 14 | partial_hybrid | 2 | 2 | 14;17;20;23 | 1 | 0.919694 | 0.560927 | 5 | 29;32 | 0.358767 | 3.532556 |
LOIO | 15 | partial_native | 3 | 3 | 15;18;21;24 | 1 | 0.886875 | 0.708972 | 1 | 3;9;12 | 0.177903 | 2.728281 |
LOIO | 15 | partial_phase_scoring | 3 | 3 | 15;18;21;24 | 1 | 0.877079 | 0.715771 | 1 | 3;9;12 | 0.161308 | 2.728281 |
LOIO | 15 | partial_hybrid | 3 | 3 | 15;18;21;24 | 1 | 0.884132 | 0.710876 | 1 | 3;9;12 | 0.173256 | 2.728281 |
LOIO | 17 | partial_native | 2 | 2 | 14;17;20;23 | 1 | 0.98423 | 0.622691 | 0 | 2;5;11 | 0.361539 | 3.908685 |
LOIO | 17 | partial_phase_scoring | 2 | 2 | 14;17;20;23 | 1 | 0.983003 | 0.651598 | 0 | 2;5;11 | 0.331405 | 3.908685 |
LOIO | 17 | partial_hybrid | 2 | 2 | 14;17;20;23 | 1 | 0.983887 | 0.630785 | 0 | 2;5;11 | 0.353102 | 3.908685 |
LOIO | 18 | partial_native | 3 | 3 | 15;18;21;24 | 1 | 0.985304 | 0.801543 | 1 | 3;9;12 | 0.183761 | 2.347511 |
LOIO | 18 | partial_phase_scoring | 3 | 3 | 15;18;21;24 | 1 | 0.984626 | 0.805994 | 1 | 3;9;12 | 0.178632 | 2.347511 |
LOIO | 18 | partial_hybrid | 3 | 3 | 15;18;21;24 | 1 | 0.985114 | 0.802789 | 1 | 3;9;12 | 0.182325 | 2.347511 |
LOIO | 20 | partial_native | 2 | 2 | 14;17;20;23 | 1 | 0.96286 | 0.600049 | 0 | 2;5;11 | 0.362811 | 3.355147 |
LOIO | 20 | partial_phase_scoring | 2 | 2 | 14;17;20;23 | 1 | 0.961196 | 0.623655 | 0 | 2;5;11 | 0.337541 | 3.355147 |
LOIO | 20 | partial_hybrid | 2 | 2 | 14;17;20;23 | 1 | 0.962394 | 0.606659 | 0 | 2;5;11 | 0.355735 | 3.355147 |
LOIO | 21 | partial_native | 3 | 3 | 15;18;21;24 | 1 | 0.975412 | 0.782504 | 1 | 3;9;12 | 0.192908 | 3.376123 |
LOIO | 21 | partial_phase_scoring | 3 | 3 | 15;18;21;24 | 1 | 0.975412 | 0.784341 | 1 | 3;9;12 | 0.191072 | 3.376123 |
LOIO | 21 | partial_hybrid | 3 | 3 | 15;18;21;24 | 1 | 0.975412 | 0.783019 | 1 | 3;9;12 | 0.192394 | 3.376123 |
LOIO | 23 | partial_native | 2 | 2 | 14;17;20;23 | 1 | 0.919169 | 0.580499 | 0 | 2;5;11 | 0.33867 | 3.819741 |
LOIO | 23 | partial_phase_scoring | 2 | 2 | 14;17;20;23 | 1 | 0.913561 | 0.605238 | 5 | 29;32 | 0.308323 | 3.819741 |
LOIO | 23 | partial_hybrid | 2 | 2 | 14;17;20;23 | 1 | 0.917598 | 0.583575 | 5 | 29;32 | 0.334024 | 3.819741 |
LOIO | 24 | partial_native | 3 | 3 | 15;18;21;24 | 1 | 0.923342 | 0.752569 | 1 | 3;9;12 | 0.170773 | 3.013334 |
LOIO | 24 | partial_phase_scoring | 3 | 3 | 15;18;21;24 | 1 | 0.920708 | 0.7588 | 1 | 3;9;12 | 0.161908 | 3.013334 |
LOIO | 24 | partial_hybrid | 3 | 3 | 15;18;21;24 | 1 | 0.922604 | 0.754313 | 1 | 3;9;12 | 0.168291 | 3.013334 |
LOIO | 27 | partial_native | 4 | 4 | 27;30;33 | 1 | 0.899087 | 0.841243 | 7 | 39;45;48 | 0.057843 | 3.20386 |
LOIO | 27 | partial_phase_scoring | 4 | 4 | 27;30;33 | 1 | 0.893734 | 0.736004 | 7 | 39;45;48 | 0.15773 | 3.20386 |
LOIO | 27 | partial_hybrid | 4 | 4 | 27;30;33 | 1 | 0.897588 | 0.811776 | 7 | 39;45;48 | 0.085812 | 3.20386 |
LOIO | 29 | partial_native | 5 | 5 | 29;32 | 1 | 0.989067 | 0.924336 | 6 | 38;41;44;47 | 0.064732 | 2.485523 |
LOIO | 29 | partial_phase_scoring | 5 | 5 | 29;32 | 1 | 0.988756 | 0.859836 | 6 | 38;41;44;47 | 0.12892 | 2.485523 |
LOIO | 29 | partial_hybrid | 5 | 5 | 29;32 | 1 | 0.98898 | 0.906276 | 6 | 38;41;44;47 | 0.082704 | 2.485523 |
LOIO | 30 | partial_native | 4 | 4 | 27;30;33 | 1 | 0.990333 | 0.928788 | 7 | 39;45;48 | 0.061545 | 3.493212 |
LOIO | 30 | partial_phase_scoring | 4 | 4 | 27;30;33 | 1 | 0.989866 | 0.876633 | 7 | 39;45;48 | 0.113233 | 3.493212 |
LOIO | 30 | partial_hybrid | 4 | 4 | 27;30;33 | 1 | 0.990202 | 0.914184 | 7 | 39;45;48 | 0.076018 | 3.493212 |
LOIO | 32 | partial_native | 5 | 5 | 29;32 | 1 | 0.972452 | 0.907006 | 6 | 38;41;44;47 | 0.065446 | 2.420645 |
LOIO | 32 | partial_phase_scoring | 5 | 5 | 29;32 | 1 | 0.972452 | 0.82663 | 6 | 38;41;44;47 | 0.145822 | 2.420645 |
LOIO | 32 | partial_hybrid | 5 | 5 | 29;32 | 1 | 0.972452 | 0.884501 | 6 | 38;41;44;47 | 0.087951 | 2.420645 |
LOIO | 33 | partial_native | 4 | 4 | 27;30;33 | 1 | 0.983492 | 0.921059 | 7 | 39;45;48 | 0.062433 | 2.938141 |
LOIO | 33 | partial_phase_scoring | 4 | 4 | 27;30;33 | 1 | 0.983337 | 0.852003 | 7 | 39;45;48 | 0.131334 | 2.938141 |
LOIO | 33 | partial_hybrid | 4 | 4 | 27;30;33 | 1 | 0.983449 | 0.901723 | 7 | 39;45;48 | 0.081725 | 2.938141 |
LOIO | 38 | partial_native | 6 | 6 | 38;41;44;47 | 1 | 0.936232 | 0.871901 | 5 | 29;32 | 0.064331 | 3.242453 |
LOIO | 38 | partial_phase_scoring | 6 | 6 | 38;41;44;47 | 1 | 0.93545 | 0.749188 | 5 | 29;32 | 0.186262 | 3.242453 |
LOIO | 38 | partial_hybrid | 6 | 6 | 38;41;44;47 | 1 | 0.936013 | 0.837541 | 5 | 29;32 | 0.098472 | 3.242453 |
LOIO | 39 | partial_native | 7 | 7 | 39;45;48 | 1 | 0.902164 | 0.844632 | 4 | 27;30;33 | 0.057532 | 3.102565 |
LOIO | 39 | partial_phase_scoring | 7 | 7 | 39;45;48 | 1 | 0.901335 | 0.78418 | 4 | 27;30;33 | 0.117154 | 3.102565 |
LOIO | 39 | partial_hybrid | 7 | 7 | 39;45;48 | 1 | 0.901932 | 0.827705 | 4 | 27;30;33 | 0.074226 | 3.102565 |
LOIO | 41 | partial_native | 6 | 6 | 38;41;44;47 | 1 | 0.986949 | 0.923586 | 5 | 29;32 | 0.063363 | 3.249367 |
LOIO | 41 | partial_phase_scoring | 6 | 6 | 38;41;44;47 | 1 | 0.986553 | 0.855574 | 5 | 29;32 | 0.130979 | 3.249367 |
LOIO | 41 | partial_hybrid | 6 | 6 | 38;41;44;47 | 1 | 0.986838 | 0.904542 | 5 | 29;32 | 0.082296 | 3.249367 |
LOIO | 44 | partial_native | 6 | 6 | 38;41;44;47 | 1 | 0.973006 | 0.906551 | 5 | 29;32 | 0.066456 | 2.88289 |
LOIO | 44 | partial_phase_scoring | 6 | 6 | 38;41;44;47 | 1 | 0.97164 | 0.798262 | 5 | 29;32 | 0.173378 | 2.88289 |
LOIO | 44 | partial_hybrid | 6 | 6 | 38;41;44;47 | 1 | 0.972624 | 0.87623 | 5 | 29;32 | 0.096394 | 2.88289 |
LOIO | 45 | partial_native | 7 | 7 | 39;45;48 | 1 | 0.983689 | 0.919788 | 4 | 27;30;33 | 0.063902 | 2.322442 |
LOIO | 45 | partial_phase_scoring | 7 | 7 | 39;45;48 | 1 | 0.983253 | 0.852791 | 4 | 27;30;33 | 0.130462 | 2.322442 |
LOIO | 45 | partial_hybrid | 7 | 7 | 39;45;48 | 1 | 0.983567 | 0.901029 | 4 | 27;30;33 | 0.082539 | 2.322442 |
LOIO | 47 | partial_native | 6 | 6 | 38;41;44;47 | 1 | 0.935858 | 0.876527 | 5 | 29;32 | 0.059331 | 2.177024 |
LOIO | 47 | partial_phase_scoring | 6 | 6 | 38;41;44;47 | 1 | 0.93416 | 0.75391 | 5 | 29;32 | 0.18025 | 2.177024 |
LOIO | 47 | partial_hybrid | 6 | 6 | 38;41;44;47 | 1 | 0.935383 | 0.842194 | 5 | 29;32 | 0.093188 | 2.177024 |
LOIO | 48 | partial_native | 7 | 7 | 39;45;48 | 1 | 0.936056 | 0.878859 | 4 | 27;30;33 | 0.057197 | 2.888718 |
LOIO | 48 | partial_phase_scoring | 7 | 7 | 39;45;48 | 1 | 0.931408 | 0.799466 | 4 | 27;30;33 | 0.131942 | 2.888718 |
LOIO | 48 | partial_hybrid | 7 | 7 | 39;45;48 | 1 | 0.934754 | 0.856629 | 4 | 27;30;33 | 0.078125 | 2.888718 |
LOIO | 50 | partial_native | 8 | 8 | 50;53;56;59 | 1 | 0.937376 | 0.822929 | 11 | 65;71 | 0.114446 | 3.516872 |
LOIO | 50 | partial_phase_scoring | 8 | 8 | 50;53;56;59 | 1 | 0.936263 | 0.703886 | 11 | 65;71 | 0.232377 | 3.516872 |
LOIO | 50 | partial_hybrid | 8 | 8 | 50;53;56;59 | 1 | 0.937064 | 0.789597 | 11 | 65;71 | 0.147467 | 3.516872 |
LOIO | 51 | partial_native | 9 | 9 | 51;57;60 | 1 | 0.899774 | 0.794013 | 10 | 63;66;69;72 | 0.105761 | 3.416023 |
LOIO | 51 | partial_phase_scoring | 9 | 9 | 51;57;60 | 1 | 0.897202 | 0.746867 | 10 | 63;66;69;72 | 0.150335 | 3.416023 |
LOIO | 51 | partial_hybrid | 9 | 9 | 51;57;60 | 1 | 0.899054 | 0.780812 | 10 | 63;66;69;72 | 0.118242 | 3.416023 |
LOIO | 53 | partial_native | 8 | 8 | 50;53;56;59 | 1 | 0.989063 | 0.869577 | 11 | 65;71 | 0.119486 | 4.208312 |
LOIO | 53 | partial_phase_scoring | 8 | 8 | 50;53;56;59 | 1 | 0.988785 | 0.779933 | 11 | 65;71 | 0.208852 | 4.208312 |
LOIO | 53 | partial_hybrid | 8 | 8 | 50;53;56;59 | 1 | 0.988985 | 0.844477 | 11 | 65;71 | 0.144509 | 4.208312 |
LOIO | 56 | partial_native | 8 | 8 | 50;53;56;59 | 1 | 0.971024 | 0.851489 | 11 | 65;71 | 0.119535 | 3.79714 |
LOIO | 56 | partial_phase_scoring | 8 | 8 | 50;53;56;59 | 1 | 0.97027 | 0.74909 | 11 | 65;71 | 0.22118 | 3.79714 |
LOIO | 56 | partial_hybrid | 8 | 8 | 50;53;56;59 | 1 | 0.970813 | 0.822817 | 11 | 65;71 | 0.147996 | 3.79714 |
LOIO | 57 | partial_native | 9 | 9 | 51;57;60 | 1 | 0.982852 | 0.867888 | 10 | 63;66;69;72 | 0.114964 | 3.365004 |
LOIO | 57 | partial_phase_scoring | 9 | 9 | 51;57;60 | 1 | 0.982852 | 0.789903 | 10 | 63;66;69;72 | 0.192949 | 3.365004 |
LOIO | 57 | partial_hybrid | 9 | 9 | 51;57;60 | 1 | 0.982852 | 0.846052 | 10 | 63;66;69;72 | 0.1368 | 3.365004 |
LOIO | 59 | partial_native | 8 | 8 | 50;53;56;59 | 1 | 0.934274 | 0.821385 | 11 | 65;71 | 0.112889 | 3.779359 |
LOIO | 59 | partial_phase_scoring | 8 | 8 | 50;53;56;59 | 1 | 0.933396 | 0.716192 | 11 | 65;71 | 0.217204 | 3.779359 |
LOIO | 59 | partial_hybrid | 8 | 8 | 50;53;56;59 | 1 | 0.934028 | 0.791931 | 11 | 65;71 | 0.142097 | 3.779359 |
LOIO | 60 | partial_native | 9 | 9 | 51;57;60 | 1 | 0.939233 | 0.831092 | 10 | 63;66;69;72 | 0.108141 | 3.328005 |
LOIO | 60 | partial_phase_scoring | 9 | 9 | 51;57;60 | 1 | 0.938144 | 0.740075 | 10 | 63;66;69;72 | 0.198069 | 3.328005 |
LOIO | 60 | partial_hybrid | 9 | 9 | 51;57;60 | 1 | 0.938928 | 0.805607 | 10 | 63;66;69;72 | 0.133321 | 3.328005 |
LOIO | 63 | partial_native | 10 | 10 | 63;66;69;72 | 1 | 0.906156 | 0.798406 | 9 | 51;57;60 | 0.10775 | 3.063593 |
End of preview.
Efficient Agent Experiment Campaigns
This snapshot preserves experiment records and artifacts from the
experiment-campaigns directory of the efficient-agent-research project.
It contains completed, failed, and interrupted runs. A directory or artifact
existing in this snapshot does not imply a valid official evaluation score.
Refer to each run's result and grader report for its status.
Contents
records/*.tar.gz: credential-redacted logs, Agent trajectories, code, configuration, manifests, grading reports, and source-code diffs grouped by campaign. Archive member paths preserve the original campaign layout.experiment-campaigns/...: model artifacts, prediction CSVs, and derived numerical arrays, retaining the original paths.MANIFEST.json: file inventory, snapshot information, and export details.
Original benchmark datasets and prepared input copies, downloadable caches, Python environments, Git object databases, credential files, and duplicate job-export archives are excluded. Data-bearing sections of source diffs are also omitted. File types such as pickle/joblib may represent fitted models or other serialized experiment state; they are not all neural-network weights.
The September 17 MLEvolve round was paused when inventoried. Its artifacts are intermediate state, not final submissions. Local copies must remain available for resuming those experiments.
Reproducibility And Redaction
Credentials and authorization headers are redacted in exported text. Original local records remain unchanged. As a result, original manifest/result hashes inside archived logs describe their original files, not the redacted copies.
The upload workflow hashes original artifacts, checks for credential-like content, refuses sources changed since preparation, and compares the uploaded files against remote hash metadata. It never deletes source files. Only a completed upload-verification record is evidence that a remote backup exists.
Dataset contents and third-party model artifacts retain their original terms; this archive does not grant a new license over them. Do not execute untrusted pickle/checkpoint contents merely to inspect this collection.
Trained Checkpoint Supplement
This revision adds 1,399 trained EAR checkpoints (74.31 GB) stored under workspace/cache. These were missed by the original directory-name filter. Their serialized metadata contains model parameters, and experiment source code saves and restores them as trained model state. Downloaded dependency caches and non-parameter image/tensor caches remain excluded.
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