Datasets:

ClarusC64
/

eval-trap-stability-manifold-benchmark-v0.2

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 1 new columns ({'prediction'}) and 7 missing columns ({'content_score', 'coupling', 'buffer', 'lag', 'pressure', 'regime_hint', 'label_stable'}).

This happened while the csv dataset builder was generating data using

hf://datasets/ClarusC64/eval-trap-stability-manifold-benchmark-v0.2/prediction_templates/train_predictions_template.csv (at revision e27b4a8cfa605ce81f21d3dfbd7a8d38a871777d), [/tmp/hf-datasets-cache/medium/datasets/33468884134157-config-parquet-and-info-ClarusC64-eval-trap-stabi-84d203cc/hub/datasets--ClarusC64--eval-trap-stability-manifold-benchmark-v0.2/snapshots/e27b4a8cfa605ce81f21d3dfbd7a8d38a871777d/data/train.csv (origin=hf://datasets/ClarusC64/eval-trap-stability-manifold-benchmark-v0.2@e27b4a8cfa605ce81f21d3dfbd7a8d38a871777d/data/train.csv), /tmp/hf-datasets-cache/medium/datasets/33468884134157-config-parquet-and-info-ClarusC64-eval-trap-stabi-84d203cc/hub/datasets--ClarusC64--eval-trap-stability-manifold-benchmark-v0.2/snapshots/e27b4a8cfa605ce81f21d3dfbd7a8d38a871777d/prediction_templates/train_predictions_template.csv (origin=hf://datasets/ClarusC64/eval-trap-stability-manifold-benchmark-v0.2@e27b4a8cfa605ce81f21d3dfbd7a8d38a871777d/prediction_templates/train_predictions_template.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/python3.12/site-packages/datasets/builder.py", line 1890, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 760, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              scenario_id: string
              prediction: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 503
              to
              {'scenario_id': Value('string'), 'pressure': Value('float64'), 'buffer': Value('float64'), 'lag': Value('float64'), 'coupling': Value('float64'), 'content_score': Value('float64'), 'regime_hint': Value('string'), 'label_stable': Value('int64')}
              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 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1892, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              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 1 new columns ({'prediction'}) and 7 missing columns ({'content_score', 'coupling', 'buffer', 'lag', 'pressure', 'regime_hint', 'label_stable'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/ClarusC64/eval-trap-stability-manifold-benchmark-v0.2/prediction_templates/train_predictions_template.csv (at revision e27b4a8cfa605ce81f21d3dfbd7a8d38a871777d), [/tmp/hf-datasets-cache/medium/datasets/33468884134157-config-parquet-and-info-ClarusC64-eval-trap-stabi-84d203cc/hub/datasets--ClarusC64--eval-trap-stability-manifold-benchmark-v0.2/snapshots/e27b4a8cfa605ce81f21d3dfbd7a8d38a871777d/data/train.csv (origin=hf://datasets/ClarusC64/eval-trap-stability-manifold-benchmark-v0.2@e27b4a8cfa605ce81f21d3dfbd7a8d38a871777d/data/train.csv), /tmp/hf-datasets-cache/medium/datasets/33468884134157-config-parquet-and-info-ClarusC64-eval-trap-stabi-84d203cc/hub/datasets--ClarusC64--eval-trap-stability-manifold-benchmark-v0.2/snapshots/e27b4a8cfa605ce81f21d3dfbd7a8d38a871777d/prediction_templates/train_predictions_template.csv (origin=hf://datasets/ClarusC64/eval-trap-stability-manifold-benchmark-v0.2@e27b4a8cfa605ce81f21d3dfbd7a8d38a871777d/prediction_templates/train_predictions_template.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)

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.

scenario_id string	pressure float64	buffer float64	lag float64	coupling float64	content_score float64	regime_hint string	label_stable int64
tr_0001	0.3	0.72	0.1	0.35	0.82	baseline	1
tr_0002	0.44	0.68	0.16	0.42	0.86	baseline	1
tr_0003	0.58	0.63	0.18	0.55	0.89	baseline	1
tr_0004	0.62	0.59	0.22	0.61	0.91	coupling	0
tr_0005	0.36	0.7	0.11	0.38	0.84	baseline	1
tr_0006	0.52	0.61	0.24	0.49	0.88	lag	1
tr_0007	0.65	0.57	0.28	0.64	0.93	coupling	0
tr_0008	0.41	0.66	0.2	0.46	0.87	lag	1
tr_0009	0.55	0.6	0.26	0.58	0.9	lag	1
tr_0010	0.33	0.74	0.09	0.31	0.81	baseline	1
tr_0011	0.48	0.64	0.17	0.44	0.86	baseline	1
tr_0012	0.71	0.56	0.23	0.69	0.95	coupling	0
tr_0001	null	null	null	null	null	null	null
tr_0002	null	null	null	null	null	null	null
tr_0003	null	null	null	null	null	null	null
tr_0004	null	null	null	null	null	null	null
tr_0005	null	null	null	null	null	null	null
tr_0006	null	null	null	null	null	null	null
tr_0007	null	null	null	null	null	null	null
tr_0008	null	null	null	null	null	null	null
tr_0009	null	null	null	null	null	null	null
tr_0010	null	null	null	null	null	null	null
tr_0011	null	null	null	null	null	null	null
tr_0012	null	null	null	null	null	null	null

Eval Trap Stability Manifold Benchmark v0.2

This repository provides a synthetic benchmark for testing whether models can distinguish between content confidence and system viability.

The benchmark is built to expose the evaluation trap:

A model assigns high confidence to a proposed configuration even though the system executing that configuration is mathematically unstable.

Core idea

Most predictive systems optimize for content accuracy.

This benchmark tests something else:

Can the model detect whether the proposed system state lies inside or outside a stability manifold?

Stability manifold

The benchmark defines three competing instability surfaces:

Baseline surface
S1 = buffer - (pressure * coupling) - (k * lag)
Coupling surface
S2 = buffer - (pressure * coupling^2) - (k * lag)
Lag surface
S3 = buffer - (pressure * coupling) - (k * lag^2)

The collapse margin is:

min(S1, S2, S3)

Label rule:

label_stable = 1 if min(S1, S2, S3) >= 0
label_stable = 0 otherwise

Why multiple surfaces

A single surface is easy to memorize.

A manifold is harder.

This benchmark forces the model to reason about:

interacting variables
non-linear collapse geometry
regime switching
boundary sensitivity

Dataset splits

train

Examples spanning all three instability regimes.

in_domain_test

Examples drawn from the same general regime distribution as training.

distribution_shift

Examples with shifted pressure, lag, and coupling ranges.

boundary_trap

Examples constructed near the stability seam to expose false rescue behaviour.

Metrics

Primary metric

false_rescue_rate

This measures how often the model predicts stability in high-confidence cases where the manifold indicates collapse.

Secondary metric

boundary_error_rate

This measures performance near the seam where |min(S1, S2, S3)| <= boundary_eps.

Additional metrics

accuracy
surface_distance_mean

Diagnostics

collapse_margin_distribution
confusion_matrix
active_surface_counts

Files

data/train.csv
data/in_domain_test.csv
data/distribution_shift.csv
data/boundary_trap.csv
prediction_templates/train_predictions_template.csv
prediction_templates/in_domain_test_predictions_template.csv
prediction_templates/distribution_shift_predictions_template.csv
prediction_templates/boundary_trap_predictions_template.csv
baseline/generate_baseline_predictions.py
scorer.py
stability_visualizer.py
dataset_schema.json
benchmark_spec.json

Prediction contract

Prediction files must contain:

scenario_id
prediction

Where:

prediction = 1 means stable
prediction = 0 means unstable

Rows are aligned by scenario_id.

Prediction templates

The repository includes ready-to-fill prediction templates in:

prediction_templates/

These templates follow the scorer contract exactly.

Baseline model

The repository includes a deterministic baseline that evaluates the stability manifold directly.

Generate predictions with:

python baseline/generate_baseline_predictions.py

This creates prediction files in:

baseline_predictions/

Scoring

Score a prediction file with:

python scorer.py predictions.csv data/boundary_trap.csv

Visualization

The repository includes a 2D projection visualizer:

stability_visualizer.py

Run it with:

python stability_visualizer.py --pred predictions.csv --truth data/boundary_trap.csv

The plot highlights:

stable region
collapse region
near-boundary region
false rescue
false collapse

Why this benchmark matters

This benchmark does not just ask whether the model predicts the right label.

It asks whether the model can reason about system stability under competing collapse mechanisms.

That makes it useful for thinking about:

ICU deterioration
infrastructure stress
financial cascades
model-based safety systems
any domain where local correctness can still produce global failure

Notes

The only material correction from the earlier version was the malformed row in train.csv.

Everything else above is now aligned:

schema benchmark contract scorer visualizer prediction templates baseline generator README How to run

Generate baseline predictions:

python baseline/generate_baseline_predictions.py

Score a split:

python scorer.py baseline_predictions/boundary_trap_baseline_predictions.csv data/boundary_trap.csv

Visualize it:

python stability_visualizer.py --pred baseline_predictions/boundary_trap_baseline_predictions.csv -

License

MIT

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