Datasets:

ClarusC64
/

boundary-masking-instability-benchmark-v0.1

scenario_id string	pressure float64	buffer_capacity float64	coupling_strength float64	intervention_lag float64	drift_gradient float64	boundary_distance float64	hidden_stress_load float64	structural_fragility float64	label_future_collapse int64
BM-0007	0.44	0.69	0.66	0.29	0.03	0.19	0.71	0.64	1
BM-0002	0.41	0.74	0.6	0.25	0.01	0.52	0.33	0.28	0
BM-0009	0.46	0.67	0.68	0.31	0.04	0.17	0.73	0.66	1
BM-0004	0.4	0.76	0.59	0.26	-0.01	0.56	0.31	0.26	0
BM-0001	0.42	0.72	0.63	0.27	0.02	0.48	0.34	0.29	0
BM-0006	0.45	0.7	0.65	0.28	0.02	0.22	0.69	0.6	1
BM-0003	0.39	0.75	0.58	0.24	-0.02	0.58	0.3	0.24	0
BM-0008	0.47	0.66	0.69	0.32	0.05	0.16	0.74	0.68	1
BM-0005	0.43	0.71	0.64	0.27	0.01	0.46	0.35	0.3	0
BM-0010	0.48	0.65	0.7	0.33	0.05	0.14	0.76	0.7	1

Boundary Masking Instability Benchmark v0.1 Overview

Many systems fail not because instability is visible but because instability is masked by apparently stable surface indicators.

This benchmark evaluates whether machine learning systems can detect collapse risk when instability boundaries are hidden behind misleading surface signals.

Surface indicators may appear stable while the system is already close to a failure boundary.

This occurs in many domains

financial liquidity collapse infrastructure cascade failures physiological deterioration control system instability

The benchmark tests whether models can detect boundary proximity rather than relying on surface stability signals.

Task

Binary classification.

Predict whether the system will collapse in the near future.

1 = future collapse 0 = stable system Dataset Structure

Each row represents a snapshot of a system state together with signals describing its proximity to instability boundaries.

Columns

scenario_id Unique scenario identifier.

pressure Current stress acting on the system.

buffer_capacity Remaining capacity to absorb disruption.

coupling_strength Interaction strength between system components.

intervention_lag Delay before corrective intervention becomes effective.

drift_gradient Directional signal indicating movement toward instability.

boundary_distance Observable distance from instability boundary.

hidden_stress_load Latent structural stress not visible in surface variables.

structural_fragility Underlying architectural vulnerability.

label_future_collapse Binary outcome label included only in the training dataset.

Tester rows do not include the label column.

Files

data/train.csv training dataset

data/tester.csv evaluation dataset without labels

scorer.py official scoring script

README.md dataset documentation

Evaluation

Primary metric

collapse recall

Secondary metrics

accuracy precision F1 score confusion matrix statistics

License

MIT

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