Datasets:
language:
- en
license: mit
task_categories:
- tabular-classification
task_ids:
- tabular-multi-class-classification
annotations_creators:
- synthetic
source_datasets:
- generated
tags:
- ai-safety
- instability-detection
- boundary-detection
- collapse-prediction
- clarus-benchmark
- instability-benchmarks
size_categories:
- n<1K
pretty_name: Boundary Masking Instability Benchmark v0.1
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