--- 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