Update README.md

README.md

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----
-license: mit
----
+---
+language: en
+license: mit
+task_categories:
+- text-classification
+tags:
+- stability-analysis
+- adversarial-benchmark
+- system-dynamics
+- collapse-detection
+size_categories:
+- 1K<n<10K
+pretty_name: CASSES State-Space Collapse Benchmark
+---
+CASSES — Collapse Analysis in State-Space Evaluation Suite
+Overview
+
+CASSES is a diagnostic benchmark designed to test whether machine learning systems can detect instability and collapse in dynamic systems.
+
+Most AI benchmarks evaluate models on tasks such as classification, language generation, or reasoning over static data.
+CASSES evaluates a different capability:
+
+state-space stability understanding.
+
+The benchmark tests whether a model can identify when a system is approaching a collapse boundary using signals derived from system dynamics.
+
+The dataset is intentionally adversarial.
+Several trap structures are included to prevent simple heuristics from solving the task.
+
+The System Model
+
+Each row represents a simulated dynamic system.
+
+The system is defined by four interacting structural variables:
+
+pressure
+buffer capacity
+intervention lag
+system coupling
+
+These variables interact non-linearly to determine the stability margin of the system.
+
+From these dynamics the dataset derives observable signals including:
+
+boundary_distance
+drift_gradient
+drift_acceleration
+recovery_feasibility
+regime_competition_ratio
+
+These signals describe where the system sits in stability space and how it is moving through that space.
+
+What Models Must Predict
+
+The core prediction task is:
+
+true_label
+
+Where:
+
+0 = system remains stable
+1 = system collapses
+
+Models must infer collapse risk from observed trajectories and derived geometry signals.
+
+Dataset Structure
+
+The dataset contains two splits.
+
+train.csv
+tester.csv
+
+The train split provides examples for model development.
+
+The tester split is used for evaluation and includes adversarial trap families designed to test robustness.
+
+Each row includes:
+
+system observations across time
+derived stability geometry
+intervention counterfactuals
+difficulty labels
+trap annotations
+
+Trap Families
+
+The dataset contains several adversarial trap types.
+
+These prevent simple threshold heuristics from solving the task.
+
+False Stability
+
+Observed signals appear stable while the underlying system state is unstable.
+
+Models must detect hidden instability.
+
+Boundary Masking
+
+Collapse occurs even though the system appears distant from the instability boundary.
+
+This tests robustness to misleading boundary signals.
+
+Trajectory Aliasing
+
+Different trajectories produce similar short-term observations but diverge later.
+
+Models must infer the correct long-term trajectory.
+
+Temporal Alias
+
+Temporal patterns appear stable over short windows but hide acceleration toward collapse.
+
+Intervention Decoy
+
+Counterfactual interventions appear stabilizing but actually increase collapse risk.
+
+Counterfactual Intervention Evaluation
+
+Some rows include simulated interventions.
+
+Fields include:
+
+intervention_action
+intervention_magnitude
+boundary_distance_before
+boundary_distance_after
+intervention_effect_direction
+
+These rows test whether models understand how interventions change system stability.
+
+Pair Evaluation
+
+Certain rows appear in paired form.
+
+Each pair contains:
+
+safe_pair
+unstable_pair
+
+The trajectories appear similar but diverge in stability outcome.
+
+Models must identify which trajectory leads to collapse.
+
+Difficulty Levels
+
+Each scenario is assigned a difficulty level.
+
+easy
+medium
+hard
+
+Difficulty reflects the clarity of the collapse signal and the degree of adversarial masking present.
+
+Evaluation
+
+Evaluation is performed using the provided scorer.
+
+Metrics include:
+
+accuracy
+precision
+recall
+F1 score
+
+Additional diagnostic metrics measure performance on specific trap families and system dynamics features.
+
+The primary composite metric is:
+
+CASSES score
+
+This score summarizes model performance across collapse detection, adversarial traps, and counterfactual reasoning.
+
+Baseline Results
+
+A simple heuristic baseline achieves approximately:
+
+CASSES score ≈ 0.64
+
+This indicates the benchmark cannot be solved with trivial rules and requires meaningful reasoning about system dynamics.
+
+Files
+
+train.csv — training scenarios
+tester.csv — evaluation scenarios
+generator.py — dataset generator
+prediction_baseline.py — reference baseline
+scorer.py — official evaluation script
+
+Intended Use
+
+CASSES is intended for:
+
+evaluation of machine learning models
+research on stability reasoning
+development of system-dynamics-aware AI
+
+The benchmark focuses on instability detection in dynamic systems rather than traditional static classification tasks.
+
+License
+
+MIT License