Spatio-temporal features extraction that measure the stabilty. The proposed method is based on a compression algorithm named Run Length Encoding. The workflow of the method is presented bellow.

Capability Schema Spec defines a shared semantic language for world model evaluation. Standardize capability definition, observation, and verification across models and benchmarks. Not a benchmark—a shared language. Define • Observe • Verify

A real-time license plate recognition pipeline utilizing a fine-tuned YOLO model for spatial detection and EasyOCR for text extraction. Features robust regex validation, character confusion remapping matrices and a majority-vote temporal stabilizaton buffer to eliminate frame jitter.

Built on peer-reviewed research accepted at IEEE DSA 2025: "Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring: Empirical Validation and Temporal Stability Analysis"

Reference implementation of the Capability Schema Specification. Proves that world model capabilities can be defined, observed, and verified in practice — with real checkpoints, real simulators, and real scores. Define • Observe • Verify • Deliver

Classical lane perception pipeline on KITTI evaluated under synthetic shadow and motion blur across temporal stability metrics.