Evaluation & Benchmarks

Run reproducible evaluation benchmarks by using a deterministic harness that enforces seeding and supports GPU/CPU loops. It evaluates multimodal models across seven cognitive layers, outputting standardized metrics like accuracy, F1, and NAB score.

Cross-modality fusion analysis supports accuracy, F1, AUROC, and NAB score metrics. It provides per-class breakdowns and confusion matrices to link rich metrics to actionable insights across standardized cognitive layers.

Yes, PyTorch and NumPy are required dependencies for generating benchmark reports. The harness outputs JSON and CSV reports, offering baseline comparison utilities and delta analysis to monitor progress for regression testing.

Yes, the evaluation harness supports synthetic data for rapid benchmark iteration. It includes self-tests and deterministic evaluation loops, making it suitable for both development and production benchmarking environments.

Compare baseline benchmarks over time using built-in baseline comparison utilities and delta analysis. This approach monitors progress by generating JSON and CSV reports that track evaluation metrics across standardized cognitive layers.

Deterministic seeding is enforced to ensure reproducible evaluation benchmarks. It guarantees consistent metric calculations, like F1 and NAB scores, across cognitive layers during both development and production benchmarking runs.