A Structured Question-Driven Framework for Post-Training Model Analysis

This work presents a framework for analyzing trained AI models through a structured set of guiding questions: What, Where, How, When, and Why. Instead of treating a model as a black box, the approach emphasizes analyzing parameter offsets, relational structures, and temporal dynamics to uncover deeper insights into model behavior. The key contributions include: What: Examining properties of the trained model and the dataset that shaped them. Where: Studying parameter offsets within the model to identify how far testing data diverges from training representations. How: Mapping relationships among offsets and between model components, revealing emergent structures and dependencies. When: Observing how internal representations and relationships evolve with new data, uncovering thresholds of change. Why: Tracing causality across learned relations to explain why specific predictions or behaviors emerge. This analysis phase is proposed as a pathway for designing next-generation models — more accurate, efficient, and interpretable — by guiding parameter tuning, architectural adjustments (adding/removing nodes), and simulation-based exploration of model variants. By reframing AI models as dynamic structures to be interrogated through these fundamental questions, the framework advances both interpretability and model improvement strategies, offering a methodology applicable across architectures (neural networks, graph models, probabilistic learners).