(untitled) (Journal of Advanced Artificial Intelligence Research, 2026)
Long video understanding remains a central challenge in artificial intelligence due to the complexity of temporal dependencies, the volume of redundant visual data, and the opacity of deep learning models. This paper proposes a framework that integrates dynamic motion tokens and temporal causal discovery to produce interpretable analyses of extended video sequences. Dynamic motion tokens are learned representations that condense local motion patterns into discrete, semantically meaningful units while preserving temporal ordering. Temporal causal discovery then identifies directed causal relationships among these tokens across time, yielding a graph-based explanation of event progression. The system is designed to support explainability by design rather than post-hoc interpretation. The paper examines the structural trade-offs involved in token granularity, causal graph sparsity, and computational efficiency. It also discusses architectural choices for large-scale deployment, including distributed processing pipelines and memory-bounded inference. Robustness considerations are addressed, particularly concerning distribution shift and adversarial perturbations. Fairness and policy implications are explored in the context of video surveillance and content moderation applications. The framework is contrasted with existing methods such as SlowFast, VideoMAE, and transformer-based architectures, highlighting the benefits of causal explainability in high-stakes domains. The work concludes with a forward-looking discussion of governance and sustainability, arguing that transparent causal models are essential for accountable video analytics in societal infrastructure. This research contributes to the growing intersection of explainable artificial intelligence and temporal reasoning, offering a principled pathway toward trustworthy long video understanding.