(untitled) (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The proliferation of long-form video content across surveillance, entertainment, education, and scientific domains has elevated the demand for robust and interpretable video question answering systems that can operate over extended temporal horizons. This paper presents a system-level investigation of graph-enhanced temporal dynamics modeling for explainable long-sequence video question answering. By fusing structured relational reasoning with multi-scale temporal encoding, the proposed framework addresses the critical tension between predictive accuracy and interpretability that pervades contemporary deep learning architectures. We examine the architectural trade-offs involved in integrating graph neural modules with hierarchical temporal transformers, paying particular attention to the decomposition of event structure, causal dependencies, and narrative coherence over hundreds of frames. The discussion extends to infrastructure scalability, deployment constraints on edge and cloud platforms, and the governance challenges surrounding fairness, accountability, and sustainability in large-scale video understanding systems. Through a synthesis of cross-domain insights from computer vision, natural language processing, and socio-technical infrastructures, we argue that graph-enhanced temporal dynamics models can serve as a principled foundation for transparent, auditable, and energy-efficient video reasoning. The paper concludes with policy-oriented recommendations for the responsible deployment of such systems in high-stakes applications, including legal evidence analysis and automated content moderation.