Context-Aware Temporal Synthesis for Scene, Entity, and Event Inference from Silent Image

Mahmoud Rokaya^1*Dalia I. Hemdan²Mohammed A. Alzain¹El-Sayed Atlam³

• ¹Taif University, Ta'if, Saudi Arabia
• ²Taif University College of Science, Taif, Saudi Arabia
• ³Taibah University - Yanbu Campus, Yanbu, Saudi Arabia

A central limitation of existing temporal image analysis and video understanding models lies in their reliance on explicit motion cues, dense supervision, or auxiliary modalities, which constrains their ability to infer latent temporal structure, evolving semantic states, and long-range dependencies from silent image sequences. This limitation becomes critical in settings where temporal meaning emerges implicitly from stable visual representations rather than explicit frame-to-frame dynamics. In this work, we propose CATS (Context-Aware Temporal Synthesis), a mathematically grounded and interpretable framework for temporal reasoning that operates directly on silent image sequences and general temporal signals. CATS integrates curvature-aware temporal alignment, symmetry-enforced attention, slot-based nonlinear recurrence, and semantic memory fusion to model temporal coherence under noise, partial observability, and unordered inputs. Unlike conventional spatiotemporal architectures, CATS does not assume fixed temporal ordering or handcrafted motion representations, enabling robust temporal abstraction across heterogeneous domains. We validate the proposed framework primarily on silent egocentric video understanding tasks and further assess its robustness and generality through controlled cross-domain temporal stress tests, including stochastic diffusion modeling (ANDI), reinforcement-based temporal alignment, and cyber–physical time-series forecasting. In particular, we demonstrate that the same architecture trained on visual data transfers effectively to the Anomalous Diffusion (ANDI) benchmark, where CATS organizes particle trajectories in latent time and separates diffusion regimes without architectural modification. This cross-domain consistency confirms that CATS captures intrinsic temporal structure rather than dataset-specific cues. Across visual and non-visual tasks, CATS consistently outperforms competitive baselines, achieving up to 15% relative improvement in mAP and F1-score on egocentric video understanding, stable regime separation and accuracy gains on anomalous diffusion dynamics, and lower forecasting error in cyber–physical time-series prediction, while maintaining stable convergence under CPU-only constraints and providing interpretable attention and memory dynamics. By unifying temporal alignment, memory, and reasoning within a principled mathematical framework, CATS establishes a domain-agnostic approach to temporal understanding, advancing the state of the art in interpretable temporal reasoning for computer vision and beyond.