Accurate change detection in remote sensing imagery requires sophisticated multi-scale temporal feature integration and attention mechanisms. Current methods suffer from suboptimal multi-scale information utilization due to uniform attention deployment and insufficient feature discriminability from deterministic processing. We propose a novel framework addressing these limitations through two key innovations. First, an Adaptive Scale-Context Attention module with resolution-aware orchestration strategically applies spatial attention at higher scales for precise boundary delineation and channel attention at lower scales for semantic feature selection. Second, and most importantly, we introduce a stochastic attention refinement mechanism that revolutionizes attention-based change detection by learning adaptive modulation patterns through contextual pattern embedding. This stochastic framework employs posterior and prior distributions to model context-dependent enhancement patterns, applying learned contextual representations to dynamically calibrate attention scores and significantly improve feature discriminability beyond deterministic approaches. Our method processes bi-temporal images through dual-stream encoders, applies Adaptive Scale-Context Attention modules with stochastic enhancement across multiple scales, and reconstructs change maps through semantically-aware upsampling. Extensive experiments on four benchmark datasets demonstrate superior performance: we achieve 93.82% F1-score on DSIFN-CD, 94.37% F1-score on WHU building dataset, 91.85% F1-score on LEVIR-CD, and 76.19% F1-score on MSRS-CD, while maintaining computational efficiency with only 6.8M parameters. Comprehensive ablation studies validate the effectiveness of both resolution-aware attention orchestration and stochastic enhancement, establishing a new paradigm for efficient and accurate remote sensing change detection.