The field of binary code analysis and circuit design is moving towards leveraging machine learning and graph neural networks to improve accuracy and efficiency. Researchers are exploring novel approaches to resolve indirect calls in binary code, predict IR drop in integrated circuits, and match target circuits in large-scale netlists. These innovative methods are achieving state-of-the-art results and have the potential to advance downstream applications in security and electronic design automation. Noteworthy papers include: Resolving Indirect Calls in Binary Code via Cross-Reference Augmented Graph Neural Networks, which introduces a novel approach for resolving indirect calls using graph neural networks, and WACA-UNet, which proposes a weakness-aware channel attention mechanism for static IR drop prediction. Additionally, Target Circuit Matching in Large-Scale Netlists using GNN-Based Region Prediction presents an efficient graph matching approach using graph neural networks, and POLARIS introduces a framework for mitigating power side-channel leakage using explainable artificial intelligence. SleepWalk exploits context switching and residual power for physical side-channel attacks, demonstrating a novel technique for cryptographic key recovery.