The recent developments in the research area have shown a significant shift towards integrating advanced machine learning techniques with traditional computational methods to tackle complex problems. A notable trend is the use of neural networks and reinforcement learning to enhance the efficiency and effectiveness of decoding algorithms, particularly in error-correction coding and optimization problems. For instance, the incorporation of neural weights in decoding processes for SC-LDPC codes and the use of RL for solving MILP problems demonstrate a promising direction in leveraging AI for improving traditional computational techniques. Additionally, there is a growing interest in applying RL to solve combinatorial puzzles and optimization tasks, as seen in the development of RL algorithms for solving Rubik's Cube and higher-dimensional sliding puzzles. These advancements not only showcase the potential of AI in enhancing computational efficiency but also open new avenues for research in integrating AI with traditional methods. Notably, the use of graph neural networks in planning and optimization problems, as well as the development of auto-encoders for short linear block codes, highlight the versatility and robustness of AI techniques in various domains. Overall, the field is moving towards a more integrated approach where AI and traditional computational methods synergize to solve complex, real-world problems.