The fields of reinforcement learning, computational complexity, physics-informed neural networks, control systems, and optimization are rapidly evolving, with a focus on developing more efficient and scalable algorithms for complex problems. Recent research has explored the use of novel frameworks, such as pseudo-MDPs, to optimize solutions for specific classes of problems, including those related to blockchain security. The development of benchmarks, such as BuilderBench and PuzzlePlex, has also been significant in evaluating the performance of foundation models and generalist agents in complex, dynamic environments. Notable papers include To Distill or Decide?, PuzzlePlex, and Pseudo-MDPs, which investigate the algorithmic trade-off between privileged expert distillation and standard RL, introduce a benchmark to assess the reasoning and planning capabilities of foundation models, and propose a novel framework for efficiently optimizing last revealer seed manipulations in blockchains, respectively. The integration of physical laws and constraints into neural network architectures has enabled more accurate and efficient solutions to forward and inverse problems in physics-informed neural networks. The development of novel control strategies, including model predictive control and reinforcement learning, has been applied to complex systems such as roll-to-roll manufacturing and turbulent flows. The field of control and reinforcement learning is rapidly evolving, with a focus on developing innovative solutions for complex systems. Recent research has explored the integration of deep reinforcement learning with model predictive control, bounded extremum seeking, and other techniques to improve robustness and adaptability in dynamic environments. Notable advancements include the development of hybrid controllers that combine the strengths of different approaches. The field of reinforcement learning is moving towards more complex and realistic scenarios, with a focus on goal-conditioned reinforcement learning. Recent research has explored various aspects of this field, including new goal representation methods, improved algorithms, and applications to real-world problems. The field of optimization and decision-making is moving towards addressing the challenges of uncertainty and complexity in real-world problems. Researchers are developing new frameworks and tools to understand and navigate the hyperparameter loss surface. The field of reinforcement learning and bandit algorithms is rapidly evolving, with a focus on developing more efficient, robust, and interpretable methods. Recent research has explored the use of shift-aware upper confidence bound algorithms, adaptive spectral-based linear approaches, and martingale-driven Fisher prompting for sequential test-time adaptation. The field of reinforcement learning is moving towards addressing complex systems and multi-agent coordination challenges. Researchers are exploring innovative approaches to tackle problems such as the traveling salesman problem, order dispatching on ride-sharing platforms, and distributed area coverage with high altitude balloons. A key trend is the development of decentralized and centralized training methods that enable effective coordination and decision-making in dynamic environments.