The recent developments in the research area have seen a significant shift towards integrating interpretability and symbolic reasoning into machine learning models. This trend is driven by the need for more transparent and explainable AI systems, particularly in complex environments where traditional deep learning models fall short. The focus has been on creating models that can bridge the gap between high-dimensional sensory inputs and abstract reasoning, often through object-centric representations and neuro-symbolic frameworks. These approaches aim to enhance the agent's ability to perform long-horizon planning and adapt to unexpected changes in their environment. Notably, the incorporation of abductive reasoning and knowledge graphs has shown promise in improving the logicality and efficiency of AI solutions, especially in tasks requiring visual reasoning and complex decision-making. The advancements highlight a move towards more robust and versatile AI systems that can operate effectively in diverse and dynamic settings.