The fields of fuzzy rule mining, neuro-symbolic systems, data visualization, multimodal AI, and formal reasoning are experiencing significant developments, driven by the need for more robust, interpretable, and deployable models. A common theme among these areas is the integration of multiple forms of input, such as text, images, and other modalities, to improve understanding, prediction, and decision-making. Notably, researchers are exploring new approaches to fuzzy implicative rules, evidence fusion, and rough sets, leading to more accurate and reliable results. The development of categorical foundations for rough sets and the introduction of possibilistic neuro-symbolic approaches are also advancing the field. In data visualization, recent research has explored the capabilities and limitations of large language models and vision language models in tasks such as chart question answering and graphical perception. New benchmarks and evaluation methods are being developed to test the performance of these models in real-world scenarios. The field of multimodal AI is moving towards a more deployment-centric approach, incorporating deployment constraints early in the development process to reduce the likelihood of undeployable solutions. Researchers are exploring the use of foundation models, such as large language models and multimodal models, to support software engineering activities, including coding and testing. Additionally, there is a growing interest in applying multimodal AI to real-world problems, such as intelligent agricultural decision-making and creating inclusive art environments. The development of multimodal large language models (MLLMs) is also progressing, with a focus on improving their reasoning capabilities, particularly in visual text grounding, ordinal understanding, and multimodal explanation. The field of formal reasoning is experiencing a significant shift towards the integration of neuro-symbolic approaches, which combine the strengths of neural networks and symbolic methods to achieve improved results. This trend is driven by the potential of neuro-symbolic methods to generate high-quality axiomatic oracles, conjecture useful lemmas, and enhance automated theorem proving. Some noteworthy papers include those introducing unified approaches to fuzzy implicative rules, possibilistic neuro-symbolic approaches, and novel methods for visual grounding and multimodal explanation. Overall, these developments are advancing the field of multimodal AI and neuro-symbolic systems, enabling the creation of more powerful, explainable, and deployable models.