The field of artificial intelligence is witnessing significant developments in multimodal learning, with a focus on improving the performance of vision-language models on various perception tasks. Researchers are exploring the concept of task transferability, which enables the evaluation of how fine-tuning a model on one task affects its performance on other tasks. This has led to the discovery of patterns of positive and negative transfer, allowing for more efficient training and data selection. Additionally, there is a growing interest in developing more effective and efficient methods for cybersecurity, particularly in the context of cyber-physical systems. Novel approaches are being proposed to overcome the limitations of traditional vulnerability discovery techniques, such as the use of model checking and concolic execution techniques to automatically verify security properties of a program's stack memory. Noteworthy papers in this area include: The paper on Understanding Task Transfer in Vision-Language Models, which introduces a systematic study of task transferability and proposes a metric to quantify the effects of finetuning on one task on the performance of others. The paper on BASICS, which presents a novel approach for buffer overflow mitigation using model checking and concolic execution techniques.