The field of software engineering is witnessing significant advancements with the integration of large language models (LLMs). Recent developments indicate a shift towards leveraging LLMs for tasks such as automated program repair, code generation, and software security verification. These models have demonstrated impressive capabilities in understanding and generating code, as well as reasoning about complex software systems. Notably, researchers are exploring the use of LLMs in multi-agent systems, where specialized agents collaborate to solve complex problems. This approach has shown promise in areas like SoC security verification and code refactoring. Furthermore, the development of adaptive request scheduling strategies for CodeLLM serving is improving the efficiency and performance of these models in resource-constrained environments. Overall, the field is moving towards more sophisticated and automated software engineering workflows, with LLMs at the forefront. Noteworthy papers include: DeepRTL2, which presents a versatile LLM for RTL-related tasks, achieving state-of-the-art performance across various tasks. Seeing is Fixing introduces GUIRepair, a cross-modal reasoning approach for resolving multimodal issue scenarios, demonstrating significant effectiveness on the SWE-bench M benchmark. SemAgent proposes a novel workflow-based procedure for automated program repair, leveraging issue, code, and execution semantics to generate patches that are complete and general.