The field of formal methods and verification is experiencing a significant shift with the integration of large language models (LLMs). Researchers are exploring the potential of LLMs to automate various tasks, such as autoformalization, assertion generation, and code reasoning. The overall direction of the field is towards leveraging LLMs to improve the efficiency and accuracy of formal verification and validation. Noteworthy papers include: AssertGen, which proposes a framework for generating SystemVerilog assertions using LLMs, achieving state-of-the-art results. PAT-Agent, which introduces an end-to-end framework for natural language autoformalization and formal model repair, demonstrating high verification success and efficiency. AssertFix, which presents a framework for automatic assertion fix using LLMs, showing noticeable improvements in fix rate and verification coverage. RagVerus, which synergizes retrieval-augmented generation with context-aware prompting to automate proof synthesis for multi-module repositories, achieving a 27% relative improvement on a challenging benchmark. Towards Verified Code Reasoning by LLMs, which describes a method to automatically validate the answers provided by a code reasoning agent, successfully validating the agent's reasoning on several examples.