The field of smart contract research is moving towards more robust and reliable methods for vulnerability detection and type safety. Recent developments have focused on leveraging large language models and ensemble learning techniques to improve the accuracy and generalization of vulnerability detection frameworks. Additionally, there is a growing interest in developing type-aware fuzzing frameworks and semantic approaches to ensure type safety in smart contracts. These advancements aim to address the limitations of existing methods and provide more effective solutions for securing smart contracts. Noteworthy papers include: LLMBugScanner, which combines domain knowledge adaptation with ensemble reasoning to improve robustness and generalization, and Belobog, a type-aware fuzzing framework for Move smart contracts that achieves high detection rates for critical and major vulnerabilities. Retrieval-Augmented Few-Shot Prompting Versus Fine-Tuning for Code Vulnerability Detection also presents a promising approach, using retrieval-augmented prompting to improve few-shot performance in code vulnerability detection. Typing Fallback Functions presents a semantic approach to type safe smart contracts, ensuring type safety of code that uses statically untypable language constructs.