The field of automated program repair and code translation is rapidly advancing with the use of large language models (LLMs). Recent research has focused on improving the capabilities of LLMs in fixing software defects and translating code between programming languages. One of the key areas of improvement is in the development of novel approaches that leverage the strengths of LLMs in different programming languages. For instance, cross-language translation and multi-agent refinement techniques have been shown to significantly enhance repair effectiveness, particularly for underrepresented languages. Additionally, the use of instrumentation and program state alignment has been proposed to improve the accuracy of code translation. Another area of research is in the development of new benchmarks and datasets to evaluate the performance of LLMs in code repair and translation tasks. Noteworthy papers include: Unlocking LLM Repair Capabilities, which introduces a novel cross-language program repair approach that leverages LLMs' differential proficiency across languages. LLMigrate, which presents an LLM-based C-to-Rust translation tool that splits modules into discrete functions and uses static analysis to retain necessary context. Enhancing LLMs in Long Code Translation, which proposes an approach that integrates instrumentation to capture and align program states during translation, significantly improving translation accuracy.