The field of natural language processing is witnessing significant developments in long-context language models and cross-lingual understanding. Researchers are exploring the potential of these models to handle extended contexts, reason over multilingual texts, and perform tasks such as translation, question answering, and summarization. A key focus area is the evaluation of these models, with new benchmarks and methodologies being proposed to assess their performance in various tasks. Additionally, there is a growing interest in applying these models to real-world problems, such as translating programming languages and evaluating the judgment performance of large language models. Noteworthy papers in this area include those that propose innovative approaches to cross-lingual context retrieval, long-context evaluation, and multilingual summarization. For instance, the LITERA model achieves unprecedented accuracy in Latin-to-English translation, while the MLRBench benchmark provides a synthetic evaluation platform for multilingual long-context reasoning. The Efficient MAP Estimation of LLM Judgment Performance with Prior Transfer paper presents a principled framework for estimating the accuracy of large language model ensembles.