The field of large language models (LLMs) is rapidly evolving, with a focus on evaluating and improving their performance in various domains. Recent research has emphasized the need for rigorous evaluation frameworks to assess the capabilities and limitations of LLMs. Accordingly, several new benchmarks and evaluation methods have been proposed to test LLMs in areas such as legal reasoning, educational applications, and game playing. These evaluations have revealed significant disparities in the performance of LLMs across different tasks and domains, highlighting the need for more targeted and specialized training approaches. Furthermore, researchers have explored the use of LLMs as evaluators for natural language generation tasks, demonstrating their potential as general-purpose evaluators. Noteworthy papers in this area include: EduEval, which introduces a comprehensive hierarchical benchmark for evaluating LLMs in Chinese K-12 education, revealing inconsistent results in creative content generation. LexGenius, an expert-level Chinese legal benchmark for evaluating legal general intelligence in LLMs, finding significant disparities across legal intelligence abilities for LLMs. LLM CHESS, an evaluation framework designed to probe the generalization of reasoning and instruction-following abilities in LLMs through extended agentic interaction in the domain of chess, showing a clear separation between reasoning and non-reasoning models.