The field of combinatorial optimization and machine learning is rapidly evolving, with a focus on developing innovative methods for solving complex problems. Recent research has explored the use of large language models (LLMs) with reasoning to generate search heuristics for combinatorial design problems, resulting in the solution of long-standing open instances. Additionally, there is a growing interest in developing efficient instruction evolving methods, such as Tag-Evol, which utilizes diverse and specific knowledge tags to achieve controlled evolution. The development of standardized evaluation frameworks, such as EVA-MILP, is also crucial for assessing the fidelity and utility of synthetic Mixed-Integer Linear Programming (MILP) instances. Furthermore, the integration of instance generation with solver design is becoming increasingly important, with frameworks like EALG providing a seamless pipeline for generating challenging instances and synthesizing adaptive heuristic algorithms. Noteworthy papers include Using Reasoning Models to Generate Search Heuristics, which successfully solved open instances of combinatorial design problems using LLMs with reasoning, and Tag-Evol, which demonstrated significant improvements in instruction evolving. EALG is also noteworthy for its ability to generate significantly harder instances than current benchmarks and synthesize effective solvers.