The field of machine learning is increasingly focused on developing methods for machine unlearning, which involves removing specific data from trained models. Recent research has made significant progress in this area, with a number of innovative approaches being proposed. One key direction is the development of methods for efficient and effective machine unlearning, such as model splitting and core sample selection. Another important area of research is the evaluation of machine unlearning methods, with a number of frameworks and metrics being proposed to assess their effectiveness. Additionally, there is a growing recognition of the need to consider fairness and privacy in machine unlearning, with methods being developed to ensure that unlearning does not introduce bias or compromise user privacy. Notably, papers such as 'Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation' and 'Enabling Group Fairness in Graph Unlearning via Bi-level Debiasing' have made significant contributions to the field, proposing novel methods for robust machine unlearning and fair graph unlearning.