The field of mechanistic interpretability of large language models is moving towards a deeper understanding of the internal workings of these models. Recent research has focused on developing methods to analyze and explain the behavior of large language models, including the identification of minimal conditions for behavioral self-awareness, the development of accelerated path patching methods for circuit discovery, and the creation of novel approaches for generating textual descriptions of data. Notably, the use of self-organizing maps to extract multiple refusal directions has shown promise in improving the safety and reliability of language models. Furthermore, the ability of language models to learn to explain their own computations has been demonstrated, offering a scalable complement to existing interpretability methods. Overall, the field is advancing rapidly, with a growing emphasis on developing innovative methods to uncover the underlying mechanisms of large language models. Noteworthy papers include: Minimal and Mechanistic Conditions for Behavioral Self-Awareness in LLMs, which characterizes the minimal conditions for self-awareness in LLMs. Training Language Models to Explain Their Own Computations, which demonstrates the ability of LMs to learn to explain their internal computations. SOM Directions are Better than One: Multi-Directional Refusal Suppression in Language Models, which proposes a novel method for extracting multiple refusal directions using self-organizing maps.