The field of natural language processing is moving towards developing more interpretable and transparent language models. Recent research has focused on understanding the internal mechanisms of large language models, including the role of attention heads and the structure of relation decoding linear operators. Studies have shown that attention heads can specialize in specific semantic or visual attributes, and that editing a small percentage of these heads can reliably suppress or enhance targeted concepts in the model output. Additionally, research has demonstrated that task-specific training can induce highly interpretable, minimal circuits in attention-only transformers. Noteworthy papers include: Head Pursuit, which introduces a method for analyzing and editing attention heads in multimodal transformers. PAHQ, which proposes a novel approach for accelerating automated circuit discovery through mixed-precision inference optimization. Emergence of Minimal Circuits, which demonstrates that task-specific training can induce highly interpreutable, minimal circuits in attention-only transformers. LLMs Process Lists With General Filter Heads, which investigates the mechanisms underlying list-processing tasks in large language models and finds that they have learned to encode a compact, causal representation of a general filtering operation.