The field of Large Language Models (LLMs) is moving towards a deeper understanding of their internal workings and representations. Recent research has focused on mechanistic interpretability, which aims to probe the inner mechanisms of LLMs and understand how they develop internal structures that are functionally analogous to human understanding. This has led to the discovery of low-dimensional linear subspaces in the latent space of LLMs, where high-level semantic information is consistently represented. These findings have significant implications for improving alignment and detecting harmful content. Furthermore, the role of intentionality in knowledge representation is being explored, with research demonstrating that even simple language models can identify themes and concepts in text without extensive training or reasoning capabilities. Additionally, studies have shown that LMs can reflect human judgments of event plausibility, providing new insights into modal categorization. Noteworthy papers include: Mechanistic Indicators of Understanding in Large Language Models, which proposes a novel theoretical framework for thinking about machine understanding. Large Language Models Encode Semantics in Low-Dimensional Linear Subspaces, which demonstrates the potential of geometry-aware tools for detecting and mitigating harmful content. Is This Just Fantasy? Language Model Representations Reflect Human Judgments of Event Plausibility, which provides new insights into LM modal categorization using techniques from mechanistic interpretability.