The field of AI research is moving towards a greater emphasis on alignment and interpretability, with a focus on developing methods to ensure that AI systems behave in a manner consistent with human values and goals. This is reflected in the growing body of research on affective taxis, value neurons, and emergent risk awareness, which aims to provide a more nuanced understanding of how AI systems make decisions and how they can be aligned with human objectives. Notable papers in this area include: Understanding How Value Neurons Shape the Generation of Specified Values in LLMs, which introduces a mechanistic interpretability framework for understanding how values are encoded in neural architectures. Fusion Steering, which presents an activation steering methodology that improves factual accuracy in large language models for question-answering tasks. Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time, which proposes a framework for aligning large language models with humans using satisficing strategies. These papers demonstrate the innovative and advancing work being done in the field, and highlight the potential for significant breakthroughs in AI alignment and interpretability.