The field of large language models (LLMs) is moving towards improving their efficiency and ability to reason causally. Researchers are exploring techniques to optimize self-consistency, a widely used test-time inference technique, to achieve state-of-the-art sample efficiency. Additionally, there is a growing interest in integrating LLMs with other planning methods, such as Hierarchical Task Network (HTN) methods, to enhance their problem-solving capabilities. Furthermore, surveying techniques for improving predictive accuracy at test-time and developing frameworks to teach LLMs causal reasoning are also gaining attention. Noteworthy papers include: Optimal Self-Consistency for Efficient Reasoning with Large Language Models, which introduces a novel variant of self-consistency that dynamically allocates samples to questions during inference. CARE: Turning LLMs Into Causal Reasoning Expert, which proposes a framework to enhance LLMs' causal-reasoning ability through supervised fine-tuning.