Recent advancements in artificial intelligence highlight new methodologies for enhancing reasoning capabilities in large language models (LLMs). Researchers from MIT introduced a concept titled 'Emergence of Abstractions: Concept Encoding and Decoding Mechanism for In-Context Learning in Transformers,' which aims to improve in-context learning. Concurrently, Meta has proposed a novel approach dubbed 'Chain of Continuous Thought,' which allows LLMs to process information through neural patterns rather than traditional language explanations, thereby increasing efficiency. Additionally, researchers from Johns Hopkins University presented their work on 'Compressed Chain of Thought: Efficient Reasoning Through Dense Representations,' further contributing to the discourse on optimizing reasoning in LLMs.