The field of large language models (LLMs) is moving towards enhancing their reasoning capabilities, with a focus on improving their ability to perform complex tasks such as multi-hop reasoning, logical reasoning, and formal verification. Researchers are exploring various techniques, including the integration of knowledge graphs, retrieval-augmented generation, and algorithm-guided search, to address the challenges of reliability and interference in LLMs. The development of new datasets and benchmarks, such as those for commonsense reasoning over long-tail knowledge and multi-hop reasoning in specific domains, is also underway to evaluate and improve the performance of LLMs. Furthermore, researchers are investigating the application of LLMs in software engineering, including automated code generation and optimization, and formal verification of software code. Noteworthy papers include: CoT-RAG, which proposes a novel reasoning framework that integrates chain-of-thought and retrieval-augmented generation to enhance reasoning in LLMs. LogicTree, which employs an algorithm-guided search to automate structured proof exploration and ensure logical coherence. CoLoTa, which presents a new dataset for entity-based commonsense reasoning over long-tail knowledge. Token-Aware Coding Flow, which introduces an innovative method to address the token inflation problem caused by smelly code in the chain-of-thought process.