The field of large language models (LLMs) is rapidly advancing, with a focus on improving reasoning capabilities. Recent developments have highlighted the importance of structured reasoning, coherence, and consistency in LLMs. Researchers are exploring new methods to evaluate and improve LLM reasoning, including the use of benchmarks, datasets, and novel evaluation frameworks. Notably, the incorporation of chain-of-thought rationales and autoregressive argumentative structure prediction has shown promise in enhancing LLM performance on various tasks. Furthermore, studies have investigated the impact of curriculum learning, data ordering, and agentic-based math data generation on LLM reasoning. Overall, the field is moving towards developing more robust, interpretable, and generalizable LLMs. Noteworthy papers include: Assessing Coherency and Consistency of Code Execution Reasoning by Large Language Models, which introduces a novel metric to measure reasoning consistency. Can LLMs Correct Themselves? A Benchmark of Self-Correction in LLMs, which evaluates the effectiveness of self-correction strategies in LLMs. End-to-End Argument Mining through Autoregressive Argumentative Structure Prediction, which proposes a framework for jointly formulating key tasks of argument mining in an end-to-end fashion. OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction, which presents a unified framework for aligning autoregressive LLMs with clinical reasoning for outcome prediction. AgenticMath: Enhancing LLM Reasoning via Agentic-based Math Data Generation, which proposes a novel pipeline for generating high-quality mathematical question-answer pairs to enhance LLM supervised fine-tuning.