Self Reasoning

Self-reasoning in artificial intelligence focuses on enhancing the ability of models, particularly large language models (LLMs), to solve complex problems by explicitly incorporating reasoning steps into their processes. Current research emphasizes developing frameworks that enable LLMs to generate and evaluate their own reasoning trajectories, often leveraging techniques like self-play, retrieval augmentation, and transformer-based architectures. These advancements aim to improve the reliability, traceability, and overall performance of AI systems on tasks requiring logical deduction and inference, with applications ranging from question answering and theorem proving to scientific discovery and image analysis.