Reasoning Paradigm

Reasoning paradigms in artificial intelligence explore how machines can perform logical inference and problem-solving, mirroring human cognitive processes. Current research focuses on improving the faithfulness and efficiency of reasoning in large language models (LLMs) through techniques like chain-of-thought prompting, multi-agent collaboration, and the integration of symbolic reasoning with LLMs. These advancements aim to enhance the reliability and explainability of AI systems across diverse applications, including healthcare, decision-making, and vision-language tasks, ultimately bridging the gap between machine and human reasoning capabilities.