Mathematical reasoning stands as a pinnacle of human intelligence. The rapid advancements in artificial intelligence, particularly in large language models (LLMs), have opened new frontiers at the intersection of AI and mathematical reasoning. This workshop aims to explore the potential of AI in comprehending and advancing mathematical reasoning, with a focus on fostering collaboration between humans and machines to push the boundaries of mathematical discovery. The central theme revolves around the question:

``How can we leverage and advance the mathematical reasoning abilities of machine learning models, and drive innovation across scientific and practical domains?''

Our workshop will bring together researchers from diverse backgrounds, institutions, and disciplines to discuss the progress and future of AI technologies in mathematics. Specifically, we will delve into the areas related to the following:

• Automated Theorem Proving: How can we build consistent theorem-proving systems? How can theorem-proving systems assist humans through human-computer interaction?
• Automated Theorem Generation: Can neural models generate new and practically meaningful theorems that have been discovered? How can we utilize these newly generated theorems?
• Autoformalization and Verification: How can we improve the precision of translating natural language proofs into formal proofs, and vice versa?
• Problem Solving: How can we develop AI models to solve complex mathematical computational problems across various domains? How can AI models improve themselves during the learning process?
• Applications of AI in Mathematics: What are the practical applications of AI-driven mathematical reasoning in various fields such as sciences, engineering, finance, and education?

The intended outcome is to identify new ideas, open problems, and interdisciplinary areas for future research related to mathematical reasoning. To this end, we welcome papers on areas related, but not limited, to:

• Algorithm: How to develop effective algorithms (e.g., reinforcement learning, self-improve/evolve) to improve reasoning ability? What are the key principles for developing algorithms that minimize resource consumption (e.g., time, memory) while maintaining or improving reasoning performance?
• Data Generation: Can AI models generate questions that they cannot answer correctly? Can AI models achieve self-improvement through self-generated data?
• Tool Utilization: How can AI systems leverage existing tools, such as code and software, to solve practical mathematical problems more effectively?
• Limitation Analysis: What are the drawbacks or limitations of current models in mathematical reasoning (e.g. robustness, generalization, and reasoning boundary)? How can these limitations be quantitatively analyzed?