A Survey of Deep Learning for Mathematical Reasoning

Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life. The development of artificial intelligence (AI) systems capable of solving math problems and proving theorems has garnered significant interest in the fields of machine learning and natural language processing. For example, mathematics serves as a testbed for aspects of reasoning that are challenging for powerful deep learning models, driving new algorithmic and modeling advances. On the other hand, recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning. In this survey paper, we review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade. We also evaluate existing benchmarks and methods, and discuss future research directions in this domain.Comment: Accepted to ACL 2023. The repository is available at https://github.com/lupantech/dl4mat

A Complexity Preserving Transformation from Jinja Bytecode to Rewrite Systems

We revisit known transformations from Jinja bytecode to rewrite systems from the viewpoint of runtime complexity. Suitably generalising the constructions proposed in the literature, we define an alternative representation of Jinja bytecode (JBC) executions as "computation graphs" from which we obtain a novel representation of JBC executions as "constrained rewrite systems". We prove non-termination and complexity preservation of the transformation. We restrict to well-formed JBC programs that only make use of non-recursive methods and expect tree-shaped objects as input. Our approach allows for simplified correctness proofs and provides a framework for the combination of the computation graph method with standard techniques from static program analysis like for example "reachability analysis".Comment: 36 page