The field of mathematical reasoning with Large Language Models (LLMs) is rapidly advancing, with a focus on improving formal theorem proving and autoformalization capabilities. Recent developments have highlighted the potential of LLMs in tackling complex mathematical problems, but also revealed limitations and challenges that need to be addressed. Notably, the trade-offs between formal and informal mathematics as training domains, and the reasons behind the brittleness of proof generation, are being explored. Innovative approaches, such as scaffolded data synthesis and self-correction, are being introduced to improve the performance of LLMs in automated theorem proving. Furthermore, the development of novel frameworks for synthesizing challenging mathematical problems is enhancing LLM reasoning capabilities.

Noteworthy papers in this area include: Goedel-Prover-V2, which achieves state-of-the-art performance in automated theorem proving with its innovative approach to scaffolded data synthesis and self-correction. StepFun-Formalizer, which unlocks the autoformalization potential of LLMs through knowledge-reasoning fusion, achieving state-of-the-art scores on FormalMATH-Lite and ProverBench. MathSmith, which proposes a novel framework for synthesizing challenging mathematical problems, exhibiting strong scalability, generalization, and transferability.