A novel framework for historical document restoration has been introduced, leveraging large language models with retrieval-augmented generation to restore damaged texts. This approach significantly outperforms existing methods, achieving substantial gains in restoring both general characters and named entities. The model has been tested on Korean historical documents, demonstrating its potential as a practical tool for domain experts.

A new arXiv paper by Alibaba researchers details a ReAct-style agentic setup integrating Large Language Models with SageMath, a powerful Computer Algebra System. This novel approach demonstrates substantial performance gains across frontier LLMs in solving research-level mathematical problems, significantly narrowing the capability gap between open-weight and closed models and paving the way for automated conjecture discovery.