Seed science is essential for modern agriculture, directly influencing crop yields and global food security. However, challenges such as interdisciplinary complexity and high costs with limited returns hinder progress, leading to a shortage of experts and insufficient technological support. While large language models (LLMs) have shown promise across various fields, their application in seed science remains limited due to the scarcity of digital resources, complex gene-trait relationships, and the lack of standardized benchmarks. To address this gap, we introduce SeedBench—the first multi-task benchmark specifically designed for seed science. Developed in collaboration with domain experts, SeedBench focuses on seed breeding and simulates key aspects of modern breeding processes. We conduct a comprehensive evaluation of 26 leading LLMs, encompassing proprietary, open-source, and domain-specific fine-tuned models. Our findings not only highlight the substantial gaps between the power of LLMs and the real-world seed science problems, but also make a foundational step for research on LLMs for seed design.

In ancient Chinese books, punctuation marks are typically absent in engraved texts. Sentence segmentation and punctuation heavily rely on the meticulous efforts of experts and scholars. Therefore, the work of automatic punctuation and sentence segmentation plays a very important role in promoting ancient books, as well as the inheritance of Chinese culture. In this paper, we present a method for fine-tuning downstream tasks for large language model using the LoRA approach, leveraging the EvaHan2024 dataset. This method ensures robust output and high accuracy while inheriting the knowledge from the large pre-trained language model Xunzi.