Despite the success of large language models (LLMs) on general-purpose tasks, their performance in highly specialized domains such as biomedicine remains unsatisfactory.A key limitation is the inability of LLMs to effectively leverage biomedical tools, which clinical experts and biomedical researchers rely on extensively in daily workflows.While recent general-domain tool-calling datasets have substantially improved the capabilities of LLM agents, existing efforts in the biomedical domain largely rely on in-context learning and restrict models to a small set of tools.To address this gap, we introduce BIOTOOL, a comprehensive biomedical tool-calling dataset designed for fine-tuning LLMs.BIOTOOL comprises 34 frequently used tools collected from the NCBI, Ensembl, and UniProt databases, along with 7,040 high-quality, human-verified query-API call pairs spanning variation, genomics, proteomics, evolution, and general biology.Fine-tuning a 4-billion-parameter LLM on BIOTOOL yields substantial improvements in biomedical tool-calling performance, outperforming cutting-edge commercial LLMs such as GPT-5.1.Furthermore, human expert evaluations demonstrate that integrating a BIOTOOLfine-tuned tool caller significantly improves downstream answer quality compared to the same LLM without tool usage, highlighting the effectiveness of BIOTOOL in enhancing the biomedical capabilities of LLMs.The full dataset and evaluation code are available at https://github.com/gxx27/BioTool.

Deep neural networks incorporating discrete latent variables have shown significant potential in sequence modeling. A notable approach is to leverage vector quantization (VQ) to generate discrete representations within a codebook. However, its discrete nature prevents the use of standard backpropagation, which has led to challenges in efficient codebook training. In this work, we introduce **Meta-Quantization (MQ)**, a novel vector quantization training framework inspired by meta-learning. Our method separates the optimization of the codebook and the auto-encoder into two levels. Furthermore, we introduce a hyper-net to replace the embedding-parameterized codebook, enabling the codebook to be dynamically generated based on the feedback from the auto-encoder. Different from previous VQ objectives, our innovation results in a meta-objective that makes the codebook training task-aware. We validate the effectiveness of MQ with VQVAE and VQGAN architecture on image reconstruction and generation tasks. Experimental results showcase the superior generative performance of MQ, underscoring its potential as a robust alternative to existing VQ methods.