Recent advances in large language models (LLMs) have highlighted the effectiveness of chain-of-thought reasoning in symbolic domains such as mathematics and programming. However, our study shows that directly transferring such text-based reasoning paradigms to protein function understanding is ineffective: reinforcement learning mainly amplifies superficial keyword patterns while failing to introduce new biological knowledge, resulting in limited generalization. We argue that protein function prediction is a knowledge-intensive scientific task that fundamentally relies on external biological priors and computational tools rather than purely internal reasoning. To address this gap, we propose PFUA, a tool-augmented protein reasoning agent that unifies problem decomposition, tool invocation, and grounded answer generation. Instead of relying on long unconstrained reasoning traces, PFUA integrates domain-specific tools to produce verifiable intermediate evidence. Experiments on four benchmarks demonstrate that PFUA consistently outperforms text-only reasoning models with an average performance improvement of 103%.
@article{arxiv.2601.03604,
title = {Interleaved Tool-Call Reasoning for Protein Function Understanding},
author = {Chuanliu Fan and Zicheng Ma and Huanran Meng and Aijia Zhang and Wenjie Du and Jun Zhang and Yi Qin Gao and Ziqiang Cao and Guohong Fu},
journal= {arXiv preprint arXiv:2601.03604},
year = {2026}
}