@inproceedings{fan-etal-2026-interleaved,
    title = "Interleaved Tool-Call Reasoning for Protein Function Understanding",
    author = "Fan, Chuanliu  and
      Ma, Zicheng  and
      Meng, Huanran  and
      Zhang, Aijia  and
      Du, Wenjie  and
      Zhang, Jun  and
      Cao, Ziqiang  and
      Fu, Guohong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.592/",
    pages = "12977--12995",
    ISBN = "979-8-89176-390-6",
    abstract = "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 Protein Function Understanding Agent (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{\%}. We believe PFUA has the potential to become a standard paradigm for agentic reasoning in knowledge-intensive life science domains."
}