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Interleaved Tool-Call Reasoning for Protein Function Understanding

Chuanliu Fan, Zicheng Ma, Huanran Meng, Aijia Zhang, Wenjie Du, Jun Zhang, Yi Qin Gao, Ziqiang Cao, Guohong Fu

TL;DR

The paper shows that long chain-of-thought reasoning developed for symbolic domains does not transfer well to protein function understanding, a knowledge-intensive task that relies on external biological priors. It introduces PFUA, a tool-augmented protein reasoning agent that interleaves problem decomposition, tool invocation, and grounded evidence generation to produce auditable intermediate outputs. Across four protein QA benchmarks, PFUA consistently outperforms text-only reasoning and retrieval-based baselines, with substantial gains in recall-based metrics and backbone robustness. The work highlights the value of explicit tool usage in grounding AI reasoning for scientific tasks and discusses broader implications and future directions for tool-enabled biological AI systems.

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 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%.

Interleaved Tool-Call Reasoning for Protein Function Understanding

TL;DR

The paper shows that long chain-of-thought reasoning developed for symbolic domains does not transfer well to protein function understanding, a knowledge-intensive task that relies on external biological priors. It introduces PFUA, a tool-augmented protein reasoning agent that interleaves problem decomposition, tool invocation, and grounded evidence generation to produce auditable intermediate outputs. Across four protein QA benchmarks, PFUA consistently outperforms text-only reasoning and retrieval-based baselines, with substantial gains in recall-based metrics and backbone robustness. The work highlights the value of explicit tool usage in grounding AI reasoning for scientific tasks and discusses broader implications and future directions for tool-enabled biological AI systems.

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 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%.
Paper Structure (27 sections, 1 equation, 3 figures, 3 tables)

This paper contains 27 sections, 1 equation, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. LLMs for Protein Understanding
  4. Reasoning LLMs
  5. Tool-Powered LLMs
  6. Methods
  7. Tool Pool
  8. Sequence basic properties.
  9. Homology search with MMseqs2.
  10. Pfam domain analysis.
  11. TMbed transmembrane topology prediction.
  12. Experiments
  13. Experimental Setup
  14. Baselines
  15. Benchmarks
  16. ...and 12 more sections

Figures (3)

  • Figure 1: GRPO Training of the Protein Function Understanding Task.
  • Figure 2: Overview of the interleaved tool call reasoning pipeline for protein function understanding.
  • Figure 3: Results on three additional protein QA benchmarks. Performance is reported using ROUGE-1 and ROUGE-L recall (ROUGE-1 / ROUGE-L). The backbone online LLM of Qwen3-RAG and PFUA is Qwen3-Max-Preview.