Designing proteins that satisfy natural language functional requirements is a central goal in protein engineering. A straightforward baseline is to fine-tune generic instruction-tuned LLMs as direct text-to-sequence generators, but this is data- and compute-hungry. With limited supervision, LLMs can produce coherent plans in text yet fail to reliably realize them as sequences. This plan-execute gap motivates ProtoCycle, an agentic framework for protein design that uses LLMs primarily to drive a multi-round, feedback-driven decision cycle. ProtoCycle couples an LLM planner with a lightweight tool environment designed to emulate the iterative workflow of human protein engineering and uses LLM-driven reflection on tool feedback to revise plans. Trained with supervised trajectories and online reinforcement learning, ProtoCycle achieves strong language alignment while maintaining competitive foldability, and ablations show that reflection substantially improves sequence quality.
@article{arxiv.2604.16896,
title = {ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design},
author = {Yutang Ge and Guojiang Zhao and Sihang Li and Zheng Cheng and Zifeng Zhao and Hanchen Xia and Guolin Ke and Linfeng Zhang and Zhifeng Gao and Yuguang Wang},
journal= {arXiv preprint arXiv:2604.16896},
year = {2026}
}
Comments
25 pages, 11 figures. Accepted to Findings of ACL 2026