@inproceedings{ge-etal-2026-protocycle,
title = "{P}roto{C}ycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design",
author = "Ge, Yutang and
Zhao, Guojiang and
Li, Sihang and
Cheng, Zheng and
Zhao, Zifeng and
Xia, Hanchen and
Ke, Guolin and
Zhang, Linfeng and
Gao, Zhifeng and
Wang, Yu Guang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.763/",
pages = "15562--15586",
ISBN = "979-8-89176-395-1",
abstract = "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 engineers 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."
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%0 Conference Proceedings
%T ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design
%A Ge, Yutang
%A Zhao, Guojiang
%A Li, Sihang
%A Cheng, Zheng
%A Zhao, Zifeng
%A Xia, Hanchen
%A Ke, Guolin
%A Zhang, Linfeng
%A Gao, Zhifeng
%A Wang, Yu Guang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F ge-etal-2026-protocycle
%X 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 engineers 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.
%U https://aclanthology.org/2026.findings-acl.763/
%P 15562-15586
Markdown (Informal)
[ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design](https://aclanthology.org/2026.findings-acl.763/) (Ge et al., Findings 2026)
ACL
- Yutang Ge, Guojiang Zhao, Sihang Li, Zheng Cheng, Zifeng Zhao, Hanchen Xia, Guolin Ke, Linfeng Zhang, Zhifeng Gao, and Yu Guang Wang. 2026. ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design. In Findings of the Association for Computational Linguistics: ACL 2026, pages 15562–15586, San Diego, California, United States. Association for Computational Linguistics.