# ProtoCycle: Reflective Tool-Augmented Planning for Text-Guided Protein Design

arXiv: https://arxiv.org/abs/2604.16896 · HTML: https://mlsanity.com/paper/2604.16896/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 18 Apr 2026.

## Builds on (most cited first, 16 of 16)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [HybridFlow: A Flexible and Efficient RLHF Framework](https://mlsanity.com/paper/2409.19256/) (2024)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [A Text-guided Protein Design Framework](https://mlsanity.com/paper/2302.04611/) (2023)
- [Advancing Multimodal Reasoning via Reinforcement Learning with Cold Start](https://mlsanity.com/paper/2505.22334/) (2025)
- [Demystifying Reinforcement Learning in Agentic Reasoning](https://mlsanity.com/paper/2510.11701/) (2025)
- [Mol-R1: Towards Explicit Long-CoT Reasoning in Molecule Discovery](https://mlsanity.com/paper/2508.08401/) (2025)
- [Annotation-guided Protein Design with Multi-Level Domain Alignment](https://mlsanity.com/paper/2404.16866/) (2024)
- [EvoLlama: Enhancing LLMs' Understanding of Proteins via Multimodal Structure and Sequence Representations](https://mlsanity.com/paper/2412.11618/) (2024)
- [Protein Design with Dynamic Protein Vocabulary](https://mlsanity.com/paper/2505.18966/) (2025)
- [PDFBench: A Benchmark for De novo Protein Design from Function](https://mlsanity.com/paper/2505.20346/) (2025)

## Built on by (most cited first, 2 of 2)

- [RefusalBench: Why Refusal Rate Misranks Frontier LLMs on Biological Research Prompts](https://mlsanity.com/paper/2605.21545/) (2026)
- [Bridging the Evidence-to-Execution Gap:A Reflective Agent for Multi-Objective Peptide Design](https://mlsanity.com/paper/2610.06190/) (2026)

## Abstract (arXiv)

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.