AutoNovo 用智能体程序搜索发现可迁移的核酸设计算法
AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms
这篇论文让智能体自己写代码来设计核酸序列，还专门解决了换模型就失效的过拟合问题，做科学计算方向的朋友可以看看。
arXiv 论文 2610.16232 提出 AutoNovo，用 agentic program search 迭代提出、执行、评估并修改核酸设计代码。该方法在 3/3 个 DNA 设计任务上取得评估方法中的最佳 in-silico 平均成绩，在 6/6 个迁移任务上对独立模型保持泛化。研究发现得到的是一族核酸设计器而非单一通用最优设计器，团队计划开源这些设计器。
AutoNovo: Agentic Program Search Discovers Transferable Nucleic Acid Design Algorithms
In-silico optimization of nucleic acids relies on predictive oracles that imperfectly capture biological function, so sequences optimized against one model may exploit model-specific artifacts and fail to generalize to independent models. Recent studies have shown that not all designers generalize equally well to independent models. In this work, we use agentic program search to iteratively propose, execute, evaluate, and revise code for nucleic-acid designers. We describe the successful strategies used to prompt the search. AutoNovo designers achieved the best observed mean among the evaluated methods on 3/3 DNA-design tasks in terms of in-silico performance, and 6/6 transfer tasks in terms of independent models. The designers come from a family of discovered nucleic acid designers rather than a single universal best designer. We plan to release the resulting designers. These results are the first steps in creating nucleic acid designers that maximize cross-model generalization, and thus improving the odds that in-silico designed sequences reflect real biology.
