内容来源：https://research.google/blog/empirical-research-assistance-era-from-nature-publication-to-catalyzing-computational-discovery/

内容总结：

谷歌发布AI科研助手“ERA”，有望加速科学发现进程

2026年5月19日，谷歌研究院 —— 今日，国际顶级学术期刊《自然》刊登了一项来自谷歌的最新研究成果。谷歌正式推出名为“实证研究助手”（Empirical Research Assistance，简称ERA）的新型人工智能工具。该工具旨在通过自动化编写和优化科学代码，帮助科学家大幅提升实验效率，从而加速科学发现的速度与广度。

据谷歌介绍，ERA是目前科研流程中耗时最长的环节之一——即反复迭代测试和优化计算实验——的“克星”。它利用谷歌AI模型Gemini，通过树状搜索策略，在数千种可能的方案中进行探索，结合科学文献并自主编写代码，最终输出针对特定科学问题的最优解决方案。

谷歌在今天上午的I/O开发者大会上宣布，基于ERA技术构建的实验性原型工具“计算发现”（Computational Discovery）已正式通过“Google Labs”的可信测试者计划开放。此外，另一项名为“假设生成”（Hypothesis Generation）的实验工具也同步上线，这两者均属于“Gemini for Science”系列产品，旨在覆盖科学研究从提出假设到进行实验验证的不同阶段。

为了验证ERA的性能，研究团队在多个学科领域进行了基准测试，包括基因组学、公共卫生、卫星图像分析、神经科学预测以及数学任务。结果显示，ERA在所有测试中均达到了专家级水平。谷歌表示，该技术不仅有望“民主化”复杂计算模型的构建能力，降低普通科研人员的门槛，还能进一步拓展资深专家的研究边界。

目前，已有8篇使用ERA解决具体科学问题的论文问世，涵盖流行病预测、二氧化碳监测、径流预报及太阳能工程等具有直接社会效益的领域。谷歌在官方声明中表示：“我们对人工智能驱动的计算工具开启的科学发现新时代感到兴奋，并期待与全球科研社区共同探索其潜力。”

中文翻译：

2026年5月19日
莉齐·多夫曼，产品经理，以及迈克尔·布伦纳，研究科学家，谷歌研究院
今天发表于《自然》杂志的实证研究助手（ERA）是一款面向专家级科学编码的AI工具，它帮助构建了计算发现原型，该原型现已通过谷歌实验室的可信测试者计划开放使用。

AI对人类最大的潜在益处之一是提升科学发现的速度与广度。谷歌开发的实证研究助手（ERA）利用Gemini编写和优化科学代码，针对科学研究中最耗时的环节之一——反复测试和改进计算实验——提供了解决方案。这项技术发表于今天出版的《自然》杂志，论文标题为《旨在帮助科学家编写专家级实证软件的AI系统》。

作为今天I/O大会上我们更广泛的科学公告的一部分，我们也将这项技术作为工具开放，以期开始帮助全球的科学家。ERA是用于构建计算发现的系统之一，计算发现是一款新的实验工具，今天通过Gemini for Science开始更广泛地推出。

我们最早在去年秋天预印本发布时分享了ERA的设计和性能。给定一个科学问题以及成功的衡量标准，ERA可以搜索科学文献、编写代码、探索解决方案、组合技术并评估结果。ERA会考虑数千种选项，采用树搜索方法，针对既定目标优化其输出的代码。

我们的《自然》论文描述了ERA在涵盖多个学科的基准问题上的测试结果：基因组学、公共卫生、卫星图像分析、神经科学预测、通用时间序列预测基准以及数学。结果显示，ERA在所有基准测试中均达到了专家级性能，这有望让未来更多人获得专家级的计算建模能力，并拓展现有专家的能力范围。

过去六个月里，谷歌研究院的科学家及我们的合作者一直在积极测试ERA。四月底，我们分享了四个项目的实例，这些项目利用ERA研究当前科学中的开放性问题。

目前，我们共有八篇将ERA应用于具体科学问题的手稿，包括下面描述的五篇新发布的论文。总体而言，这些结果表明ERA如何能够推动多个领域的进展，带来直接的科研影响和公共效益。

今天，谷歌将开始逐步开放基于AlphaEvolve和ERA构建的计算发现工具的访问权限。我们对这一由AI计算工具推动的科学发现新时代充满期待，并希望与更广泛的社区共同进一步开发这些工具。

另一项新推出的Gemini for Science实验是假设生成，它基于AI联合科学家构建，同样在今天发表于《自然》杂志的论文中有所描述。假设生成、计算发现以及新的文献洞察实验工具相辅相成，支持科学方法的不同阶段。请访问labs.google/science登记您的兴趣。

我们感谢作者列表中的合作者们，他们帮助创造了ERA，同时也感谢所有早期采用该工具的科学家。ERA背后的算法开发由埃塞尔·艾根、格奥尔基·科马尼奇和希布尔·穆拉德主导。流行病学预测工作由扎赫拉·沙姆西、莎拉·马丁森、尼古拉斯·赖希、玛蒂娜·普洛梅卡和布莱恩·威廉姆斯领导。二氧化碳监测研究由亚伦·索纳本德-W、肖恩·坎贝尔、蕾妮·约翰斯顿、维沙尔·巴楚、卡尔·埃尔金、克里斯托弗·范·阿斯代尔、约翰·普拉特和安娜·米查拉克领导。径流预测论文由伊格纳西奥·洛佩斯-戈麦斯、迈克尔·布伦纳和塔皮奥·施耐德撰写。太阳能工程领域的论文由迈克尔·布伦纳、莉齐·多夫曼和约翰·普拉特撰写。宏观经济零售销售预测研究由迈克尔·布伦纳、钱泽祝、扎赫拉·沙姆西、梅特·尼尔森和保罗·拉库利亚领导。我们感谢约翰·普拉特、迈克尔·布伦纳、希布尔·穆拉德、莉齐·多夫曼、维普·古普塔、佐宾·加赫拉马尼、艾莉森·伦茨、埃里卡·布兰德、凯瑟琳·周、罗尼特·莱瓦维·莫拉德、约西·马蒂亚斯和詹姆斯·曼尼卡提供的领导支持。

英文来源：

May 19, 2026
Lizzie Dorfman, Product Manager, and Michael Brenner, Research Scientist, Google Research
Published today in Nature, Empirical Research Assistance (ERA) is an AI tool for expert-level scientific coding that helped build the Computational Discovery prototype, now available through a trusted tester program in Google Labs.
One of AI’s greatest potential benefits to humanity is increasing the speed and scope of scientific discovery. Empirical Research Assistance (ERA), a Google-developed research tool that uses Gemini to write and optimize scientific code, addresses one of the most time-consuming parts of scientific research: iteratively testing and refining computational experiments. It is described in "AI system designed to help scientists write expert-level empirical software”, published today in the journal Nature.
As part of our wider science announcements at I/O today, we are also making this technology accessible as a tool that can begin to help scientists around the world. ERA is one of the systems used to build Computational Discovery, a new experimental tool that is starting to roll out more broadly today through Gemini for Science.
We first shared the design and performance of ERA in the fall, when the preprint was released. Given a scientific problem and a measure of success, ERA can search scientific literature, write code, explore solutions, combine techniques and evaluate the results. ERA considers thousands of options, using a tree search approach to optimize its output code against its given goal.
Our Nature publication describes testing ERA on benchmark problems spanning a variety of disciplines: genomics, public health, satellite imagery analysis, neuroscience prediction, a general time-series forecasting benchmark, and mathematics. Results show ERA achieves expert-level performance across all of these benchmarks, potentially democratizing future access to expert-level computational modeling and expanding the capabilities of current experts.
Over the past six months, Google Research scientists and our collaborators have been actively experimenting with ERA. In late April, we shared examples of four projects we’d worked on that use ERA to investigate current open problems in science.
We now have a total of eight manuscripts that apply ERA to specific scientific problems, including the five newly released papers described below. Collectively, these results show how ERA can help drive progress in several domains with immediate scientific impact and public benefit.
Today, Google will begin gradually opening access to Computational Discovery, built with AlphaEvolve and ERA. We are excited for this new era of scientific discovery enabled by AI-based computational tools, and to further develop them alongside the broader community.
Another of the newly launched Gemini for Science experiments is Hypothesis Generation, built with AI Co-Scientist, also described in a paper published today in Nature. Hypothesis Generation and Computational Discovery, as well as the new Literature Insights experimental tool, are complementary in their support of different stages of the scientific method. Visit labs.google/science to register your interest.
We’d like to thank our collaborators, listed on the authors’ list, who helped create ERA, as well as all the scientists who are among the early adopters. Algorithm development underlying ERA was led by Eser Aygun, Gheorghe Comanici and Shibl Mourad. The epidemiological forecasting work is led by Zahra Shamsi, Sarah Martinson, Nicholas Reich, Martyna Plomecka, and Brian Williams. The research on carbon dioxide monitoring is led by Aarón Sonabend-W, Sean Campbell, Renee Johnston, Vishal Batchu, Carl Elkin, Christopher Van Arsdale, John Platt, and Anna Michalak. The paper on runoff forecasting is authored by Ignacio Lopez-Gomez, Michael Brenner, and Tapio Schneider. The manuscript in solar energy engineering is authored by Michael Brenner, Lizzie Dorfman, and John Platt. The research in macroeconomic retail sales forecasting is led by Michael Brenner, Qian-Ze Zhu, Zahra Shamsi, Mette Nielsen, and Paul Raccuglia. We are grateful for leadership support from John Platt, Michael Brenner, Shibl Mourad, Lizzie Dorfman, Vip Gupta, Zoubin Ghahramani, Alison Lentz, Erica Brand, Katherine Chou, Ronit Levavi Morad, Yossi Matias, and James Manyika.