@inproceedings{xie-etal-2026-far,
title = "How Far Are {AI} Scientists from Changing the World? A Survey of Autonomous Scientific Discovery",
author = "Xie, Qiujie and
Weng, Yixuan and
Zhu, Minjun and
Fang, Junying and
Shen, Fuchen and
Huang, Shulin and
Lin, Zhen and
Wang, Zhouyang and
Li, Shichen and
Zhou, Jiahui and
Mao, Zilan and
Liu, Sifan and
Liu, Zhenhao and
Yin, Dalv and
Wei, Fanjunduo and
Ning, Zhiyuan and
Yang, Zijie and
Yang, Linyi and
Wu, Jian and
Zhang, Yue",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1285/",
pages = "29272--29288",
ISBN = "979-8-89176-459-0",
abstract = "This survey asks how far AI Scientist systems are from changing the world, and argues that their impact should be evaluated not by whether they produce plausible papers, but by whether they produce auditable research trajectories that can be inspected, reproduced, and challenged. We first provide a definition of AI Scientists, distinguish them from adjacent scientific AI systems, and organize the field around four core capabilities: knowledge acquisition, hypothesis generation, experimental validation, and evolution. We further synthesize representative systems by autonomy, execution grounding, auditability, human control, and evidence level. The resulting picture shows a field moving from prompt-to-paper automation toward auditable research laboratories, where executable grounding, provenance, and human-governed autonomy become central. We conclude by identifying open challenges toward trustworthy AI-driven science and providing a roadmap for integrating AI Scientists into the future landscape of scientific discovery."
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<abstract>This survey asks how far AI Scientist systems are from changing the world, and argues that their impact should be evaluated not by whether they produce plausible papers, but by whether they produce auditable research trajectories that can be inspected, reproduced, and challenged. We first provide a definition of AI Scientists, distinguish them from adjacent scientific AI systems, and organize the field around four core capabilities: knowledge acquisition, hypothesis generation, experimental validation, and evolution. We further synthesize representative systems by autonomy, execution grounding, auditability, human control, and evidence level. The resulting picture shows a field moving from prompt-to-paper automation toward auditable research laboratories, where executable grounding, provenance, and human-governed autonomy become central. We conclude by identifying open challenges toward trustworthy AI-driven science and providing a roadmap for integrating AI Scientists into the future landscape of scientific discovery.</abstract>
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%0 Conference Proceedings
%T How Far Are AI Scientists from Changing the World? A Survey of Autonomous Scientific Discovery
%A Xie, Qiujie
%A Weng, Yixuan
%A Zhu, Minjun
%A Fang, Junying
%A Shen, Fuchen
%A Huang, Shulin
%A Lin, Zhen
%A Wang, Zhouyang
%A Li, Shichen
%A Zhou, Jiahui
%A Mao, Zilan
%A Liu, Sifan
%A Liu, Zhenhao
%A Yin, Dalv
%A Wei, Fanjunduo
%A Ning, Zhiyuan
%A Yang, Zijie
%A Yang, Linyi
%A Wu, Jian
%A Zhang, Yue
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F xie-etal-2026-far
%X This survey asks how far AI Scientist systems are from changing the world, and argues that their impact should be evaluated not by whether they produce plausible papers, but by whether they produce auditable research trajectories that can be inspected, reproduced, and challenged. We first provide a definition of AI Scientists, distinguish them from adjacent scientific AI systems, and organize the field around four core capabilities: knowledge acquisition, hypothesis generation, experimental validation, and evolution. We further synthesize representative systems by autonomy, execution grounding, auditability, human control, and evidence level. The resulting picture shows a field moving from prompt-to-paper automation toward auditable research laboratories, where executable grounding, provenance, and human-governed autonomy become central. We conclude by identifying open challenges toward trustworthy AI-driven science and providing a roadmap for integrating AI Scientists into the future landscape of scientific discovery.
%U https://aclanthology.org/2026.emnlp-main.1285/
%P 29272-29288
Markdown (Informal)
[How Far Are AI Scientists from Changing the World? A Survey of Autonomous Scientific Discovery](https://aclanthology.org/2026.emnlp-main.1285/) (Xie et al., EMNLP 2026)
ACL
- Qiujie Xie, Yixuan Weng, Minjun Zhu, Junying Fang, Fuchen Shen, Shulin Huang, Zhen Lin, Zhouyang Wang, Shichen Li, Jiahui Zhou, Zilan Mao, Sifan Liu, Zhenhao Liu, Dalv Yin, Fanjunduo Wei, Zhiyuan Ning, Zijie Yang, Linyi Yang, Jian Wu, and Yue Zhang. 2026. How Far Are AI Scientists from Changing the World? A Survey of Autonomous Scientific Discovery. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 29272–29288, Budapest, Hungary. Association for Computational Linguistics.