The paper introduces ScientistTwo, a fully autonomous multi‑agent framework that takes a scientific problem, establishes baselines, generates hypotheses, and coordinates specialized agents to conduct an end‑to‑end discovery cycle without human intervention. It rigorously tests and refines its methods through automated experiments, ablation studies, and a closed‑loop peer‑review engine. Benchmarking against top conferences (ICLR, ICML, NeurIPS) shows that ScientistTwo produces expert‑level, publishable papers and codebases that outperform human state‑of‑the‑art models and receive higher review ratings under automated AI review.
By Jaehyun Nam, Jinsung Yoon, Yanzhou Pan, Yubo Wang, Rui Meng, Parthasarathy Ranganathan, Tomas Pfister
arXiv:2606. 15497v1 Announce Type: new Abstract: The automation of science is a long-standing ambition in the field of AI.
By Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Chris Lu, Shengran Hu, Jakob Foerster, David Ha, Jeff Clune
The paper "AI in Science: Early Insights" analyzes AI’s impact on scientific work using data from 15 million Gemini interactions, 2,600 specialized AI models, and a survey of 600 scientists. It finds widespread AI adoption, complementary use of large language models and specialized tools, significant productivity gains of about seven hours per week, and a shift in research bottlenecks toward hypothesis backlog and verification needs. The study suggests AI can boost scientific productivity but its full effect depends on addressing new downstream challenges.
By Mihai Codreanu, Alex Imas, Juan Mateos-Garcia, Joseph Emmens, Evalyne Muiruri, Arthur Turrell, Julian Jacobs, Atoosa Kasirzadeh, Ana Trisovic, Yiyuan Chen, Tanya Rodchenko, Catherine Pollard, Scott Strand, Daniel Rock, Zanna Iscenko, Fabien Curto Millet, Neil Thompson, James Manyika
arXiv:2607. 28631v1 Announce Type: new Abstract: AI Scientist systems capable of autonomous research have the potential to significantly accelerate scientific discovery.
By Vaibhava Lakshmi Ravideshik, Mayank Kejriwal
arXiv:2610.28763v1 Announce Type: cross
Abstract: Frontier coding agents are increasingly framed as end-to-end automated researchers, and ML for science offers a demanding test of that promise: impro...
By Ming Zhong, Stacy Li, Nicholas Carlini, Matthew Jagielski
arXiv:2606. 07462v1 Announce Type: new Abstract: As foundation models advance and agent scaffolding becomes increasingly sophisticated, agents have demonstrated remarkable proficiency in complex, long-horizon coding tasks and even autonomous experiment execution.
By Jiayu Wang, Weijiang Lv, Bowen Fu, Jing Fu, Jiayi Song, Lingyu Zhang, Lanxuan Xue, Luodi Chen, Zepeng Xin, Kaiyu Li, Xiangyong Cao
arXiv:2608. 14407v1 Announce Type: new Abstract: We present a survey of the past and future of AI Scientists: machines capable of automating science.
By Ross D. King
arXiv:2608. 02775v1 Announce Type: new Abstract: Scientific discovery has advanced through successive transformations in the organization of knowledge.
By Xinjie Yao, Xingxin Xu, Xiyuan Gao, Zhoupeng Guo, Kunlong Yang, Dengyu Zhao, Siqi Zhao, Zhihe Fan, Yichen Dong, Xin Li, Jiekang Feng, Jiahe Wu, Sen Wang, Beiming Yu, Kejia Zhao, Ruipu Zhao, Jiaqi Zhou, Heyang Li, Jianjun Chen, Anbo Dai, Xin Liu, Zhengtao Yu, Qinghua Hu, Pengfei Zhu
IdeaTaste is a benchmark that evaluates large language models (LLMs) on their ability to generate research proposals that align with what human researchers actually publish. The study compares proposals generated by six contemporary LLMs against real papers from four research areas, finding that LLMs achieve only about 26% of the citation matching and 18% of the idea matching of human communities. Even larger LLM populations and richer field contexts do not close this gap, indicating that LLMs struggle more with translating research directions into concrete approaches than with selecting the right topics.
By Shanshan Zhong, Chen Xu, Jingjie Ning, Zhengzhong Liu, Aviral Kumar, Chenyan Xiong
arXiv:2609.07611v1 Announce Type: new
Abstract: Scientific ideation is the capacity to formulate novel and testable hypotheses from scientific evidence, and autonomous AI scientists depend on it. Exi...
By Yunxiang Mo, Tianshi Zheng, Yisen Gao, Rui Wang, Newt Nguyen Kim Hue Nam, Kelvin Kiu Wai Tam, Jiaxin Bai, Yangqiu Song, Ginny Wong, Simon See
SAIL is an open model with 35 B total and 3 B active parameters designed for literature research, scientific coding, and multi‑step research workflows. It is developed through a science‑aware improvement loop in which agents built on frontier AI models analyze task failures, create training tasks to address capability gaps, and iterate this process across multiple development cycles. The training pipeline combines supervised fine‑tuning, specialist training, multi‑teacher on‑policy distillation, and agentic reinforcement learning, enabling SAIL to achieve competitive performance on scientific research tasks with fewer parameters than leading open‑weight models.
By SAIL Model Team, Boyuan Sun, Bryan Dai, Che Liu, Chi Liu, Derek Li, Hongming Piao, Mengzhuo Chen, Xidong Wang, Yan Shu, Yinda Chen, Ziyang Zeng
arXiv:2610.07184v1 Announce Type: new
Abstract: A key challenge in building AI systems for scientific research is enabling $\textit{scientific exploration}$: the systematic process of investigating u...
By Xuchen Gong, Shane Gu, Haokun Liu, Dixi Yao, Chenhao Tan, Tian Li