The paper proposes using large language models to automatically build simple baselines for deep learning studies in the physical and life sciences. By assembling 36 supervised classification and regression tasks from high‑impact journals, the authors show that the generated baselines recover a median of 95.5% of the performance gap between a trivial predictor and the deep learning model, and in 12 studies even match or surpass the model’s performance. The work also explores how baseline quality varies across domains, venues, and the influence of test‑time compute and contextual information.
By Caleb N. Ellington, Jiaqi Wang, Sohan Addagudi, Elijah Cole
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. 20539v1 Announce Type: cross Abstract: While deep learning has accelerated drug discovery, its impact on biomanufacturing has been considerably more limited.
By Kyunghoon Hur, Eunjung Jeon, Hyun Woo Kim, Gyubok Lee, Seongjun Yang
The paper introduces the task of Scientific Claim Unlearning and presents a new benchmark, SciUnlearn, to evaluate it. It highlights that language models trained on static scientific corpora risk disseminating outdated or retracted claims as scientific knowledge evolves. Current machine unlearning methods fail to effectively remove claim-level knowledge, often only suppressing it superficially, underscoring the need for specialized techniques for structured knowledge removal.
By Snigdha Paul, Manasi Patwardhan, Arman Cohan
arXiv:2506.22729v3 Announce Type: replace-cross
Abstract: Persistence is often regarded as a virtue in science. In this paper, however, we challenge this conventional view by highlighting its context...
By Honglin Bao, Beichen Lu, Kai Li
arXiv:2606. 18874v1 Announce Type: new Abstract: AI systems can increasingly automate scientific workflows, but the reasoning that links prior evidence, generated ideas, experiments and final claims often remains implicit inside model inference.
By Zijian Wang, Hanqi Li, Ziyue Yang, Zijian Hu, Shenghan Zuo, Yunzhe Zhang, Da Ma, Danyu Luo, Chenrun Wang, Jing Peng, Tiancheng Huang, Sijia Guo, Huayang Wang, Zichen Zhu, Senyu Han, Yilu Cao, Kai Yu, Lu Chen