OpenAI4S is an open‑source scientific research agent that treats code as action and science as sessions, combining a persistent computing runtime with structured session management. It uses tool calls for orchestration, executes code cells in persistent Python and R kernels, and records an append‑only Action Ledger, per‑cell execution logs, versioned artifacts, environment snapshots, and workspace checkpoints to preserve provenance and enable session recovery, branching, and extension. Evaluated on 36 research scenarios—including retrosynthesis, molecular dynamics, and protein design—OpenAI4S achieved a higher overall score (7.83) than a general‑purpose coding harness, especially on long‑horizon, computation‑intensive workflows, though reproducibility remains an open challenge.
whyItMatters":"The system demonstrates that persistent execution coupled with session‑level provenance can enhance the reliability of AI‑assisted scientific workflows, as evidenced by its superior performance across diverse research scenarios."
By Gongbo Zhang, Hao Li, Yu Wang, Mujie Lin, Liuzhenghao Lv, Yicheng Mao, Yimi Wang, Jun Zhu, Minhan Tang, Zhengxiang Jiang, Yusong Wang, Jiayu Yao, Kunpeng Ning, Dawei Pang, Yonghong Tian, OpenAI4S Community, Yuyang Liu, Li Yuan
OSWorld-Science is a benchmark and evaluation environment for computer-using agents that use visual language models (VLMs) to perform scientific software tasks. It includes 12 VLMs and 146 high-quality tasks across domains such as molecular drawing, pathology image analysis, statistical computing, and physical simulation, with artifact-based evaluation and a harness that logs interactions and supports model comparison. The benchmark was developed through expert proposals and iterative human–AI co‑design, and results show that current VLMs still struggle with key scientific questions, offering insights into factors like language, reasoning, and context length.
By Dingyuan Dai, Heli Qi, Lei Liu, Yinxi Li, Baiding Chen, Zijun Dou, Qingcheng Zeng, Qi Kang, Oliver Sun, Eric Wang, Bo Zhou, Haixin Wang, Yufan Du, Shi Bo, Ruihan Lin, Mengqi Yuan, Dunjie Lu, Steven Dillmann, Yiming Shi, Tina Su, Amy Xin, Minghao Liu, Xi Wang, Xu Huang, Ge Zhang, Pengyu Nie, Zhen Yang, Jie Tang, Juanzi Li, Weihao Xuan, Tianyu Liu
arXiv:2509. 23426v3 Announce Type: replace Abstract: AI scientists are emerging computational systems that serve as collaborative partners in discovery.
By Shanghua Gao, Richard Zhu, Pengwei Sui, Zhenglun Kong, Sufian Aldogom, Yepeng Huang, Ayush Noori, Reza Shamji, Krishna Parvataneni, Theodoros Tsiligkaridis, Marinka Zitnik
arXiv:2603. 01421v3 Announce Type: replace Abstract: While large language models accelerate scientific discovery, existing agents face severe limitations in adaptability, domain generalization, and multimodal scalability, often struggling to autonomously process raw, domain-specific experimental data.
By Ke Lin, Owais Aijaz, Yilin Lu, Yiyang Luo, Xuehang Guo, Preslav Nakov
arXiv:2605. 06177v2 Announce Type: replace Abstract: Reproducing and comparing deep research agents today is hard: the same backbone evaluated on the same benchmark can report different accuracies across papers because the harness and tool registry differ, and integrating a new model into a comparable evaluation surface costs weeks of model-specific engineering.
By Jinge Wu, Hongjian Zhou, Mingde Zeng, Jiayuan Zhu, Junde Wu, Jiazhen Pan, Ayush Noori, Sean Wu, Honghan Wu, Fenglin Liu, David A. Clifton
arXiv:2606. 30246v1 Announce Type: new Abstract: Existing autonomous research agents can support parts of the research process, but most systems still treat research as either an isolated assistant task or a closed workflow.
By Zihan Guo, Zeyi Chen, Zhiyu Chen, Zicai Cui, Shuai Shao, Bo Huang, Zhi Han, Yuanyi Song, Yuan Yuan, Chenxi Zeng, Xiaohang Nie, Zhengxi Yu, Hanwen Zhu, Junwei Liao, Ming Zhou, Yang Li, Yuanjian Zhou, Weinan Zhang
arXiv:2606. 21005v2 Announce Type: replace Abstract: Scientific discovery workflows often depend on structured curation from the literature.
By Sheng Zhang, Qin Liu, Renqian Luo, Shufang Xie, Reuben Tan, Sean Hayes, Gregory Bryman, Wendong Ge, Ruilian Zhang, Oluwaseun Egbelowo, Kelly Yee, Hoifung Poon
arXiv:2606. 13662v1 Announce Type: new Abstract: LLM-based agents have shown increasing potential in automating scientific discovery.
By Amy Xin, Jiening Siow, Junjie Wang, Zijun Yao, Fanjin Zhang, Jian Song, Lei Hou, Juanzi Li
arXiv:2607. 02703v1 Announce Type: cross Abstract: In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a LiteLLM/MLflow control plane for authentication, budgeting, PII masking, and observability, and an application augmentation layer for AI coding agents.
By Landung Setiawan, Anant Mittal, Cordero Core, Anshul Tambay, Carlos Garcia Jurado Suarez, David A. C. Beck, Andrew J. Connolly, Vani Mandava
arXiv:2606. 26614v1 Announce Type: cross Abstract: Large language model (LLM) agents enable natural language interaction for scientific visualization (SciVis).
By Kuangshi Ai, Patrick Phuoc Do, Chaoli Wang
arXiv:2607. 22677v1 Announce Type: cross Abstract: Scientific datasets intended for AI use require both computational readiness for model training and metadata readiness for discovery, sharing, and reuse.
By Sean R. Wilkinson, Polina Shpilker, Wesley Brewer
arXiv:2605. 18661v2 Announce Type: replace Abstract: AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and simulate critique with minimal human input.
By Lingdong Kong, Xian Sun, Wei Chow, Linfeng Li, Kevin Qinghong Lin, Xuan Billy Zhang, Song Wang, Rong Li, Qing Wu, Wei Gao, Yingshuo Wang, Shaoyuan Xie, Jiachen Liu, Leigang Qu, Shijie Li, Lai Xing Ng, Benoit R. Cottereau, Ziwei Liu, Tat-Seng Chua, Wei Tsang Ooi