BaseCamp is an agentic AI framework that automates the decision layer of DNA sequencing pipelines by deploying six specialized AI agents for tasks such as sample intake, quality control, alignment, variant calling, annotation, cross‑stage monitoring, and reporting. The agents rely on established bioinformatics tools for actual sequence analysis, while using fine‑tuned, domain‑specialized large language models to select, configure, and interpret these tools’ outputs, ensuring reproducibility and local data privacy. Evaluation demonstrates that the agents’ configurations align with expert practice, provide an explicit filtering ledger for traceability, and detect anomalies that traditional monitoring may miss.
By Eranga Bandara, Xueping Liang, Asanga Gunaratna, Tharaka Hewa, Abdul Rahman, Peter Foytik, Safdar H. Bouk, Sachini Rajapakse, Isurunima Kularathna, Pramoda Karunarathna, Chalani Rajapakse, Ng Wee Keong, Kasun De Zoysa, Amin Hass, Wathsala Herath, Ross Gore, Ravi Mukkamala, Nihal Siriwardanagea, Gihan Siriwardanagea, Aruna Withanage, Nilaan Loganathan, Sachin Shetty
arXiv:2601. 21800v4 Announce Type: replace Abstract: We introduce BioAgent Bench, an evaluation suite designed for measuring the performance and robustness of AI agents in common bioinformatics tasks.
By Dionizije Fa, Marko Culjak, Bruno Pandza, Mateo Cupic
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
arXiv:2507.21035v4 Announce Type: replace
Abstract: Gene expression analysis holds the key to many biomedical discoveries, yet extracting insights from raw transcriptomic data remains challenging due...
By Haoyang Liu, Yijiang Li, Haohan Wang
The Bioinfoysis Technical Report introduces a multi‑agent harness designed to improve long‑horizon bioinformatics tasks by maintaining persistent, artifact‑grounded analysis runs. It combines global planning with step‑wise, evidence‑driven replanning, ensuring intermediate results are tied to responsible agents and preventing stale evidence reuse. The system was evaluated on BixBench and LAB‑Bench 2, achieving state‑of‑the‑art accuracy and demonstrating that reliable bioinformatics automation relies on robust planning, execution, memory, and evidence flow.
By Qingyang Shao, Xin Zhang, Zhouyang Yuan, Xianying Chen, Yujia Xiang, Zihao Yang, Tong Ye, Yangqi Zhang, Jiakang Xu, Xiaoqing Yan, Xuan Luo, Keyi Li, Enci Fan, Kai Kang, Zhuohan Liu, Xingyu Jin, Chunran Teng, Tao Li, Xinyu Lv, Minghui Wang, Wenfeng Li, Yidan Gao, Siyu Liu, Mingrui Luo, Zhu Liang, Guanren Qiao, Zhiping Xu
arXiv:2610.16867v1 Announce Type: cross
Abstract: Reliable agentic tool use requires training data that pair diverse multi-step workflows with executable evidence that the requested outcomes were ach...
By Xiaoyang Yuan, Ji Zhao, Xingyu Hu, Wenhan Wang, Yutao Hu, Qiaosheng Zhang, Pengfei Liu, Yu Qiao, Yi Bin, Wenqi Shao