LabAgent: Customize Any Research Hubs for Scientific Discoveries Using AI Agents
Abstract
Scientific research is a continuous process that emphasizes inheritance. Methods developed by predecessors are often expanded upon by new researchers to explore more novel and in-depth scientific questions. However, the change of lab staff, such as student graduation, leads to a lack of personnel capable of replicating methods. Methods that have been developed with significant effort and resources cannot be continued. To address these limitations, we propose LabAgent, a reproduce and discovery harness tailored for a lab's continuous work. LabAgent employs two mechanisms to guarantee that all skills can be executed and verified and to record the corrective methods and experiences, allowing for direct correction or avoidance of similar errors. We applied LabAgent to drug property prediction, biomedical problem analysis, protein variant effect prediction, and statistical genetics in life science domains. LabAgent ranks first over commercial generalist agents in every domain, and demonstrates accurate reproduction of a published figure. Overall, these results demonstrate that LabAgent can effectively integrate and reasonably expand laboratory knowledge.
Related
- Scientific criteria and guidelines for identifying, reporting and interpreting omics data – transcriptomics, proteomics, metabolomics and lipidomics – in meat science research
- Report from the 2026 Barth Syndrome Foundation International Scientific, Medical & Family Conference: Advances in Barth Syndrome Research, Therapy Development, and Clinical Care.
- Authorship responsibilities of scientists who use AI research agents.
- Reply: Stem cell-based embryo models: scientific promise clashes with ethical reality
- Anti-Glioma Agents and Hyperbaric Oxygen for Prospective Therapeutic Approaches
BibTeX
@misc{liu2026labagent,
title = {{LabAgent: Customize Any Research Hubs for Scientific Discoveries Using AI Agents}},
author = {Lei Liu and Yikun Zhang and Jialin Chen and Wanjia Zhao and Rex Ying and Wengong Jin and Hua Xu and James Zou and Tianyu Liu and Hongyu Zhao},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.13437},
eprint = {2609.13437},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.13437},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - LabAgent: Customize Any Research Hubs for Scientific Discoveries Using AI Agents
AU - Lei Liu
AU - Yikun Zhang
AU - Jialin Chen
AU - Wanjia Zhao
AU - Rex Ying
AU - Wengong Jin
AU - Hua Xu
AU - James Zou
AU - Tianyu Liu
AU - Hongyu Zhao
PY - 2026
DA - 2026/09/11
PB - arXiv
DO - 10.48550/arxiv.2609.13437
UR - https://arxiv.org/abs/2609.13437
AB - Scientific research is a continuous process that emphasizes inheritance. Methods developed by predecessors are often expanded upon by new researchers to explore more novel and in-depth scientific questions. However, the change of lab staff, such as student graduation, leads to a lack of personnel capable of replicating methods. Methods that have been developed with significant effort and resources cannot be continued. To address these limitations, we propose LabAgent, a reproduce and discovery harness tailored for a lab's continuous work. LabAgent employs two mechanisms to guarantee that all skills can be executed and verified and to record the corrective methods and experiences, allowing for direct correction or avoidance of similar errors. We applied LabAgent to drug property prediction, biomedical problem analysis, protein variant effect prediction, and statistical genetics in life science domains. LabAgent ranks first over commercial generalist agents in every domain, and demonstrates accurate reproduction of a published figure. Overall, these results demonstrate that LabAgent can effectively integrate and reasonably expand laboratory knowledge.
ER -