BioCoLoop: Collaborative Agentic Research for Biological Model Improvement

Artificial intelligence is increasingly used to automate biological model development. Recent self-improving agents extend this process by iteratively proposing model changes, training and evaluating them, and using the observed results to guide subsequent proposals. However, biological data are often distributed across laboratories and cannot be centrally pooled, making this iterative process difficult to apply. We introduce BioCoLoop, a collaborative research framework that extends collaboration from parameter fitting to model development itself. BioCoLoop collaboratively trains and evaluates proposed model structures and training hyperparameters across laboratories, and uses the resulting evaluation results to determine what should be tested next and which models should receive further training. Across drug-target interaction prediction, proteomic efficacy prediction, and cell perturbation identification, BioCoLoop consistently improves predictive performance. We further show that early evaluation results can guide which models continue training, improving performance under the same training and evaluation budget.