Integrating Dynamical Systems Learning with Foundational Models: A Meta-Evolutionary AI Framework for Clinical Trials

Artificial intelligence (AI) is evolving into an ecosystem of specialized “species.” This manuscript compares two: DeepSeek-V3, a Mixture of Experts LLM, and NetraAI, a dynamical-system-based framework designed for small, structured datasets in clinical trials. We formalize NetraAI’s contraction-based learning architecture, its ability to efficiently discover causal bundles of variables, and introduce a meta-evolutionary loop where a large language model acts as a strategist, orchestrating NetraAI’s subpopulation discovery process. Across datasets in schizophrenia, depression, and pancreatic cancer, NetraAI uncovers high-effect subgroups defined by 2–4 variables—Personas—that significantly outperform LLMs and other machine learning methods at this task. This work advances explainable, agentic AI architectures and proposes a new design philosophy for interpretable, modular systems for clinical trial enrichment and derisking.