STELLA: Self-Evolving LLM Agent for Biomedical Research

Modern biomedical research is defined by both immense opportunity and staggering complexity. As a cornerstone of science, it generates vast quantities of data from large-scale experiments, but this progress is hampered by a research landscape that is profoundly fragmented (1-3). The knowledge, specialized software, and databases required to make discoveries are numerous, constantly evolving, and dispersed, forcing researchers to expend significant time and effort on the manual and labor-intensive task of discovering, learning, and integrating these disparate resources. While the advent of AI agents holds the promise of automating this intricate work (4-6), current systems inherit a critical limitation: they typically rely on manually curated, static toolsets (7-14). This approach is inefficient, fails to scale, and cannot keep pace with the rapid evolution of biomedical science, leaving the agents perpetually behind the cutting edge. This raises a critical question: Can we design a self-evolving agent that transcends these limitations by automatically discovering and integrating new tools, continuously updating its knowledge base, and iteratively upgrading its own capabilities through direct experience? Here we present STELLA, a generalist biomedical AI agent designed around the core principle of self-evolution (15). STELLA learns and improves from every problem it solves, continuously enhancing its own reasoning strategies and technical abilities.