STELLA: Self-Evolving LLM Agent for Biomedical Research

• URL: http://arxiv.org/abs/2507.02004v1
• Date: Tue, 01 Jul 2025 20:52:01 GMT
• Title: STELLA: Self-Evolving LLM Agent for Biomedical Research
• Authors: Ruofan Jin, Zaixi Zhang, Mengdi Wang, Le Cong,
• Abstract summary: We introduce STELLA, a self-evolving AI agent designed to overcome limitations.<n> STELLA employs a multi-agent architecture that autonomously improves its own capabilities.<n>We demonstrate that STELLA achieves state-of-the-art accuracy on a suite of biomedical benchmarks.
• Score: 40.841136388072385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid growth of biomedical data, tools, and literature has created a fragmented research landscape that outpaces human expertise. While AI agents offer a solution, they typically rely on static, manually curated toolsets, limiting their ability to adapt and scale. Here, we introduce STELLA, a self-evolving AI agent designed to overcome these limitations. STELLA employs a multi-agent architecture that autonomously improves its own capabilities through two core mechanisms: an evolving Template Library for reasoning strategies and a dynamic Tool Ocean that expands as a Tool Creation Agent automatically discovers and integrates new bioinformatics tools. This allows STELLA to learn from experience. We demonstrate that STELLA achieves state-of-the-art accuracy on a suite of biomedical benchmarks, scoring approximately 26\% on Humanity's Last Exam: Biomedicine, 54\% on LAB-Bench: DBQA, and 63\% on LAB-Bench: LitQA, outperforming leading models by up to 6 percentage points. More importantly, we show that its performance systematically improves with experience; for instance, its accuracy on the Humanity's Last Exam benchmark almost doubles with increased trials. STELLA represents a significant advance towards AI Agent systems that can learn and grow, dynamically scaling their expertise to accelerate the pace of biomedical discovery.

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