EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale
The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. However, common agent infrastructure is repeatedly rebuilt across scientific fields (loop fragmentation) and useful evidence and experience are lost in long-horizon research (loop discontinuity), bringing obstacles to Agentic Science at Scale. We introduce EvoMaster, a foundational evolving agent framework for Agentic Science at Scale. EvoMaster handles loop fragmentation and loop discontinuity by implementing Loop Research in which external evidence persists and improves later decisions. Through three nested loops, Execution, Exploration and Evolution (E$^3$), loop resear...
| Authors | Xinyu Zhu, Yuzhu Cai, Zexi Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 19 April 2026 |
| Last updated at source | 10 September 2026 |
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