GraphMind: From Operational Traces to Self-Evolving Workflow Automation
GraphMind is a new system designed to automate complex operational workflows without human input. It constructs, executes, and evolves workflow graphs by extracting structured data from human resolution traces. The system has shown significant improvements in performance metrics when evaluated on production data.
- ▪GraphMind automates the construction and execution of workflow graphs, reducing the need for human intervention.
- ▪The system operates in three phases: extracting workflow graphs, executing workflows with a multi-agent engine, and self-optimizing through Adaptive Traversal Reinforcement.
- ▪In evaluations, GraphMind outperformed a baseline system in terms of mitigation reach, groundedness, and diagnostic throughput.
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Computer Science > Artificial Intelligence arXiv:2605.17617 (cs) [Submitted on 17 May 2026] Title:GraphMind: From Operational Traces to Self-Evolving Workflow Automation Authors:Yiwen Zhu, Joyce Cahoon, Anna Pavlenko, Qiushi Bai, Nima Shahbazi, Divya Vermareddy, Meina Wang, Mathieu Demarne, Swati Bararia, Wenjing Wang, Hemkesh Vijaya Kumar, Hannah Lerner, Katherine Lin, Steve Toscano, Miso Cilimdzic, Subru Krishnan View a PDF of the paper titled GraphMind: From Operational Traces to Self-Evolving Workflow Automation, by Yiwen Zhu and 15 other authors View PDF HTML (experimental) Abstract:Complex operational workflows coordinating personnel, tools, and information are central to enterprise operations, yet end-to-end automation remains challenging due to extensive requirements for human…
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