Continual Enterprise World Model Discovery in Dynamic Systems
Read the original on arXiv AI →The paper introduces a method for continual enterprise world model discovery, enabling an agent to learn and adapt to business rules in dynamic systems without prior knowledge. Using a ServiceNow environment called EnterpriseWorldShift, the authors evaluate their Continual Discovery Agent (CDA) across four rule-modification scenarios—discovery, revision, extension, and retirement—showing that CDA predicts rule effects more accurately than lookup-based approaches, improving IoU by up to 8.98 points. The agent can answer queries from its internal model without querying the live system.
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