Autonomous Diagnostic Intelligence: A Large Reasoning Model Architecture for Multi- Dimensional KPI Anomaly Attribution, Counterfactual Impact Simulation, and Prescriptive Action Generation in Retail Enterprise Analytics

Abstract

Retail enterprises increasingly track performance through interconnected KPIs spanning sales, inventory, promotions, and customer behavior, yet most analytics systems are still better at detecting anomalies than explaining why they occurred or what action should follow. Research on anomaly reasoning, root-cause attribution, counterfactual explanation, and prescriptive analytics suggests that enterprise intelligence becomes more useful when diagnosis, simulation, and action design are treated as one reasoning problem. The main gap is the lack of architectures that can jointly perform multi-dimensional KPI anomaly attribution, counterfactual impact simulation, and prescriptive action generation in a unified retail analytics workflow. This matters because delayed or fragmented interpretation of KPI anomalies can lead to weak operational response and avoidable business loss. This article presents an autonomous diagnostic intelligence framework based on a large reasoning model for KPI anomaly attribution, counterfactual impact simulation, and prescriptive action generation in retail enterprise analytics. The results show improved attribution accuracy across reasoning iterations under increasing anomaly complexity, along with strong counterfactual recovery and prescriptive action effectiveness across retail decision scenarios. The study shows that large reasoning models can provide a stronger foundation for action-oriented retail diagnostic intelligence.