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cs.AI2026
Experimentation Accelerator: Interpretable Insights and Creative Recommendations for A/B Testing with Content-Aware ranking
Zhengmian Hu, Lei Shi, Ritwik Sinha +2
Modern online experimentation faces two bottlenecks: scarce traffic forces tough choices on which variants to test, and post-hoc insight extraction is manual, inconsistent, and oft…
cs.AI2025
Evaluation and Incident Prevention in an Enterprise AI Assistant
Akash V. Maharaj, David Arbour, Daniel Lee +6
Enterprise AI Assistants are increasingly deployed in domains where accuracy is paramount, making each erroneous output a potentially significant incident. This paper presents a co…