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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.AI2026
QUARK: Robust Retrieval under Non-Faithful Queries via Query-Anchored Aggregation
Rita Qiuran Lyu, Michelle Manqiao Wang, Lei Shi
User queries in real-world retrieval are often non-faithful (noisy, incomplete, or distorted), causing retrievers to fail when key semantics are missing. We formalize this as retri…