3 papers
cs.IR2026
Adaptive Model Compression (AMC): Saliency-Driven Resource Allocation for Ultra-Low-Power Transformer Inference
Jiayin Hu, Kai Yuan, Vanessa Hu +3
Deploying large-scale transformer models on resource-constrained edge devices remains a challenge due to the high energy and memory overhead inherent in static inference, which pro…
cs.IR2026
Unifying Ranking and Generation in Query Auto-Completion via Retrieval-Augmented Generation and Multi-Objective Alignment
Kai Yuan, Anthony Zheng, Jia Hu +9
Query Auto-Completion (QAC) suggests query completions as users type, helping them articulate intent and reach results more efficiently. Existing approaches face fundamental challe…
cs.IR2026
Scaling Search Relevance: Augmenting App Store Ranking with LLM-Generated Judgments
Evangelia Christakopoulou, Vivekkumar Patel, Hemanth Velaga +3
Large-scale commercial search systems optimize for relevance to drive successful sessions that help users find what they are looking for. To maximize relevance, we leverage two com…