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The proposed QCATS is a query context-aware transformer slicing framework that enables efficient sparse inference inside database systems and achieves up to 4.42x latency reduction while preserving prediction accuracy comparable to dense baselines.
Autonomous agents increasingly rely on external data to complete downstream tasks such as model training and decision support. However, existing data discovery systems remain largely retrieval-oriented: they surface candidate datasets from heterogeneous sources, but provide limited support for estimating task-specific...
This study identifies depth-dependent prefix redundancy in final-readout LLM embedding models, notably across representative backbones including Qwen3-Embedding and Qwen3-VL-Embedding, and introduces FastE, a training-free, plug-and-play method that offers practical value for scalable embedding generation in retrieval,...
This work proposes ScaleSense, a proactive, query-level resource scaling framework that addresses the critical performance-cost trade-off while maintaining low-overhead inference latency, and confirms its practical performance in production deployments.
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