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Lingkun Li

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Book Open access Aug 2026

DSETA: A Dual-Stage Continual Learning Framework for Travel Time Prediction in Dynamic Traffic Environments

This work designs and develops an incrementally updated Dual-Stage ETA prediction framework that has been successfully deployed in DiDi's production environment, processing hundreds of millions of daily requests and validating its strong performance in industrial applications.

Yanming Lyu, Yue Cheng, Lingkun Li et al. · 0 citations
Preprint Jul 2026

Is Your NPU Ready for LLMs? Dissecting the Hidden Efficiency Bottlenecks in Mobile LLM Inference

This study presents the first comprehensive, cross-layer measurement study of mobile LLM inference, uniquely spanning five mainstream frameworks and three hardware backends, and identifies a distinct phase split where NPUs excel at compute-bound prefilling, while CPUs outperform all other backends in memory-bound decod...

Guanyu Cai, Ruiming Tian, Lang Yang et al. · 0 citations

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