Author

Zhu-Zhong Qian

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Conference Jul 2026

Computing-And-Power Cooperative Scheduling for Distributed AI Training at Scale

As AI training moves toward geo-distributed elastic infrastructures, its bottlenecks extend beyond GPU availability to variable energy and network conditions. The key challenge is translating heterogeneous placement, scaling, congestion, and training-stage effects into useful progress. We propose Effective Compute Util...

Yu-Xin Chen, Zhu-Zhong Qian, Lin Qian et al. · Fall Joint Computer Conferen... · 0 citations

#artificial intelligence Preprint Sep 2026

Failure-Guided Co-Evolution of Prompts and Training Data

ForGE is introduced, a failure-guided framework that co-evolves prompts and training data and establishes failures as a shared interface between prompt optimization and data synthesis, and shows the benefit of jointly adapting what a model is instructed to do and what it learns from

Tian-Yu Yuan, Zhu-Zhong Qian · 0 citations

Aug 2026

Learning to schedule: interference-aware optimization for edge AI inference on shared GPUs

A time-varying integer program to minimize the long-term total cost of the edge AI inference system, including the inference latency, the inference error rate, the query-dispatching communication cost, and the energy consumption, subject to resource and workload constraints is proposed.

Ming-Tao Ji, Hehan Zhao, Lei Jiao et al. · Science China Information Sc... · 0 citations