As artificial intelligence systems increasingly rely on multi-source perception and cross-modal learning for human-like visual understanding, their outputs should be both semantically valid and spatially grounded. However, large-scale vision-language benchmarks with precise grounding remain costly to construct, while existing synthetic pipelines often suffer from distribution drift, error propagation, and weak spatial verification. We introduce VQA-G Annotator, a self-improving agentic framework that combines distribution-aware planning, multi-agent orchestration, and spatial reasoning verification in a closed-loop generation, evaluation, and refinement process. Experiments across four benchmark sources show that VQA-G Annotator achieves an average VQAScore of 0.88 for semantic alignment and an Acc@0.5 of 0.57 for spatial grounding, comparing favorably with strong automatic baselines. Downstream and human evaluations further support the utility of the synthesized data 18 for scalable and trustworthy grounded visual understanding.

Load balancing is vital for the efficient and long-term operation of cloud data centers. With virtualization, post (reactive) migration of virtual machines (VMs) after allocation is the traditional way for load balancing and consolidation. However, it is not easy for reactive migration to obtain predefined load balance objectives and it may interrupt services and bring instability. Therefore, we provide a new approach, called Prepartition, for load balancing. It partitions a VM request into a few sub-requests sequentially with start time, end time and capacity demands, and treats each sub-request as a regular VM request. In this way, it can proactively set a bound for each VM request on each physical machine and makes the scheduler get ready before VM migration to obtain the predefined load balancing goal, which supports the resource allocation in a fine-grained manner. Simulations with real-world trace and synthetic data show that our proposed approach with offline version (PrepartitionOff) scheduling has 10%–20% better performance than the existing load balancing baselines under several metrics, including average utilization, imbalance degree, makespan and Capacity_makespan. We also extend Prepartition to online load balancing. Evaluation results show that our proposed approach also outperforms state-of-the-art online algorithms.