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M-LoRA is proposed, a memory-aware multi-LoRA serving system that reduces queuing delay and improves throughput through efficient request scheduling guided by fine-grained memory modeling.
This paper investigates LVLMs’ behavior in VAD from a visual-textual co-occurrence perspective, and proposes VAD-DPO, a direct preference optimization method supervised with counter-example pairs that enhances both anomaly detection and reasoning performance, particularly in scene-dependent scenarios.
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