AI RESEARCH
INAR-VL: Input-Aware Routing for Edge-Cloud Vision-Language Inference
arXiv CS.LG
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ArXi:2605.18853v1 Announce Type: new Edge deployment of Vision-Language Models (VLMs) faces a tradeoff between latency and accuracy: cloud execution provides high-quality predictions but incurs communication delay and energy cost, while edge-only execution is faster but less accurate due to limited model capacity. This trade-off is further complicated by heterogeneity in image quality and reasoning complexity, making static placement suboptimal. We present INAR-VL, a lightweight edge-cloud routing system for multimodal inference in a two-tier deployment.