AI RESEARCH
CR^2: Cost-Aware Risk-Controlled Routing for Wireless Device-Edge LLM Inference
arXiv CS.AI
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ArXi:2605.12001v1 Announce Type: cross As large language models (LLMs) move from centralized clouds to mobile edge environments, efficient serving must balance latency, energy consumption, and accuracy under constrained device-edge resources. Query-level routing between lightweight on-device models and stronger edge models provides a flexible mechanism to navigate this trade-off. However, existing routers are designed for centralized cloud settings and optimize token-level costs, failing to capture the dynamic latency and energy overheads in wireless edge deployments.