CR^2: Cost-Aware Risk-Controlled Routing for Wireless Device-Edge LLM Inference
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. In this paper, we formulate mobile edge LLM routing as a deployment-constrained, cost-aware decision problem, and propose CR^2, a two-stage device-edge routing f...
| Authors | Nan Xue, Shengkang Chen, Zhiyong Chen et al. |
|---|---|
| Primary category | cs.IT |
| Published | 12 May 2026 |
| Last updated at source | 12 May 2026 |
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