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Boundary ablations show that retrieving a small set of input-relevant rules preserves performance close to using all boundaries with substantially less context, supporting efficient reuse of flagship-generated label knowledge for lightweight classification.
Large language models are increasingly deployed in IoT, mobile, and edge-assisted scenarios, where inference is constrained by limited computation, GPU memory capacity, heterogeneous hardware, network conditions, and cloud invocation costs. Existing selection and offloading methods mainly focus on coarse-grained model...
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