A joint token masking and detection scheme with masked language models improves token reconstruction over noisy wireless channels by up to 1.77x on Europarl and 1.63x on WikiText-103 compared to conventional methods.
Communication-efficient hybrid language model via uncertainty- aware opportunistic and compressed transmission
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GELATO combines drift-plus-penalty Lyapunov control with generative entropy early exiting to adaptively offload tokens in device-edge speculative decoding, delivering higher throughput and lower energy use than prior distributed SD systems while preserving output quality.
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Context-Aware Wireless Token Communication via Joint Token Masking and Detection
A joint token masking and detection scheme with masked language models improves token reconstruction over noisy wireless channels by up to 1.77x on Europarl and 1.63x on WikiText-103 compared to conventional methods.
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GELATO: Generative Entropy- and Lyapunov-based Adaptive Token Offloading for Device-Edge Speculative LLM Inference
GELATO combines drift-plus-penalty Lyapunov control with generative entropy early exiting to adaptively offload tokens in device-edge speculative decoding, delivering higher throughput and lower energy use than prior distributed SD systems while preserving output quality.