A device–server split of recurrent latent LLM reasoning plus semantic MoE-SAC scheduling yields about 18% higher simulated system throughput than plain SAC under energy, recurrence, and latency budgets.
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MORES: Mobile Reasoning-as-a-Service via Distributed LLM Inference-Time Scaling
A device–server split of recurrent latent LLM reasoning plus semantic MoE-SAC scheduling yields about 18% higher simulated system throughput than plain SAC under energy, recurrence, and latency budgets.