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Under a local linearization of the WiFi forward process, a common task-observability condition is derived under which a strict common linear representation is recoverable from every geometry-induced operator while preserving the gesture task.
This work presents Pallas, a \textit{proactive} KV-cache migration framework that prepares the inference state at the predicted target before handover, in parallel with ongoing source-side inference and token delivery.
FeDiSyn is proposed, a unified framework that holistically considers the interplay between pre-training and FedFT to minimize the overall LVM training time and introduces a scaling law for FedFT pre-training to determine the optimal number of synthetic images, balancing pre-training benefit against generation/pre-train...
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