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KV caches make long-context serving efficient by reusing past computation, but model switching breaks this reuse and forces the receiver to prefill the context again. We study \emph{zero-prefill cross-model context handoff}: translating a sender cache into a receiver-compatible state so that the receiver can continue d...
The proposed TDDM-Melatt, a disentangled memory-based traffic classification framework with diffusion-based data augmentation, provides a new and effective technical pathway for encrypted traffic classification in real-world network environments.
This work proposes GMoT, a Gated Motion-Aware Tokenization module that explicitly distills sparse kinematic evidence into a compact sequence prior to temporal modeling, and introduces Body-Region Grounding (BRG) Recall as an anatomical-grounding proxy conditioned on correct predictions.
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