AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This approach substantially reduces the computation of long-context modeling, while enhancing the performance of the baseline model, achieving a speedup of 1.8$\times to 2.5$\times compared to the baseline VLLM.
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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