AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper introduces ProLaViT (Progressive Latent Visual Thought), a framework empowering MLLMs to perform structured visual derivation in the continuous latent space, leveraging an endogenous self-distillation mechanism, utilizing the model's own visual encoder to supervise latent thoughts.
The proposed NeuroStat framework captures uncompressed token-level probabilistic logits alongside deep semantic hidden states from a single causal language model backbone through Macro-State Residual Modulation, which adaptively calibrates local convolutional features using global uncertainty indicators.
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