AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work presents Token-Disentangled Latent Test-Time Scaling, an inference-time framework that makes latent refinement token-role-aware and outperforms strong output-space test-time scaling baselines under matched decoded-candidate budgets.
It is shown decoder-only MDMs, despite a larger modeling space, can achieve significant inference speedups and comparable perplexity with techniques like temperature annealing, offering a path to reduced inference compute.
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