AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Latent reasoning replaces natural-language chains of thought with compact continuous"soft-token"representations, reducing the number of intermediate tokens that must be decoded autoregressively. We use a small external encoder to induce latent reasoning in a frozen backbone model. The encoder iteratively maps the backb...
Complex reasoning queries can be decomposed into directed acyclic task graphs and distributed across heterogeneous LLMs, reducing latency through parallelism and enabling smaller models to solve complex tasks. In practice, however, the suitability of an LLM for a given subtask may be a priori unknown, and execution alo...
This work introduces TWISTER, the first automaton-twisted Sequential Monte Carlo decoder for MDLMs, and proves that for regular language constraints, the Feynman-Kac correction is exactly computable, with the twists obtained efficiently using quantities pre-computed for step-exact sampling.
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