AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Looped Transformers apply the same layers several times per token, adding compute to improve quality without more parameters. Each recursion, however, writes its own key-value cache, so memory still grows with compute. Inference-time techniques can shrink this cache at a cost in quality. We pretrain looped language mod...
This paper takes state--prediction separation to its limit with a free pause token: a prediction stream that writes no keys or values at all and so rides the sequence's existing positions, and reduces the raw flops required at inference time.
C$^{3}$T (Counterfactual Causal Conversation Transformer), a thread-structured temporal model that jointly predicts node sentiment and shifts, learns sparse ancestor attribution, and supports counterfactual queries by forcing conversational intervention embeddings on or off to estimate potential outcomes, is proposed.
This paper improves both training and inference efficiency by stacking sub-models of increasing size into a single nested architecture trained end-to-end in a Matryoshka training framework that reduces the total parameter count, enables low-cost distillation from the largest to all smaller sub-models at every training...
Kevo is a co-evolving framework that jointly learns to construct a structured knowledge base and reason over it for knowledge-intensive question answering, which leads to larger, better-connected knowledge structures with higher answer reachability, while also improving compositional factual reasoning and controllabili...
This work proposes continuous-query LMLM (CO-LMLM), where the KB pairs continuous keys with textual knowledge values, a significant departure from prior reliance on relational KB and queries, which outperforms prior LMLMs and vanilla LLMs in both perplexity and factual precision.
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