AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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SplitJEPA is introduced, a JEPA that jointly recovers the latent state and its invariant and variant organization directly in representation space, without any reconstruction, and extends reconstruction-free latent recovery to invariant-variant block identification.
A unified objective function for explainable time series learning that bridges attribution and counterfactual reasoning within a single framework is proposed and a novel explanation framework is introduced that learns a parametric transformation network to construct explanation-embedded instances.
TALRanker is a novel framework that formalizes pointwise relevance scoring as an agentic Markov decision process that achieves state-of-the-art performance across standard and reasoning-intensive retrieval benchmarks, matching throughput with pointwise rerankers while outperforming parameter-heavy reasoning models.
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