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Recent work has shown that probabilistic flow matching for time series forecasting benefits from a data-matched prior. The resulting prior introduces local correlations, which a sequential architecture usually absorbs: a recurrent neural network (RNN), a structured state-space model (S4), or a Transformer. However, suc...
Large-scale neural recommender systems are typically trained with a softmax cross-entropy objective over the full item vocabulary. For a typical large number of possible items $K$, the final classification layer dominates memory, requiring $O(nK)$ logits and gradients to materialize for a batch of $n$ examples. Sampled...
A GENerative ADversarial Attack (GENADA) is proposed that learns a generative model to produce deceptive perturbations directly in a single forward pass and a procedure to train it, and achieves comparable attack quality to strong baselines while requiring less time to generate perturbations during inference.
This work proposes CLaST, a VAE framework for probabilistic multivariate time series forecasting that learns embeddings that preserve contextual similarity between observations through the authors' contrastive loss function.
INTRYGUE (Induction-Aware Entropy Gating for Uncertainty Estimation), a training-free, mechanistically grounded method that gates predictive entropy by an attention-based estimate of induction-head activity, is proposed, suggesting that hallucination detection in RAG benefits from combining predictive uncertainty with...
DecoVAE is proposed, a lightweight interpretable trend-seasonal VAE framework that explicitly decomposes time series into trend and seasonal components by applying domain-specific inductive biases.
BEAR-Bench (Bilingual Enterprise and Academic Reasoning), a self-contained, complex English-and-Russian benchmark comprising 1000 human-annotated questions based on text-rich business and scientific documents, is introduced, and existing hallucination detection methods are compared.
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