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arXiv:2603.14443v1 Announce Type: new Abstract: This study examines phonetic texture in Persian poetry as a literary-historical phenomenon rather than a by-product of meter or a …
arXiv:2603.14456v1 Announce Type: new Abstract: Persian poses unique audio understanding challenges through its classical poetry, traditional music, and pervasive code-switching - none captured by existing …
arXiv:2603.13231v1 Announce Type: new Abstract: Transformer-based models have improved predictive modeling on longitudinal electronic health records through large-scale self-supervised pretraining. However, most EHR transformer architectures …
arXiv:2603.13234v1 Announce Type: new Abstract: Breiman and Cutler's original Random Forest was designed as a unified ML engine -- not merely an ensemble predictor. Their …
arXiv:2603.13235v1 Announce Type: new Abstract: Continual fine-tuning aims to adapt a pre-trained backbone to new tasks sequentially while preserving performance on earlier tasks whose data …
arXiv:2603.13254v1 Announce Type: new Abstract: We present a new algorithm for clustering longitudinal data. Data of this type can be conceptualized as consisting of individuals …
arXiv:2603.13258v1 Announce Type: new Abstract: While "Intent-oriented programming" (or "Vibe Coding") redefines software engineering, existing code agents remain tethered to static code snapshots. Consequently, they …
arXiv:2603.13263v1 Announce Type: new Abstract: The pursuit of world model based artificial intelligence has predominantly relied on projecting high-dimensional observations into parameterized latent spaces, wherein …
arXiv:2603.13264v1 Announce Type: new Abstract: Personalized recommendation increasingly relies on private user data, motivating approaches that can adapt to individuals without centralizing their information. We …
arXiv:2603.13265v1 Announce Type: new Abstract: Modern self-supervised predictive architectures excel at capturing complex statistical correlations from high-dimensional data but lack mechanisms to internalize verifiable human …
arXiv:2603.13272v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models have shown promise for learning generalizable representations, yet they remain sensitive to channel heterogeneity, such as …
arXiv:2603.13273v1 Announce Type: new Abstract: Microclimate models are essential for linking climate to ecological processes, yet most physically based frameworks estimate temperature independently for each …