arXiv:2603.08682v2 Announce Type: replace-cross Abstract: We introduce structural causal bottleneck models (SCBMs), a novel class of structural causal models. At the core of SCBMs lies the assumption that causal effects between high-dimensional variables only depend on low-dimensional summary statistics, or…
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 logic, leaving them susceptible to spurious correlations and shortcut learning. Conversely, traditional rule-based inference systems…
arXiv:2603.08206v3 Announce Type: replace Abstract: Tabular foundation models such as TabPFN and TabICL already produce full predictive distributions, yet the benchmarks used to evaluate them (TabArena, TALENT, and others) still rely almost exclusively on point-estimate metrics (RMSE, $R^2$). This mismatch…
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 transition dynamics are subsequently learned. However, this conventional paradigm is mathematically flawed: it merely displaces…
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 present Federated Targeted Recommendations with Evolving Knowledge graphs and Language Models (FedTREK-LM), a framework that…
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 struggle to model the critical information embedded in the temporal evolution of projects, failing to…
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 and, for each such individual, observations of a time-dependent variable made at various times. Generically,…
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 are no longer available. Existing approaches fall into two categories which include input- and parameter-adaptation.…
arXiv:2510.01116v2 Announce Type: replace Abstract: Complex numerical time series analysis often demands multi-step reasoning capabilities beyond current models’ reach. Tasks like medical diagnosis and weather forecasting require sequential reasoning processes – including counterfactual analysis, logical deduction, knowledge application, and multi-modal…
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 changes in channel composition or ordering. We propose channel-aware multimodal EEG-text alignment contrastive language-image pretraining…
