arXiv:2602.21036v1 Announce Type: cross Abstract: Conditional independence tests (CIT) are widely used for causal discovery and feature selection. Even with false discovery rate (FDR) control procedures, they often fail to provide frequentist guarantees in practice. We highlight two common failure…
arXiv:2602.20224v1 Announce Type: new Abstract: The rapid expansion of biomedical publications creates challenges for organizing knowledge and detecting emerging trends, underscoring the need for scalable and interpretable methods. Common clustering and topic modeling approaches such as K-means or LDA remain…
arXiv:2412.06871v2 Announce Type: replace Abstract: In the context of rail transit operations, real-time passenger flow prediction is essential; however, most models primarily focus on normal conditions, with limited research addressing incident situations. There are several intrinsic challenges associated with prediction…
arXiv:2602.20232v1 Announce Type: new Abstract: Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond…
arXiv:2505.19193v4 Announce Type: replace Abstract: Real-world temporal data often consists of multiple signal types recorded at irregular, asynchronous intervals. For instance, in the medical domain, different types of blood tests can be measured at different times and frequencies, resulting in…
arXiv:2602.20271v1 Announce Type: new Abstract: Accurate delivery delay prediction is critical for maintaining operational efficiency and customer satisfaction across modern supply chains. Yet the increasing complexity of logistics networks, spanning multimodal transportation, cross-country routing, and pronounced regional variability, makes this…
arXiv:2509.23115v3 Announce Type: replace Abstract: Predicting human mobility is inherently challenging due to complex long-range dependencies and multi-scale periodic behaviors. To address this, we introduce RHYTHM (Reasoning with Hierarchical Temporal Tokenization for Human Mobility), a unified framework that leverages large…
arXiv:2602.20273v1 Announce Type: new Abstract: Large language models (LLMs) have been reported to linearly encode truthfulness, yet recent work questions this finding’s generality. We reconcile these views with the truthfulness spectrum hypothesis: the representational space contains directions ranging from broadly…
arXiv:2602.10693v2 Announce Type: replace Abstract: Training stability remains a central challenge in reinforcement learning (RL) for large language models (LLMs). Policy staleness, asynchronous training, and mismatches between training and inference engines all cause the behavior policy to diverge from the…
arXiv:2602.20293v1 Announce Type: new Abstract: We study a discrete denoising diffusion framework that integrates a sample-efficient estimator of single-site conditionals with round-robin noising and denoising dynamics for generative modeling over discrete state spaces. Rather than approximating a discrete analog of…
