Study: Immigrants help address the US eldercare shortage
Economists find that in metro areas with more immigration, nurses are spending more time with elderly patients.
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MoRFI: Monotonic Sparse Autoencoder Feature Identification
arXiv:2604.26866v1 Announce Type: cross Abstract: Large language models (LLMs) acquire most of their factual knowledge during the pre-training stage, through next token prediction. Subsequent stages of post-training often introduce new facts outwith the parametric knowledge, giving rise to hallucinations. While…
The Alignment Flywheel: A Governance-Centric Hybrid MAS for Architecture-Agnostic Safety
arXiv:2603.02259v2 Announce Type: replace-cross Abstract: Multi-agent systems provide mature methodologies for role decomposition, coordination, and normative governance, capabilities that remain essential as increasingly powerful autonomous decision components are embedded within agent-based systems. While learned and generative models substantially expand system…
A Randomized PDE Energy driven Iterative Framework for Efficient and Stable PDE Solutions
arXiv:2604.25943v1 Announce Type: new Abstract: Efficient and stable solution of partial differential equations (PDEs) is central to scientific and engineering applications, yet existing numerical solvers rely heavily on matrix based discretizations, while learning based methods require costly training and often…
FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projection
arXiv:2604.24012v2 Announce Type: replace Abstract: Federated learning enables a population of clients to collaboratively train machine learning models without exchanging their raw data, but standard algorithms such as FedAvg suffer from slow convergence and high communication and memory costs in…
Out-of-Distribution Generalization of In-Context Learning: A Low-Dimensional Subspace Perspective
arXiv:2505.14808v2 Announce Type: replace-cross Abstract: The transformer’s remarkable ability to perform in-context learning (ICL) has sparked a wide range of studies designed to understand its strengths and limitations. However, a theoretical understanding of when ICL can and cannot generalize beyond…
RoseCDL: Robust and Scalable Convolutional Dictionary Learning for Rare event and Anomaly Detection
arXiv:2509.07523v4 Announce Type: replace Abstract: Detecting rare events and anomalies in large-scale signals is essential in fields such as astronomy, physical simulations, and biomedical science. In many cases, this problem naturally decomposes into identifying common local patterns and detecting deviations…
Causally Sufficient and Necessary Feature Expansion for Class-Incremental Learning
arXiv:2603.09145v3 Announce Type: replace Abstract: Current expansion-based methods for Class Incremental Learning (CIL) effectively mitigate catastrophic forgetting by freezing old features. However, such task-specific features learned from the new task may collide with the old features. From a causal perspective,…
RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts
arXiv:2604.26039v1 Announce Type: new Abstract: The optimal kernel configuration for Mixture-of-Experts (MoE) inference depends on both batch size and the expert routing distribution, yet production systems dispatch from batch size alone, leaving 10-70% of kernel throughput unrealized. We present RaMP,…
Observable Neural ODEs for Identifiable Causal Forecasting in Continuous Time
arXiv:2604.26070v1 Announce Type: new Abstract: Causal inference in continuous-time sequential decision problems is challenged by hidden confounders. We show that, in latent state-space models with time-varying interventions, observability of the latent dynamics from observed data is necessary for identifying dynamic…