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arXiv:2603.23738v1 Announce Type: new Abstract: A major challenge of Reinforcement Learning is that agents often learn undesired behaviors that seem to defy the reward structure …
arXiv:2603.23746v1 Announce Type: new Abstract: Events in spatiotemporal domains arise in numerous real-world applications, where uncovering event relationships and enabling accurate prediction are central challenges. …
arXiv:2603.23755v1 Announce Type: new Abstract: Curriculum learning improves reinforcement learning (RL) efficiency by sequencing tasks from simple to complex. However, many self-paced curriculum methods rely …
arXiv:2603.23780v1 Announce Type: new Abstract: Large Language Models (LLMs) have introduced new capabilities to recommender systems, enabling dynamic, context-aware, and conversational recommendations. However, LLM-based recommender …
arXiv:2603.23783v1 Announce Type: new Abstract: Adapting large-scale foundation models to new domains with limited supervision remains a fundamental challenge due to latent distribution mismatch, unstable …
arXiv:2603.23784v1 Announce Type: new Abstract: Grokking-the phenomenon where validation accuracy of neural networks on modular addition of two integers rises long after training data has …
arXiv:2603.23792v1 Announce Type: new Abstract: Diffusion models often generate novel samples even when the learned score is only \emph{coarse} -- a phenomenon not accounted for …
arXiv:2603.23799v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) are increasingly used in mathematical epidemiology to bridge the gap between noisy clinical data and compartmental …
arXiv:2603.23805v1 Announce Type: new Abstract: Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended …
arXiv:2603.23823v1 Announce Type: new Abstract: Knowledge tracing models mastery over interconnected concepts, often organized by prerequisites. We analyze hierarchical prerequisite propagation through a circuit-complexity lens …
arXiv:2603.23831v1 Announce Type: new Abstract: Deep neural networks (DNNs), particularly those using Rectified Linear Unit (ReLU) activation functions, have achieved remarkable success across diverse machine …
arXiv:2603.23854v1 Announce Type: new Abstract: Symbolic discovery of governing equations is a long-standing goal in scientific machine learning, yet a fundamental trade-off persists between interpretability …
