Academic
arXiv:2602.16833v1 Announce Type: new Abstract: Exploration remains a key bottleneck for reinforcement learning (RL) post-training of large language models (LLMs), where sparse feedback and large …
arXiv:2602.16837v1 Announce Type: new Abstract: Transformer models systematically favor certain token positions, yet the architectural origins of this position bias remain poorly understood. Under causal …
arXiv:2602.16839v1 Announce Type: new Abstract: Large reasoning models (LRMs) excel on complex problems but face a critical barrier to efficiency: reinforcement learning (RL) training requires …
arXiv:2602.16842v1 Announce Type: new Abstract: We study the offline data-driven newsvendor problem with censored demand data. In contrast to prior works where demand is fully …
arXiv:2602.16849v1 Announce Type: new Abstract: We present a comprehensive analysis of how two-layer neural networks learn features to solve the modular addition task. Our work …
arXiv:2602.16864v1 Announce Type: new Abstract: Time series (TS) modeling has come a long way from early statistical, mainly linear, approaches to the current trend in …
arXiv:2602.16876v1 Announce Type: new Abstract: Modern datasets often contain ballast as redundant or low-utility information that increases dimensionality, storage requirements, and computational cost without contributing …
arXiv:2602.16887v1 Announce Type: new Abstract: To build a dementia classification model for middle-aged and elderly Brazilians, implemented in Python, combining variable selection and multivariable analysis, …
arXiv:2602.16944v1 Announce Type: new Abstract: This work introduces a verification framework that provides both sound and complete guarantees for data poisoning attacks during neural network …
arXiv:2602.16947v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have become essential in high-stakes domains such as drug discovery, yet their black-box nature remains a …
arXiv:2602.16954v1 Announce Type: new Abstract: We challenge black-box purely deep neural approaches for molecules and graph generation, which are limited in controllability and lack formal …
arXiv:2602.16965v1 Announce Type: new Abstract: We study the decentralized multi-player stochastic bandit problem over a continuous, Lipschitz-structured action space where hard collisions yield zero reward. …
