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arXiv:2603.15654v1 Announce Type: new Abstract: In classification tasks, the long-tailed minority classes usually offer the predictions that are most important. Yet these classes consistently exhibit …
arXiv:2603.15655v1 Announce Type: new Abstract: In decentralized Multi-Agent Reinforcement Learning (MARL), steganographic collusion -- where agents develop private protocols to evade monitoring -- presents a …
arXiv:2603.15656v1 Announce Type: new Abstract: The performance of neural network models deteriorates due to their unreliable behavior on non-robust features of corrupted samples. Owing to …
arXiv:2603.15678v1 Announce Type: new Abstract: Despite hundreds of millions of parameters, transformer training trajectories evolve within only a few coherent directions. We introduce \emph{Spectral Edge …
arXiv:2603.15681v1 Announce Type: new Abstract: Flash floods are the most destructive natural hazard in Himachal Pradesh (HP), India, causing over 400 fatalities and $1.2 billion …
arXiv:2603.15687v1 Announce Type: new Abstract: Accurate Remaining Useful Life (RUL) prediction without labeled target domain data is a critical challenge, and domain adaptation (DA) has …
arXiv:2603.15689v1 Announce Type: new Abstract: Mainstream flow matching methods typically focus on learning the local velocity field, which inherently requires multiple integration steps during generation. …
arXiv:2603.15696v1 Announce Type: new Abstract: Hypergraph neural networks (HGNNs) have demonstrated strong capabilities in modeling complex higher-order relationships. However, existing HGNNs often suffer from over-smoothing …
arXiv:2603.15708v1 Announce Type: new Abstract: Imbalanced data distribution remains a critical challenge in sequential learning, leading models to easily recognize frequent categories while failing to …
arXiv:2603.15713v1 Announce Type: new Abstract: Industrial financial systems operate on temporal event sequences such as transactions, user actions, and system logs. While recent research emphasizes …
arXiv:2603.15724v1 Announce Type: new Abstract: Existing test-time scaling (TTS) methods for unified multimodal models (UMMs) in text-to-image (T2I) generation primarily rely on search or sampling …
arXiv:2603.15797v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated exceptional logical reasoning capabilities but frequently struggle with the continuous spatiotemporal dynamics governed by …