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arXiv:2603.18492v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models increase parameter capacity without proportional per-token compute, but the deployment still requires storing all experts, making …
arXiv:2603.18533v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have shown exceptional reasoning capabilities, but they also suffer from the issue of overthinking, often generating …
arXiv:2603.18534v1 Announce Type: new Abstract: Synthetic data augmentation has emerged as a promising solution when pre-training is constrained by data rather than compute. We study …
arXiv:2603.18538v1 Announce Type: new Abstract: Decentralized Federated Learning (DFL) remains highly vulnerable to adaptive backdoor attacks designed to bypass traditional passive defense metrics. To address …
arXiv:2603.16878v1 Announce Type: new Abstract: Foundation models have recently extended beyond natural language and vision to timeseries domains, including physiological signals. However, progress in electrodermal …
arXiv:2603.16881v1 Announce Type: new Abstract: Multi-agent deep learning (MADL), including multi-agent deep reinforcement learning (MADRL), distributed/federated training, and graph-structured neural networks, is becoming a unifying …
arXiv:2603.16888v1 Announce Type: new Abstract: Dynamic pricing in competitive retail markets requires strategies that adapt to fluctuating demand and competitor behavior. In this work, we …
arXiv:2603.16917v1 Announce Type: new Abstract: Sequence modeling universally relies on discrete subword tokenization to circumvent the $\mathcal{O}(N^2)$ computational intractability of native byte-level attention. However, this …
arXiv:2603.16929v1 Announce Type: new Abstract: Regulating the importance ratio is critical for the training stability of Group Relative Policy Optimization (GRPO) based frameworks. However, prevailing …
arXiv:2603.16937v1 Announce Type: new Abstract: Sleep quality is influenced by a complex interplay of behavioral, environmental, and psychosocial factors, yet most computational studies focus mainly …
arXiv:2603.16951v1 Announce Type: new Abstract: Identifying physical laws from noisy observational data is a central challenge in scientific machine learning. We present Minimum-Action Learning (MAL), …
arXiv:2603.16983v1 Announce Type: new Abstract: Machine learning models for geotechnical hazard prediction can achieve high accuracy while learning physically inconsistent relationships from sparse or biased …
