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arXiv:2604.03606v1 Announce Type: new Abstract: Federated learning (FL) research increasingly relies on single-node simulations with hundreds or thousands of virtual clients, making both efficiency and …
arXiv:2604.03496v1 Announce Type: new Abstract: Knowledge graph construction typically relies either on predefined ontologies or on schema-free extraction. Ontology-driven pipelines enforce consistent typing but require …
arXiv:2604.04064v1 Announce Type: new Abstract: Small language models (SLMs) in the 100M-10B parameter range increasingly power production systems, yet whether they possess the internal emotion …
arXiv:2604.03582v1 Announce Type: new Abstract: Neural operators have emerged as data-driven surrogates for solving partial differential equations (PDEs), and their success hinges on efficiently modeling …
arXiv:2604.03321v1 Announce Type: new Abstract: Machine learning, especially physics-informed neural networks (PINNs) and their neural network variants, has been widely used to solve problems involving …
arXiv:2604.03675v1 Announce Type: new Abstract: In agentic search, large language models (LLMs) are trained to perform multi-turn retrieval and reasoning for complex tasks such as …
arXiv:2604.03820v1 Announce Type: new Abstract: Large language models are increasingly used for qualitative data analysis, but many workflows obscure how analytic conclusions are produced. We …
arXiv:2604.03541v1 Announce Type: new Abstract: This study surveys the historical development of regularization, tracing its evolution from stepwise regression in the 1960s to recent advancements …
arXiv:2604.03374v1 Announce Type: new Abstract: Creative problem-solving requires combining multiple cognitive abilities, including logical reasoning, lateral thinking, analogy-making, and commonsense knowledge, to discover insights that …
arXiv:2604.03641v1 Announce Type: new Abstract: Reinforcement learning in real-world systems is often accompanied by delayed feedback, which breaks the Markov assumption and impedes both learning …
arXiv:2604.03650v1 Announce Type: new Abstract: Multimodal Sentiment Analysis (MSA) requires effective modeling of cross-modal interactions and contextual dependencies while remaining computationally efficient. Existing fusion approaches …
arXiv:2604.03393v1 Announce Type: new Abstract: Multimodal reasoning has emerged as a powerful framework for enhancing reasoning capabilities of reasoning models. While multi-turn table reasoning methods …