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arXiv:2604.04998v1 Announce Type: new Abstract: This paper proposes a novel framework for enhancing the prediction accuracy and lead time of El Ni\~no events, crucial for …
arXiv:2604.05158v1 Announce Type: new Abstract: Large language models encode extensive world knowledge valuable for zero-shot named entity recognition. However, their causal attention mechanism, where tokens …
arXiv:2604.05267v1 Announce Type: new Abstract: In the era of Large Language Models (LLMs), the Mixture of Experts (MoE) architecture has emerged as an effective approach …
arXiv:2604.05529v1 Announce Type: new Abstract: Human mobility modeling is indispensable for diverse urban applications. However, existing data-driven methods often suffer from data scarcity, limiting their …
arXiv:2604.05162v1 Announce Type: new Abstract: Reconfigurable Intelligent Surfaces (RIS) are pivotal for next-generation smart radio environments, yet their practical deployment is severely bottlenecked by the …
arXiv:2604.05192v1 Announce Type: new Abstract: Byte Pair Encoding (BPE) is a widely used tokenization algorithm, whose tokens cannot extend across pre-tokenization boundaries, functionally limiting it …
arXiv:2604.05857v1 Announce Type: new Abstract: Clustering mixed-type tabular data is fundamental for exploratory analysis, yet remains challenging due to misaligned numerical-categorical representations, uneven and context-dependent …
arXiv:2604.05116v1 Announce Type: new Abstract: Clinical diagnosis requires sequential evidence acquisition under uncertainty. However, most Large Language Model (LLM) based diagnostic systems assume fully observed …
arXiv:2604.05136v1 Announce Type: new Abstract: Fuzzy Cognitive Maps constitute a neuro-symbolic paradigm for modeling complex dynamic systems, widely adopted for their inherent interpretability and recurrent …
arXiv:2604.05923v1 Announce Type: new Abstract: State space models (SSMs) have been shown to possess the theoretical capacity to model both star-free sequential tasks and bounded …
arXiv:2604.05426v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA) is now the dominant method for parameter-efficient fine-tuning of large language models, but achieving a high-quality adapter …
arXiv:2604.05072v1 Announce Type: new Abstract: Recent large language models have shifted SVG generation from differentiable rendering optimization to autoregressive program synthesis. However, existing approaches still …