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arXiv:2603.12152v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has accelerated progress toward universal AI assistants. However, existing benchmarks for personalized …
arXiv:2603.12165v1 Announce Type: new Abstract: Synthetic data has become essential for training code generation models, yet it introduces significant noise and hallucinations that are difficult …
arXiv:2603.12191v1 Announce Type: new Abstract: While decoder-only Large Language Models (LLMs) have recently dominated the NLP landscape, encoder-only architectures remain a cost-effective and parameter-efficient standard …
arXiv:2603.12201v1 Announce Type: new Abstract: Long-context agentic workflows have emerged as a defining use case for large language models, making attention efficiency critical for both …
arXiv:2603.12206v1 Announce Type: new Abstract: State space models (SSMs) like Mamba have gained significant traction as efficient alternatives to Transformers, achieving linear complexity while maintaining …
arXiv:2603.12226v1 Announce Type: new Abstract: Despite interdisciplinary research leading to larger and longer-term impact, most work remains confined to single-domain academic silos. Recent AI-based approaches …
arXiv:2603.11049v1 Announce Type: new Abstract: Outlier detection is a well-researched and crucial problem in machine learning. However, there is little research on string data outlier …
arXiv:2603.11052v1 Announce Type: new Abstract: Neural operators (NOs) provide fast, resolution-invariant surrogates for mapping input fields to PDE solution fields, but their predictions can exhibit …
arXiv:2603.11090v1 Announce Type: new Abstract: Prior-data fitted networks (PFNs) have emerged as powerful foundation models for tabular causal inference, yet their extension to time series …
arXiv:2603.11094v1 Announce Type: new Abstract: Streaming sources of data are becoming more common as the ability to collect data in real-time grows. A major concern …
arXiv:2603.11099v1 Announce Type: new Abstract: The success of large pretrained Transformers is closely tied to tokenizers, which convert raw input into discrete symbols. Extending these …
arXiv:2603.11114v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models through conditional computation, yet the routing mechanisms responsible for …