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arXiv:2603.23821v1 Announce Type: new Abstract: Linguistic representation learning in deep neural language models (LMs) has been studied for decades, for both practical and theoretical reasons. …
arXiv:2603.23841v1 Announce Type: new Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact …
arXiv:2603.23844v1 Announce Type: new Abstract: Recent work shows overwhelming evidence that LLMs, even those trained to scale their reasoning trace, perform unsatisfactorily when solving planning …
arXiv:2603.23848v1 Announce Type: new Abstract: LLMs are increasingly used as long-running conversational agents, yet every major benchmark evaluating their memory treats user information as static …
arXiv:2603.23911v1 Announce Type: new Abstract: As Large Language Models (LLMs) scale up, inference efficiency becomes a critical bottleneck. Multi-Token Prediction (MTP) could accelerate LLM inference …
arXiv:2603.23937v1 Announce Type: new Abstract: Evidence-based medicine (EBM) is central to high-quality care, but remains difficult to implement in fast-paced primary care settings. Physicians face …
arXiv:2603.23938v1 Announce Type: new Abstract: Most testbeds for omni-modal models assess multimodal understanding via textual outputs, leaving it unclear whether these models can properly speak …
arXiv:2603.23949v1 Announce Type: new Abstract: Argument Mining(AM) aims to uncover the argumentative structures within a text. Previous methods require several subtasks, such as span identification, …
arXiv:2603.23951v1 Announce Type: new Abstract: Discovering improved policy optimization algorithms for language models remains a costly manual process requiring repeated mechanism-level modification and validation. Unlike …
arXiv:2603.23971v1 Announce Type: new Abstract: Developers and consumers increasingly choose reasoning language models (RLMs) based on their listed API prices. However, how accurately do these …
arXiv:2603.23972v1 Announce Type: new Abstract: Large language models (LLMs) have achieved remarkable progress in many language tasks, yet they continue to struggle with complex historical …
arXiv:2603.23989v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has shown promising results in enhancing Q&A by incorporating information from the web and other external sources. …
