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arXiv:2603.05818v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at multi-step reasoning, yet increasing the structural complexity of inference does not consistently improve system-level …
arXiv:2603.05828v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable performance in text generation and knowledge-intensive question answering. Nevertheless, they are prone to …
arXiv:2603.05863v1 Announce Type: new Abstract: While Large Language Models (LLMs) have revolutionized code generation, standard "System 1" approaches, generating solutions in a single forward pass, …
arXiv:2603.05878v1 Announce Type: new Abstract: Pruning is widely recognized as an effective method for reducing the parameters of large language models (LLMs), potentially leading to …
arXiv:2603.05881v1 Announce Type: new Abstract: Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing methods are predominantly answer-first, producing confidence only after …
arXiv:2603.05883v1 Announce Type: new Abstract: Tokenization is the foundational step in all large language model (LLM) pipelines, yet the dominant approach Byte Pair Encoding (BPE) …
arXiv:2603.05890v1 Announce Type: new Abstract: What happens when a storyteller forgets its own story? Large Language Models (LLMs) can now generate narratives spanning tens of …
arXiv:2603.05895v1 Announce Type: new Abstract: This paper introduces a new methodology for using LLM-based systems for accurate and efficient semantic tagging of UN Security Council …
arXiv:2603.05909v1 Announce Type: new Abstract: LLMs are increasingly deployed in high-stakes domains such as medical triage and legal assistance, often as document-grounded QA systems in …
arXiv:2603.05923v1 Announce Type: new Abstract: Truly proactive AI systems must anticipate what we will do next. This foresight demands far richer information than the sparse …
arXiv:2603.05928v1 Announce Type: new Abstract: Language model training and inference ignore a fundamental linguistic fact -- there is a dependence between multiple sequences of text …
arXiv:2603.05933v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated impressive capabilities in role-playing (RP); however, small Language Models (SLMs) with highly stylized personas …