Academic
arXiv:2602.22766v1 Announce Type: new Abstract: Latent visual reasoning aims to mimic human's imagination process by meditating through hidden states of Multimodal Large Language Models. While …
arXiv:2602.22787v1 Announce Type: new Abstract: Large language models (LLMs) often generate fluent but unfounded claims, or hallucinations, which fall into two types: (i) faithfulness violations …
arXiv:2602.22790v1 Announce Type: new Abstract: The rapid evolution of large language models (LLMs) has transformed prompt engineering from a localized craft into a systems-level governance …
arXiv:2602.22827v1 Announce Type: new Abstract: This paper presents a comprehensive evaluation framework for assessing the cultural competence of large language models (LLMs) in Persian. Existing …
arXiv:2602.22828v1 Announce Type: new Abstract: Background: Retrieval augmented generation (RAG) technology can empower large language models (LLMs) to generate more accurate, professional, and timely responses …
arXiv:2602.22846v1 Announce Type: new Abstract: Argumentation mining comprises several subtasks, among which stance classification focuses on identifying the standpoint expressed in an argumentative text toward …
arXiv:2602.22865v1 Announce Type: new Abstract: Explicit representations of predicate-argument relations form the basis of interpretable semantic analysis, supporting reasoning, generation, and evaluation. However, attaining such …
arXiv:2602.22868v1 Announce Type: new Abstract: Diffusion Large Language Models (DLLMs) promise fast non-autoregressive inference but suffer a severe quality-speed trade-off in parallel decoding. This stems …
arXiv:2602.22871v1 Announce Type: new Abstract: Reasoning with large language models often benefits from generating multiple chains-of-thought, but existing aggregation strategies are typically trajectory-level (e.g., selecting …
arXiv:2602.22918v1 Announce Type: new Abstract: Vision-language models (VLMs) can read text from images, but where does this optical character recognition (OCR) information enter the language …
arXiv:2602.23057v1 Announce Type: new Abstract: Transformer attention is typically implemented using softmax normalization, which enforces attention weights with unit sum normalization. While effective in many …
arXiv:2602.23062v1 Announce Type: new Abstract: Case Report Forms (CRFs) collect data about patients and are at the core of well-established practices to conduct research in …
