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arXiv:2604.06543v1 Announce Type: new Abstract: In this work, we demonstrate that reliable stochastic sampling is a fundamental yet unfulfilled requirement for Large Language Models (LLMs) …
arXiv:2604.06542v1 Announce Type: new Abstract: Empirical scaling laws for language models have encouraged the development of ever-larger LLMs, despite their growing computational and memory costs. …
arXiv:2604.06507v1 Announce Type: new Abstract: Pashto is absent from Whisper's pre-training corpus despite being one of CommonVoice's largest language collections, leaving off-the-shelf models unusable: all …
arXiv:2604.06505v1 Announce Type: new Abstract: Large language models (LLMs) are widely explored for reasoning-intensive research tasks, yet resources for testing whether they can infer scientific …
arXiv:2604.06484v1 Announce Type: new Abstract: Cultural values are expressed not only through language but also through visual scenes and everyday social practices. Yet existing evaluations …
arXiv:2604.06474v1 Announce Type: new Abstract: Deep research with Large Language Model (LLM) agents is emerging as a powerful paradigm for multi-step information discovery, synthesis, and …
arXiv:2604.06465v1 Announce Type: new Abstract: Reasoning models have demonstrated remarkable capabilities in solving complex problems by leveraging long chains of thought. However, this more deliberate …
arXiv:2604.06456v1 Announce Type: new Abstract: Current Machine Translation (MT) systems for Arabic often struggle to account for dialectal diversity, frequently homogenizing dialectal inputs into Modern …
arXiv:2604.06452v1 Announce Type: new Abstract: Multi-agent systems using large language models (LLMs) have demonstrated impressive capabilities across various domains. However, current agent communication suffers from …
arXiv:2604.06424v1 Announce Type: new Abstract: This paper presents a transformer-based approach to solving the SympTEMIST named entity recognition (NER) and entity linking (EL) tasks. For …
arXiv:2604.06422v1 Announce Type: new Abstract: Understanding when Vision-Language Models (VLMs) will behave unexpectedly, whether models can reliably predict their own behavior, and if models adhere …
arXiv:2604.06421v1 Announce Type: new Abstract: This paper introduces Arabic-DeepSeek-R1, an application-driven open-source Arabic LLM that leverages a sparse MoE backbone to address the digital equity …