arXiv:2605.01386v1 Announce Type: new
Abstract: Large Language Models (LLMs) lack persistent memory for long-term personalized conversations. Existing graph-based memory systems suffer from information dilution, absent provenance tracking, and uniform…
arXiv:2605.01417v1 Announce Type: cross
Abstract: Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insufficient coverage of relevant tasks. Existing suite…
arXiv:2605.01451v1 Announce Type: new
Abstract: Large language models (LLMs) are rapidly being integrated into high-stakes public safety systems, including emergency call triage and dispatch decision support, yet their demographic fairness in this con…
arXiv:2504.02293v3 Announce Type: replace-cross
Abstract: Gloss is a written approximation that bridges Sign Language (SL) and its corresponding spoken language. Despite a deaf and hard-of-hearing population of at least 3 million in Bangladesh, Bangla…
arXiv:2605.01537v1 Announce Type: new
Abstract: Isolated word meanings are inherently uncertain. This uncertainty reduces when they are combined and anchored in context. We propose that grammar compresses meaning uncertainty cross-linguistically, whic…
arXiv:2605.01605v1 Announce Type: cross
Abstract: Large language models are sensitive to minor prompt perturbations, yet existing robustness methods usually enforce consistency at the whole-sequence level. This holistic view can hide an important fail…
arXiv:2605.01630v1 Announce Type: cross
Abstract: Rankings produced by holistic LLM-as-a-judge scoring are sensitive to the bias of the chosen judge model. We show that switching to binary rubric scoring with multi-judge filtering removes this sensiti…
arXiv:2601.03267v2 Announce Type: replace-cross
Abstract: This is the system card published alongside the OpenAI GPT-5 launch, August 2025.
GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reasoning model f…
arXiv:2601.19827v3 Announce Type: replace-cross
Abstract: Retrieval-Augmented Generation (RAG) extends large language models (LLMs) beyond parametric knowledge, yet it is unclear when iterative retrieval-reasoning loops meaningfully outperform static …
arXiv:2605.01749v1 Announce Type: new
Abstract: Large Reasoning Models achieve strong performance on complex tasks but remain prone to hallucinations, particularly in long-form generation where errors compound across reasoning steps. Existing approach…