Quality follows upgrading
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arXiv:2603.23518v1 Announce Type: new Abstract: General-purpose embedding models excel at recognizing semantic similarities but fail to capture the characteristics of texts specified by user instructions. …
arXiv:2603.23516v1 Announce Type: new Abstract: Long-term memory is a cornerstone of human intelligence. Enabling AI to process lifetime-scale information remains a long-standing pursuit in the …
arXiv:2603.23515v1 Announce Type: new Abstract: Improving the accuracy and reliability of medical coding reduces clinician burnout and supports revenue cycle processes, freeing providers to focus …
arXiv:2603.23514v1 Announce Type: new Abstract: Large Language Models appear competent when answering general questions but often fail when pushed into domain-specific details. No existing methodology …
arXiv:2603.23513v1 Announce Type: new Abstract: Commercial AI scribes cost \$99-600 per physician per month, operate as opaque systems, and do not return data to institutional …
arXiv:2603.23512v1 Announce Type: new Abstract: We present S-Path-RAG, a semantic-aware shortest-path Retrieval-Augmented Generation framework designed to improve multi-hop question answering over large knowledge graphs. S-Path-RAG …
arXiv:2603.23511v1 Announce Type: new Abstract: Document intelligence requires accurate text extraction and reliable reasoning over document content. We introduce \textbf{DISCO}, a \emph{Document Intelligence Suite for …
arXiv:2603.23510v1 Announce Type: new Abstract: As multimodal language models (MLMs) are increasingly used in social and collaborative settings, it is crucial to evaluate their perspective-taking …
arXiv:2603.23509v1 Announce Type: new Abstract: This work identifies a critical failure mode in frontier large language models (LLMs), which we term Internal Safety Collapse (ISC): …
arXiv:2603.23508v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) is increasingly deployed in enterprise search and document-centric assistants, where responses must be grounded in long and …
arXiv:2603.23507v1 Announce Type: new Abstract: While Masked Diffusion Language Models (MDLMs) relying on token masking and unmasking have shown promise in language modeling, their computational …
arXiv:2603.23506v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) in healthcare creates an urgent need for scalable and psychometrically sound evaluation …
