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arXiv:2603.23521v1 Announce Type: new Abstract: Multimodal research has predominantly focused on single-image reasoning, with limited exploration of multi-image scenarios. Recent models have sought to enhance …
arXiv:2603.23525v1 Announce Type: new Abstract: The economics of prompt compression depend not only on reducing input tokens but on how compression changes output length, which …
arXiv:2603.23528v1 Announce Type: new Abstract: The rapid proliferation of Large Language Models has created an environmental paradox: the very technology that could help solve climate …
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:2603.23508v1 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:2603.23509v1 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:2603.23527v1 Announce Type: new Abstract: Prompt compression is often evaluated by input-token reduction, but its real deployment impact depends on how compression changes output length …
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:2603.23515v1 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:2603.23526v1 Announce Type: new Abstract: The growing publication rate of research papers has created an urgent need for better ways to fact-check information, assess writing …
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:2603.23522v1 Announce Type: new Abstract: Evaluating large language models (LLMs) on open-ended questions is difficult because response quality depends on the question's context. Binary scores …
