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arXiv:2603.23523v1 Announce Type: new Abstract: Recent 3D Large-Language Models (3D-LLMs) claim to understand 3D worlds, especially spatial relationships among objects. Yet, we find that simply …
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:2603.23512v1 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:2603.23514v1 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:2603.23524v1 Announce Type: new Abstract: Sparse autoencoders (SAEs) trained on large language model activations output thousands of features that enable mapping to human-interpretable concepts. The …
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:2603.23511v1 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:2603.23525v1 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:2603.23521v1 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:2603.23515v1 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:2603.23520v1 Announce Type: new Abstract: Medicine is an empirical discipline refined through long-term observation and the messy, high-variance reality of clinical practice. Physicians build diagnostic …