Text understanding and language tasks
![[2603.24326] Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2603.24326: Boosting Document Parsing Efficiency and Performance with Coarse-to-Fine Visual Processing
Abstract page for arXiv paper 2601.13508: Autonomous Computational Catalysis Research via Agentic Systems
Abstract page for arXiv paper 2510.20847: Integrated representational signatures strengthen specificity in brains and models
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NVIDIA has launched the Nemotron-Nano-9B-v2-Japanese, a lightweight language model designed to enhance Japanese language understanding an...
Cohere has launched Tiny Aya, a family of open multilingual models that support over 70 languages and can run on everyday devices, enhanc...
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The paper presents a novel framework for enhancing privacy protection in mobile GUI agents by anonymizing sensitive data while maintainin...
The paper explores the role of paraphrase generation and detection in language modeling, emphasizing the need for fine-grained semantic u...
The paper explores Code World Models (CWMs), which simulate program execution and identify error sources, focusing on local semantic exec...
This study examines the relationship between cryptocurrency whitepaper claims and actual market behavior, revealing weak predictive power...
This paper presents a two-stage retrieval system designed for the TREC Tip-of-the-Tongue task, integrating multiple retrieval methods wit...
This paper presents a unified framework for Query Auto-Completion (QAC) that integrates Retrieval-Augmented Generation (RAG) and multi-ob...
ShotFinder introduces a novel benchmark for open-domain video shot retrieval, utilizing LLMs to enhance video search capabilities through...
RosettaSpeech introduces a zero-shot framework for speech-to-speech translation, overcoming the need for parallel speech data by using mo...
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