Hugging Face Trending Papers June 9, 2026

One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA

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External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal evidence. However, existing memory paradigms represent each memory item in raw text and image forms, so retrieval-based systems must pass the retrieved text or images to the generation LLMs/VLMs, resulting in high token consumption and storage pressure, making it unaffordable for resource-constrained applications.

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arXiv AI

Jun 10

One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA

arXiv:2606. 10572v1 Announce Type: new Abstract: External memory effectively grounds large language models (LLMs) and vision-language models (VLMs)-based question answering (QA) in relevant multimodal evidence.

By Zhi Zheng, Ziqiao Meng, Hao Luan, Wei Liu, Wee Sun Lee

llms rag nlp efficiency multimodal benchmarks

arXiv AI

Sep 2

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arXiv Computation and Language

Aug 27

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arXiv AI

Jun 26

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By Ji Xie, Trevor Darrell, Luke Zettlemoyer, XuDong Wang

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arXiv AI

Jul 29

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By Noor Islam S. Mohammad, Ulu\u{g} Bayaz{\i}t

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arXiv Machine Learning

Sep 22

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One Token per Multimodal Evidence: Latent Memory for Resource-Constrained QA

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