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arXiv — cs.AI

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RE-SciVQA: Improving Scientific VQA With Context-Relevant Retrieval and Lightweight MLLM Adaptation

arXiv:2611.00242v1 Announce Type: new Abstract: A deep understanding of scientific literature often relies on interpreting figures that contain key information beyond the main text. Leveraging Large Language Models (LLMs) to extract insights from these figures can significantly…

arXiv — cs.AIBy: Sheikh Tahmim Mehdi, Muminul Islam JamiNovember 3, 2026 (13 hours ago)
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  • arXiv:2611.00242v1 Announce Type: new Abstract: A deep understanding of scientific literature often relies on interpreting figures that contain key information beyond the main text
  • Leveraging Large Language Models (LLMs) to extract insights from these figures can significantly…
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RE-SciVQA: Improving Scientific VQA With Context-Relevant Retrieval and Lightweight MLLM Adaptation

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