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cs.CL2026
DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation
Penghao Liang, Mengwei Yuan, Jianan Liu +4
We present DynaRAG, a retrieval-augmented generation (RAG) framework designed to handle both static and time-sensitive information needs through dynamic knowledge integration. Unli…
cs.CL2026
DomainCQA: Crafting Knowledge-Intensive QA from Domain-Specific Charts
Yujing Lu, Ling Zhong, Jing Yang +5
Chart Question Answering (CQA) evaluates Multimodal Large Language Models (MLLMs) on visual understanding and reasoning over chart data. However, existing benchmarks mostly test su…