DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914408212463616 |
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| author | Liang, Penghao Yuan, Mengwei Liu, Jianan Yang, Jing Li, Xianyou Yan, Weiran Wu, Yichao |
| author_facet | Liang, Penghao Yuan, Mengwei Liu, Jianan Yang, Jing Li, Xianyou Yan, Weiran Wu, Yichao |
| contents | We present DynaRAG, a retrieval-augmented generation (RAG) framework designed to handle both static and time-sensitive information needs through dynamic knowledge integration. Unlike traditional RAG pipelines that rely solely on static corpora, DynaRAG selectively invokes external APIs when retrieved documents are insufficient for answering a query. The system employs an LLM-based reranker to assess document relevance, a sufficiency classifier to determine when fallback is necessary, and Gorilla v2 -- a state-of-the-art API calling model -- for accurate tool invocation. We further enhance robustness by incorporating schema filtering via FAISS to guide API selection. Evaluations on the CRAG benchmark demonstrate that DynaRAG significantly improves accuracy on dynamic questions, while also reducing hallucinations. Our results highlight the importance of dynamic-aware routing and selective tool use in building reliable, real-world question-answering systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_18012 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation Liang, Penghao Yuan, Mengwei Liu, Jianan Yang, Jing Li, Xianyou Yan, Weiran Wu, Yichao Computation and Language Artificial Intelligence Information Retrieval We present DynaRAG, a retrieval-augmented generation (RAG) framework designed to handle both static and time-sensitive information needs through dynamic knowledge integration. Unlike traditional RAG pipelines that rely solely on static corpora, DynaRAG selectively invokes external APIs when retrieved documents are insufficient for answering a query. The system employs an LLM-based reranker to assess document relevance, a sufficiency classifier to determine when fallback is necessary, and Gorilla v2 -- a state-of-the-art API calling model -- for accurate tool invocation. We further enhance robustness by incorporating schema filtering via FAISS to guide API selection. Evaluations on the CRAG benchmark demonstrate that DynaRAG significantly improves accuracy on dynamic questions, while also reducing hallucinations. Our results highlight the importance of dynamic-aware routing and selective tool use in building reliable, real-world question-answering systems. |
| title | DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation |
| topic | Computation and Language Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2603.18012 |