DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2603.18012v1
• Date: Tue, 24 Feb 2026 03:55:07 GMT
• Title: DynaRAG: Bridging Static and Dynamic Knowledge in Retrieval-Augmented Generation
• Abstract summary: DynaRAG is a retrieval-augmented generation (RAG) framework designed to handle both static and time-sensitive information needs.<n>Unlike traditional RAG pipelines that rely solely on static corpora, DynaRAG selectively invokes external APIs when retrieved documents are insufficient for answering a query.
• Score: 8.036549927091286
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents [68.52563127206808]
  Structure-aware Retrieval Augmented Generation (SA-RAG) uses tables as an intermediate structured representation to provide a compact and controllable interface.<n>We introduce a quality-aware table metadata generation framework that models metadata normalization and effectiveness.<n>Experiments on two noisy real-world datasets show that SA-RAG significantly outperforms existing RAG baselines.
  arXiv  Detail & Related papers  (2026-05-23T03:07:33Z)
• NaviRAG: Towards Active Knowledge Navigation for Retrieval-Augmented Generation [50.16741209529908]
  NaviRAG is a novel framework that shifts from passive segment retrieval to active knowledge navigation.<n>We show that NaviRAG consistently improves both retrieval recall and end-to-end answer performance over conventional RAG baselines.
  arXiv  Detail & Related papers  (2026-04-14T14:07:01Z)
• MARAG-R1: Beyond Single Retriever via Reinforcement-Learned Multi-Tool Agentic Retrieval [50.30107119622642]
  Large Language Models (LLMs) excel at reasoning and generation but are inherently limited by static pretraining data.<n>Retrieval-Augmented Generation (RAG) addresses this issue by grounding LLMs in external knowledge.<n>MarAG-R1 is a reinforcement-learned multi-tool RAG framework that enables LLMs to dynamically coordinate multiple retrieval mechanisms.
  arXiv  Detail & Related papers  (2025-10-31T15:51:39Z)
• LAD-RAG: Layout-aware Dynamic RAG for Visually-Rich Document Understanding [37.12229829548839]
  We propose LAD-RAG, a novel layout-Aware Dynamic RAG framework.<n>LAD-RAG constructs a symbolic document graph that captures layout structure and cross-page dependencies.<n>Experiments on MMLongBench-Doc, LongDocURL, DUDE, and MP-DocVQA demonstrate that LAD-RAG improves retrieval, achieving over 90% perfect recall on average without any top-k tuning.
  arXiv  Detail & Related papers  (2025-10-08T17:02:04Z)
• Online-Optimized RAG for Tool Use and Function Calling [10.294181998196555]
  retrieval-augmented generation (RAG) drives tool use and function calling by embedding user queries to pre-specified tool/function descriptions.<n>Online-d RAG adapts retrieval embeddings from live interactions using minimal feedback.
  arXiv  Detail & Related papers  (2025-09-24T09:08:46Z)
• TURA: Tool-Augmented Unified Retrieval Agent for AI Search [18.427511565701394]
  Traditional RAG approaches struggle with real-time needs and structured queries.<n>We introduce TURA, a novel three-stage framework that combines RAG with agentic tool-use to access both static content and dynamic, real-time information.
  arXiv  Detail & Related papers  (2025-08-06T16:24:17Z)
• Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation [6.62734677678023]
  Real-world live retrieval-augmented generation (RAG) systems face challenges when processing user queries that are noisy, ambiguous, and contain multiple intents.<n>This paper introduces Omni-RAG, a novel framework designed to improve the robustness and effectiveness of RAG systems in live, open-domain settings.
  arXiv  Detail & Related papers  (2025-06-26T15:35:12Z)
• ImpRAG: Retrieval-Augmented Generation with Implicit Queries [34.72864597562907]
  ImpRAG is a query-free RAG system that integrates retrieval and generation into a unified model.<n>We show that ImpRAG achieves 3.6-11.5 improvements in exact match scores on unseen tasks with diverse formats.
  arXiv  Detail & Related papers  (2025-06-02T21:38:21Z)
• DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented Generation [23.060355911225923]
  Reranker plays vital role in refining retrieved documents to enhance generation quality and explainability.<n>We propose DynamicRAG, a novel RAG framework where the reranker dynamically adjusts both the order and number of retrieved documents.
  arXiv  Detail & Related papers  (2025-05-12T05:19:01Z)
• Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
  Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [66.93260816493553]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios.<n>With a focus on factual accuracy, we propose three novel metrics: Completeness, Hallucination, and Irrelevance.<n> Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• RAGGED: Towards Informed Design of Scalable and Stable RAG Systems [51.171355532527365]
  Retrieval-augmented generation (RAG) enhances language models by integrating external knowledge.<n>RAGGED is a framework for systematically evaluating RAG systems.
  arXiv  Detail & Related papers  (2024-03-14T02:26:31Z)
• Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection [74.51523859064802]
  We introduce a new framework called Self-Reflective Retrieval-Augmented Generation (Self-RAG) Self-RAG enhances an LM's quality and factuality through retrieval and self-reflection. It significantly outperforms state-of-the-art LLMs and retrieval-augmented models on a diverse set of tasks.
  arXiv  Detail & Related papers  (2023-10-17T18:18:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.