mlsanityData to 7 Oct 2026

Toward Agentic RAG for Ukrainian

arXiv 2604.14896 ↗First posted 16 Apr 2026cs.AIcited by 0

On the map: Retrieval-augmented generation → Agentic RAG, agents and memory

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Abstract (arXiv)

We present an initial investigation into Agentic Retrieval-Augmented Generation (RAG) for Ukrainian, conducted within the UNLP 2026 Shared Task on Multi-Domain Document Understanding. Our system combines two-stage retrieval (BGE-M3 with BGE reranking) with a lightweight agentic layer performing query rephrasing and answer-retry loops on top of Qwen2.5-3B-Instruct. Our analysis reveals that retrieval quality is the primary bottleneck: agentic retry mechanisms improve answer accuracy but the overall score remains constrained by document and page identification. We discuss practical limitations of offline agentic pipelines and outline directions for combining stronger retrieval with more advanced agentic reasoning for Ukrainian.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agentic RAG, agents and memory, one of 15 directions in Retrieval-augmented generation. Builds on 5 of its 8 references that are in the map. First posted on arXiv 16 Apr 2026.

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  1. 2026MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains1 Mar
  2. 2025Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG15 Jan

Other references · 3

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
  3. 2021WikiCheck: An end-to-end open source Automatic Fact-Checking API based on Wikipediacited by 12

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