mlsanityData to 14 Oct 2026

DoTA-RAG: Dynamic of Thought Aggregation RAG

arXiv 2506.12571 ↗First posted 14 Jun 2025cs.CL cs.AI cs.IRcited by 1

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

In this paper, we introduce DoTA-RAG (Dynamic-of-Thought Aggregation RAG), a retrieval-augmented generation system optimized for high-throughput, large-scale web knowledge indexes. Traditional RAG pipelines often suffer from high latency and limited accuracy over massive, diverse datasets. DoTA-RAG addresses these challenges with a three-stage pipeline: query rewriting, dynamic routing to specialized sub-indexes, and multi-stage retrieval and ranking. We further enhance retrieval by evaluating and selecting a superior embedding model, re-embedding the large FineWeb-10BT corpus. Moreover, we create a diverse Q&A dataset of 500 questions generated via the DataMorgana setup across a broad range of WebOrganizer topics and formats. DoTA-RAG improves the answer correctness score from 0.752 (baseline, using LiveRAG pre-built vector store) to 1.478 while maintaining low latency, and it achieves a 0.929 correctness score on the Live Challenge Day. These results highlight DoTA-RAG's potential for practical deployment in domains requiring fast, reliable access to large and evolving knowledge sources.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 11 of its 16 references that are in the map. First posted on arXiv 14 Jun 2025.

Builds on

11 of 16 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorgana22 Jan
  2. 2023Retrieval-Augmented Generation for Large Language Models: A Survey18 Dec

Other references · 9

  1. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,708
  2. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,253
  3. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
  4. 2024The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scalecited by 1,160
  5. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  6. 2025Chain of Draft: Thinking Faster by Writing Lesscited by 297
  7. 2025Organize the Web: Constructing Domains Enhances Pre-Training Data Curationcited by 76
  8. 2024Arctic-Embed 2.0: Multilingual Retrieval Without Compromisecited by 58
  9. 2023From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICLcited by 17

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025SIGIR 2025 -- LiveRAG Challenge Reportcited by 0

Similar, not cited

Close in wording, no citation either way

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  3. 2025RAGtifier: Evaluating RAG Generation Approaches of State-of-the-Art RAG Systems for the SIGIR LiveRAG Competition
  4. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries
  5. 2025Structured RAG for Answering Aggregative Questions
  6. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs
  7. 2025QuIM-RAG: Advancing Retrieval-Augmented Generation with Inverted Question Matching for Enhanced QA Performance
  8. 2024CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity