DoTA-RAG: Dynamic of Thought Aggregation RAG
Authors: name: Saksorn Ruangtanusak, name: Natthapath Rungseesiripak, name: Peerawat Rojratchadakorn, name: Monthol Charattrakool, name: Natapong Nitarach

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.