SPD-RAG: Sub-Agent Per Document Retrieval-Augmented Generation
Yagiz Can Akay, Muhammed Yusuf Kartal, Esra Alparslan, Faruk Ortakoyluoglu, Arda Akpinar

TL;DR
SPD-RAG introduces a hierarchical multi-agent framework for multi-document question answering, decomposing the task across documents to improve answer quality and scalability while reducing costs.
Contribution
It presents a novel hierarchical multi-agent approach with document-level specialization and centralized fusion for efficient, high-quality multi-document QA.
Findings
Outperforms baseline models on LOONG benchmark with higher scores.
Achieves 58.1 average score, significantly better than RAG and Agentic RAG.
Reduces API cost to 38% of full-context baseline.
Abstract
Answering complex, real-world queries often requires synthesizing facts scattered across vast document corpora. In these settings, standard retrieval-augmented generation (RAG) pipelines suffer from incomplete evidence coverage, while long-context large language models (LLMs) struggle to reason reliably over massive inputs. We introduce SPD-RAG, a hierarchical multi-agent framework for exhaustive cross-document question answering that decomposes the problem along the document axis. Each document is processed by a dedicated document-level agent operating only on its own content, enabling focused retrieval, while a coordinator dispatches tasks to relevant agents and aggregates their partial answers. Agent outputs are synthesized by merging partial answers through a token-bounded synthesis layer (which supports recursive map-reduce for massive corpora). This document-level specialization…
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Taxonomy
TopicsTopic Modeling · Multimodal Machine Learning Applications · Natural Language Processing Techniques
