GRADRAG: Cross-Component Prompt Adaptation for Coordinated Multi-Agent RAG

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Paolo Pedinotti, Enrico Santus
• Venue: arXiv: cs.CL
• Published: 2026-07-23
• Collected: 2026-07-24
• URL: http://arxiv.org/abs/2607.21324v1
• PDF: https://arxiv.org/pdf/2607.21324v1
• DOI: UNKNOWN

Plain-Language Brief

The authors introduce GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating improvements across multiple agents in the pipeline, rather than optimizing components in isolation. The system includes an Evaluator that critiques downstream answers and evidence, providing feedback to update adaptive agents like retrievers, graph constructors, and answerers. GRADRAG was evaluated on SQUALITY and QMSUM benchmarks under two retrieval paradigms: flat chunk-based retrieval and graph-based retrieval. In both settings, GRADRAG consistently outperformed one-step refinement baselines, achieving a significant net preference margin in LLM-judged pairwise comparisons.

What the Document Says

• GRADRAG is a framework for cross-component prompt adaptation in Retrieval-Augmented Generation (RAG) systems.
• The framework models the RAG pipeline as a computational graph and uses structured evaluation feedback to update upstream agents.
• It includes an Evaluator that critiques downstream answers and evidence, providing actionable feedback for iterative updates of adaptive agents.
• The Evaluator also triggers early stopping when the output is deemed satisfactory.
• GRADRAG was evaluated on SQUALITY and QMSUM benchmarks under two retrieval paradigms: flat chunk-based retrieval and graph-based retrieval.
• Across both settings, GRADRAG consistently outperformed one-step refinement baselines.

Key Concepts Mentioned

• Retrieval-Augmented Generation (RAG)
• GRADRAG
• computational graph
• Evaluator
• Prompt Optimizer
• SQUALITY
• QMSUM
• LLM

Claims Made by the Document

• GRADRAG improves RAG systems by coordinating improvements across multiple agents in the pipeline.
• GRADRAG consistently outperforms one-step refinement baselines.

Evidence Used

• The text describes GRADRAG's design and its evaluation on benchmarks, showing consistent performance improvements.

Methods / Approach

• Cross-component prompt adaptation
• Structured evaluation feedback
• Iterative updates of adaptive agents

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Retrieval-Augmented Generation (RAG) systems increasingly employ multiple LLM agents. Yet, most prior work optimizes components in isolation rather than coordinating improvements across the pipeline. We introduce GRADRAG, a framework for cross-component prompt adaptation that models the RAG pipeline as a computational graph and propagates structured evaluation feedback to update upstream agents. An Evaluator critiques downstream answers and supporting evidence, producing actionable feedback that a Prompt Optimizer uses to iteratively update adaptive agents, such as retrievers, graph constructors, and answerers. The Evaluator also triggers early stopping when the output is deemed satisfactory. We evaluate GRADRAG on the SQUALITY and QMSUM benchmarks under two retrieval paradigms: flat chunk-based retrieval using IRCoT-style query refinement (Trivedi et al., 2023), and graph-based retrieval that constructs and iteratively enriches an entity-relation graph from the document. Across both settings, GRADRAG consistently outperforms one-step refinement baselines that update only the final generator, achieving a 12-15 percentage point net preference margin in LLM-judged pairwise comparisons, with most gains realized within two refinement iterations.