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 present GRADRAG, a framework for improving Retrieval-Augmented Generation (RAG) systems by coordinating updates across multiple agents in the pipeline. The system uses feedback from an Evaluator to update adaptive agents such as retrievers, graph constructors, and answerers. It was tested on two benchmarks under different retrieval paradigms and outperformed one-step refinement baselines.

What the Document Says

• GRADRAG is a framework for cross-component prompt adaptation in Retrieval-Augmented Generation (RAG) systems.
• It 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, providing actionable feedback to a Prompt Optimizer 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.
• It outperformed one-step refinement baselines, achieving a 12-15 percentage point net preference margin in LLM-judged pairwise comparisons.

Key Concepts Mentioned

• Retrieval-Augmented Generation (RAG)
• GRADRAG
• computational graph
• evaluation feedback
• adaptive agents
• SQUALITY
• QMSUM
• IRCoT
• LLM

Claims Made by the Document

• GRADRAG improves RAG systems by coordinating updates across multiple agents.
• It outperforms one-step refinement baselines.

Evidence Used

• The text describes GRADRAG's design and its benefits in terms of performance.

Methods / Approach

UNKNOWN

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.

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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.