GRACE-RAG: Governed Retrieval Architecture for Canonical Evidence Synthesis, Enabling Lightweight Deployment in Closed-Domain Institutional Settings
arXiv:2607.00013v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems are widely used in institutional question answering settings where responses must be grounded in authoritative documentation (Gao et al., 2023). In entity-dense domains where relevant information is distributed across heterogeneous documents, vector-only retrieval often produces fragmented evidence and increases dependence on inference-time reasoning (Zhao et al., 2024). This paper introduces GRACE-RAG, a retrieval-governed, graph-augmented RAG architecture that externalizes structural reasoning from
The proliferation of RAG systems in institutional settings, combined with the increasing complexity of entity-dense informational domains, necessitates architectural advancements to improve evidence synthesis.
This development addresses critical limitations in current RAG systems by improving how they retrieve and synthesize authoritative information, making them more reliable for high-stakes institutional use cases.
RAG systems can now leverage graph-augmented retrieval and governed architectures to provide more coherent and grounded responses, reducing fragmentation and dependence on post-retrieval inference.
- · Institutions with proprietary data
- · AI platform developers
- · Knowledge management software providers
- · Developers of simplistic RAG architectures
- · Organizations relying on unreliable AI outputs
Improved accuracy and trustworthiness of AI-generated responses in closed-domain institutional applications.
Accelerated adoption of AI-driven knowledge systems across regulated and sensitive industries.
Enhanced operational efficiency and reduced risk in sectors like legal, finance, and intelligence through more robust AI evidence synthesis.
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Read at arXiv cs.AI