RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings

• URL: http://arxiv.org/abs/2608.01311v1
• Date: Sun, 02 Aug 2026 15:27:00 GMT
• Title: RH-RAG: Trustworthy Long-Form Generation for Privacy-Constrained Settings
• Abstract summary: RH-RAG is a framework for secure and trustworthy long form generation using local language models.<n>It decomposes generation into three coordinated stages: a Planner Agent that constructs a global document outline from semantic summaries, a Writer Agent that incrementally generates coherent section-wise content, and a Checker Agent that mitigates hallucinations through natural language inference-based factual verification.<n> Evaluations across literary, financial, and legal domains demonstrate that RH-RAG consistently improves factual grounding, semantic coherence, and document-level alignment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating long-form content from extensive internal reports remains challenging for organizations operating under strict privacy and security constraints, where proprietary cloud-based LLM APIs are often not viable. While locally deployed open-weight models offer a privacy-preserving alternative, existing retrieval-augmented generation (RAG) approaches on smaller models frequently lack effective global planning and accumulate factual inconsistencies over long outputs. To address these limitations, we present RH-RAG, a multi-agent framework for secure and trustworthy long form generation using local language models. RH-RAG decomposes generation into three coordinated stages: a Planner Agent that constructs a global document outline from high-level semantic summaries, a Writer Agent that incrementally generates coherent section-wise content using bounded coherence memory, and a Checker Agent that mitigates hallucinations through natural language inference-based factual verification and an attestation-driven revision loop. The framework further employs a dual-level retrieval index that supports efficient planning and fine-grained contextual generation on consumer-grade hardware. Evaluations across literary, financial, and legal domains demonstrate that RH-RAG consistently improves factual grounding, semantic coherence, and document-level alignment compared to standard and hierarchical RAG baselines, while achieving reliability competitive with proprietary cloud-based systems without compromising data privacy.

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