2026-07-13 04:00 UTCOriginal source2 min readUpdated: 2026-07-13 08:19 UTC

AgentKGV: Agentic LLM-RAG Framework with Two-Stage Training for the Fact Verification of Knowledge Graphs

Knowledge graphs (KGs) often contain factual errors from automatic construction. AgentKGV proposes an agentic LLM-RAG framework with dynamic routing and iterative query rewriting, enhanced by a two-stage training strategy (distillation-based SFT and trajectory-level GRPO) for improved accuracy and cost efficiency. On the T-REx benchmark, macro-F1 improves by 14.9 percentage points over single-turn RAG, with search calls halved.

SourcearXiv Computational LinguisticsAuthor: Yumin Heo, Hyeon-gu Lee, Sumin Seo, Youngjoong Ko

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[Submitted on 10 Jul 2026]

Title:AgentKGV: Agentic LLM-RAG Framework with Two-Stage Training for the Fact Verification of Knowledge Graphs

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Abstract:Knowledge graphs (KGs) are often automatically constructed from large-scale corpora, but they inevitably contain factual errors due to noisy sources and extraction failures, and verifying them reliably at industrial scale remains a critical challenge. To address this, we propose AgentKGV, the Agentic LLM-RAG framework for KG fact Verification, that integrates dynamic routing and iterative query rewriting, which handles surface-form mismatch in document-level retrieval. To make this framework more accurate and cost-efficient for industrial deployment, we further introduce a two-stage training strategy: turn-level distillation-based SFT that transfers reasoning ability from a large teacher model into a small model for stable query rewriting and reasoning, and trajectory-level GRPO that optimizes the search policy to reduce unnecessary retrieval at scale. On the long-tail-predicate split of the open-domain T-REx benchmark, our framework improves macro-F1 over single-turn RAG by 5.5 \%p, and two-stage training does it further by 9.4 \%p. GRPO also cuts the average number of search calls from 3.24 to 1.63 without lowering accuracy.

Subjects:

Computation and Language (cs.CL)

Cite as: arXiv:2607.09092 [cs.CL]

(or arXiv:2607.09092v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.09092

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yumin Heo [view email] [v1] Fri, 10 Jul 2026 04:22:34 UTC (585 KB)

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