AgentKGV: Agentic LLM-RAG Framework with Two-Stage Training for the Fact Verification of Knowledge Graphs
Quick Answer
AgentKGV is a novel LLM-RAG framework for verifying knowledge graphs, improving macro-F1 scores by 5.5% over single-turn RAG and 9.4% with two-stage training.
Quick Take
AgentKGV is a novel LLM- framework for verifying knowledge graphs, improving macro-F1 scores by 5.5% over single-turn RAG and 9.4% with two-stage training. The GRPO method reduces search calls from 3.24 to 1.63 while maintaining accuracy, addressing the challenge of factual errors in KGs at scale.
Key Points
- AgentKGV integrates dynamic routing and iterative query rewriting for improved KG verification.
- Two-stage training includes turn-level distillation and trajectory-level GRPO for efficiency.
- The framework achieved a macro-F1 improvement of 9.4% on the T-REx benchmark.
- GRPO reduces average search calls significantly without compromising accuracy.
- Addresses critical challenges in verifying automatically constructed knowledge graphs.
Paper Resources
📖 Reader Mode
~2 min readAbstract: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)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.CL
See more →Letting the Data Speak: Extracting Keywords from Crowdsourced Collections with AI
The study evaluates three NLP approaches—Named Entity Recognition, Keyword Extraction, and Topic Modelling—using the Their Finest Hour Online Archive to automate keyword extraction from crowdsourced WWII collections. Findings suggest that while NLP methods show promise, no single approach is sufficient, and ethical considerations in automated keyword extraction are crucial for responsible stewardship.