Knowledge Graph-Guided Retrieval Augmented Generation
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Xiangrong Zhu, Yuexiang Xie, Yi Liu, Yaliang Li, Wei Hu. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.
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Standard retrieval-augmented approaches fail to address the disconnected nature of long-term agentic memory.
“While Large Language Models (LLMs) excel at generalized reasoning, standard retrievalaugmented approaches fail to address the disconnected nature of long-term agentic memory.”
From Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation (2026) · cites this paper as Zhu et al., 2025
The Introduction says standard RAG can produce isolated pieces of information because retrieved chunks lack intrinsic relationships. The Abstract similarly describes retrieval of isolated chunks. Neither passage addresses long-term agentic memory, so applying this limitation to that setting goes beyond what the paper establishes. (LLM, conf=0.88)
Experimental — accuracy not yet measuredExisting systems suffer from Contextual Isolation, a failure mode stemming from the implicit Search Assumption that the relevance of a past memory is strictly determined by its semantic proximity to the current query.
“We argue that existing systems suffer from Contextual Isolation , a failure mode stemming from the implicit Search Assumption : that the relevance of a past memory is strictly determined by its semantic proximity to the current query (Zhu et al., 2025; Edge et al., 2025; Sarthi et al., 2024).”
From Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation (2026) · cites this paper as Zhu et al., 2025
The Introduction says similarity-based RAG retrieval can produce chunks that lack intrinsic relationships and become isolated pieces of information. That supports the proposed failure mode for retrieved documents or chunks, but the paper does not discuss past memories or say their relevance is strictly determined by semantic proximity; it also describes keyword-based retrieval. (LLM, conf=0.84)
Experimental — accuracy not yet measuredStandard RAG treats memory as a static library to be indexed, rather than a dynamic network to be reasoned over, which imposes a critical limitation on reasoning agents.
“While effective for factual lookup (Borgeaud et al., 2022), standard RAG imposes a critical limitation on reasoning agents: it treats memory as a static library to be indexed, rather than a dynamic network to be reasoned over (Gutiérrez et al., 2024; Zhu et al., 2025).”
From Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation (2026) · cites this paper as Zhu et al., 2025
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Tran, V., & Nguyen, L. H. B. (2026) · Communications in computer and information science · doi:10.1007/978-981-92-2597-2_33
Dhanpal, R., Regu, V., Devarapally, R., Wang, J., Prasad, S. K., Srivastava, S., Zuo, L., & Zhu, M. (2026) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw71298.2026.00125
SemGraphRAG: Hybrid RAG with Semantic Knowledge Graph Integration
Lam, A. N., Elvesæter, B., Avogadro, R., Thomas, A., & Ma, X. (2026) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac69091.2026.00265
TLC-RAG: A Plain Text RAG Method Based on Triples and Logic Chains
Yang, W., Cui, L., & Xue, N. (2026) · IEEE International Conference on Big Data and Artificial Intelligence (BDAI) · doi:10.1109/bdai70753.2026.11655161
Security-Augmented Use Case Flow Refinement for LLM-Based Agentic Systems
Wang, G., Li, B., Wu, J., & Shao, Z. (2026) · Electronics · doi:10.3390/electronics15184135
Dube, B. M., & Fonou-Dombeu, J. V. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10100324
Shaik, H., Villuri, G., & Doboli, A. (2026) · IEEE Conference on Information Technology and Data Science (CITDS) · doi:10.1109/citds70571.2026.11706879
Structure-Constrained Knowledge Reasoning for Manufacturing Process Planning Support
Feng, X., & Peihan, W. (2026) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case69030.2026.11704283
PARM: Passage-Anchored Relation-Aware Multi-Hop Retrieval for Evidence-Grounded Question Answering
Wang, M., Wu, F., Jia, G., Han, L., Shi, R., Zhu, H., Qi, Z., & Xu, F. (2026) · Applied Sciences · doi:10.3390/app16209995
Ontology-Enriched GraphRAG: A Case Study in Portuguese Geoscience Question Answering
da Rocha, T. R., Netto, J. C., & Becker, K. (2026) · Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) · doi:10.5753/kdmile.2026.31150
KG-RAG: A Hybrid Retrieval Architecture for Institutional Normative Documents
Pereira, C. Y. S., Araújo, G. S., Silva, M. S., dos Santos, S. M., Rêgo, R. C. D. S., & Lobato, F. M. F. (2026) · Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) · doi:10.5753/kdmile.2026.31792
Beyond Retrieval: Bidirectional Neuro-Symbolic Validation for Reliable Graph-RAG
Araújo, L. A. D. O., & Vega‐Oliveros, D. A. (2026) · Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) · doi:10.5753/kdmile.2026.32086
CodeSeed: An Evidence-Verified Agent with Bounded Reflection for Algorithm Learning Archives
Li, J., & Li, Y. (2026) · International Conference of Data and Information Systems (DIS) · doi:10.1109/dis72268.2026.11725596
Self-citationExploring Metamodels and LLM Agents for Planning Tasks in Modular Material Handling Systems
Makarem, H., & Ohnemus, L. (2026) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa68377.2026.11724741
Evidence-Traceable KG-RAG for Potato Disease and Pest Question Answering
Ni, Y., Hu, S., Gao, L., & Tang, M. (2026) · International Conference of Data and Information Systems (DIS) · doi:10.1109/dis72268.2026.11725708
Wiangnak, P., Kertkeidkachorn, N., & Shirai, K. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-42026-8_18
How Graphs Ground Large Language Models Counterfactuals for Subgraph Verbalizations
Buchmann, S., Slany, E., & Scheele, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-42015-2_23
Sudharson, D., Vimal, E. A., Srinivasan, N. G., Krishnaprasath, V. T., K, H. S., & S, H. V. (2026) · doi:10.1109/icsl-dsga70378.2026.11736739
Task-Aware Knowledge Reasoning for Flood-Season Decision Support in Cascade Hydropower Dispatch
Chuai, Y., Xu, J., Li, Q., Zhao, Z., & Zhang, Z. (2026) · IEEE International Conference on Artificial Intelligence, Computer Science and Information Processing (AICSIP) · doi:10.1109/aicsip69702.2026.11744037
- Authors
- Zhu, X., Xie, Y., Liu, Y., Li, Y., & Hu, W.
- Affiliations
- Nanjing University (CN)
- Alibaba Group (China) (CN)
- Venue
- Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
- Published
- 2025
- Pages
- 8912-8924
- OpenAlex
- W4411119292
- Cited by
- 91
- References
- 0
- Citations by year
- 2026: 74 · 2025: 17
- FWCI
- 86.75
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (62272219)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- knowledge graphs, retrieval-augmented generation, large language models
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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