The article discusses how calibrated decision models can manage high‑frequency graph decisions while large language models (LLMs) concentrate on reasoning, synthesis, and open‑ended generation. It introduces GraphRAG with TypeSafe Jev as a system‑one approach to building scalable knowledge graphs. The focus is on separating decision‑making from generative tasks to improve efficiency and reliability.
By Partha Sarkar
The paper introduces a novel LLM‑driven multi‑agent pipeline that converts relational databases into graph databases by standardizing table and column names and iteratively refining the graph schema through ETL, Analyzer, and Graph agents. The resulting graph database meets accuracy, groundedness, and faithfulness criteria and shows significant performance gains, achieving 85.6% Q&A accuracy—12.12% higher than an SQL agent on PostgreSQL—and reducing latency by roughly threefold on a BFSI dataset. This demonstrates an efficient, automated method for transforming tabular data into a more intuitive and faster‑executing graph format.
By Dinh-Khanh Pham, Quy-Anh Dang, Lam Mai Thanh, Khanh Bui, Truong-Son Hy
arXiv:2608. 15834v1 Announce Type: new Abstract: Tool-calling LLM agents navigate unfamiliar codebases with a handful of generic primitives for listing, reading and searching files (ls, cat, grep).
By Marius Dragic, Ruben Ifrah, Alexandre Rio
arXiv:2606. 11560v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems.
By Arijit Khan, Longxu Sun, Xin Huang
Large Language Models (LLMs) have advanced rapidly, but their limitations in structured and multi-hop reasoning underscore the need for graph-native, synergistic artificial intelligence (AI) systems. Graph-structured data underpins critical applications across social, biological, financial, transportation, web, and knowledge domains, making it essential to understand how LLMs can leverage graph computation for grounded, context-rich inference.
arXiv:2605. 26874v2 Announce Type: replace-cross Abstract: LLM-based agents for industrial asset operations show limited accuracy when reasoning over flat document stores.
By Madhulatha Mandarapu, Sandeep Kunkunuru
arXiv:2408. 03910v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) excel in stand-alone code tasks like HumanEval and MBPP, but struggle with handling entire code repositories.
By Xiangyan Liu, Bo Lan, Zhiyuan Hu, Yang Liu, Zhicheng Zhang, Fei Wang, Michael Shieh, Wenmeng Zhou
arXiv:2508. 02548v3 Announce Type: replace-cross Abstract: We propose KG-ER, a conceptual schema language for knowledge graphs that describes the structure of knowledge graphs independently of their representation (relational databases, property graphs, RDF) while helping to capture the semantics of the information stored in a knowledge graph.
By Enrico Franconi, Beno\^it Groz, Jan Hidders, Nina Pardal, S{\l}awek Staworko, Jan Van den Bussche, Piotr Wieczorek
arXiv:2608.21156v1 Announce Type: cross
Abstract: LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms includi...
By Yuyuan Feng, Zhishang Xiang, Chaobin Yang, Qichao Ma, Zerui Chen, Yujing Zhang, Ke Huang, Chuanjie Wu, Zhaoxu Liu, Yili Wang, Xin He, Jiapu Wang, Zijin Hong, Hao Chen, Yuanchen Bei, Kun Wang, Shengyuan Chen, Ningyu Zhang, Enyan Dai, Linhao Luo, Qingyi Pan, Qi Wang, Wenqi Fan, Guangjing Wang, Na Zou, Yangqiu Song, Xin Wang, Zechao Li, Xia Hu, Qing Li, Xiao Huang, Zhihong Zhang, Jinsong Su, Qinggang Zhang, Yi Chang
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities,...
arXiv:2605.14259v3 Announce Type: replace
Abstract: Applying Large Language Models (LLMs) to heterogeneous enterprise systems is hindered by hallucinations and failures in multi-hop, n-ary reasoning....
By Ling Wang, Xin Liu, Songnan Liu, Jianan Wang, Cheng Cheng, Yihan Zhu, Enyu Li, Yu Xiao, Jiangyong Xie, Duogong Yan, Jiangyi Chen
arXiv:2602.16715v2 Announce Type: replace
Abstract: We explore the potential of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Graph-based RAG (GraphRAG) for generating Desig...
By H. Sinan Bank, Daniel R. Herber