RelAgent is an LLM-based autonomous data scientist designed for relational learning. It operates in two phases: first, the LLM agent constructs SQL feature programs and selects a predictive model using database, validation, and evaluation tools; second, the resulting program is executed without further LLM calls. The final predictor combines SQL queries and a classical model, providing fast, deterministic, and interpretable predictions that rely solely on human-readable query-defined features.
By Xingyue Huang, Louis Tichelman, Jinwoo Kim, Krzysztof Olejniczak, \.Ismail \.Ilkan Ceylan
The paper presents a multi‑agent, persona‑driven NL2SQL framework that addresses common LLM shortcomings such as schema hallucination, syntax errors, and intent mismatch. It incorporates domain relevance detection, schema‑grounded knowledge construction, KPI‑aware SQL synthesis, syntax‑level self‑repair via sqlite feedback, and semantic consistency checking through SQL‑to‑NL reverse engineering. Evaluated on 60 queries, the system achieves 90% correctness using Mistral across seven coordinated LLM calls, demonstrating a significant improvement in reliability for high‑stakes environments.
By Anjali Vemula, Akshat Dasula
Build2SPARQL is a large-scale benchmark dataset for translating natural-language questions into SPARQL queries over building knowledge graphs. The dataset is generated by a KG‑grounded pipeline that produces 6,136 executable SPARQL queries and 30,680 corresponding natural-language questions across six query-pattern families and five vocabulary registers, covering 201 building KGs. Human validation shows high semantic fidelity, naturalness, and operational plausibility, and retrieval‑augmented evaluation demonstrates significant accuracy gains for open‑weight language models.
By Wooyoung Jung
arXiv:2602. 11745v2 Announce Type: replace Abstract: Graph models are fundamental to data analysis in domains rich with complex relationships.
By Songlin Lyu, Lujie Ban, Zihang Wu, Tianqi Luo, Jirong Liu, Ayoub Moussaid, Oskar van Rest, Heng Lin, Chenhao Ma, Nan Tang, Shipeng Qi, Yongchao Liu, Zhan Qiu, Juelu Zhang, Jiajun Zheng
arXiv:2606. 03705v1 Announce Type: new Abstract: Knowledge Graphs (KGs) are widely used to mitigate the limitations of Large Language Models (LLMs), such as outdated knowledge and hallucinations.
By Weiwei Ding, Zixuan Li, Long Bai, Zhuo Chen, Kun Su, Fei Wang, Xiaolong Jin, Jin Zhang, Jiafeng Guo, Xueqi Cheng
arXiv:2606. 06003v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) fails systematically on queries requiring structural reasoning over interconnected entities.
By Grama Chethan
The paper introduces QRAKEN, a training‑free, ontology‑agnostic neurosymbolic pipeline that grounds natural‑language queries to RDF knowledge graphs using empirical graph evidence instead of schema expectations. QRAKEN distills a compact TTQL representation offline, then uses it online to guide large language models, providing deterministic checks and an iterative refinement loop. On the CK25 benchmark, QRAKEN achieves a strict F1 of 0.643–0.652 with GPT‑4.1 mini and GPT‑5.4, outperforming state‑of‑the‑art systems and demonstrating the effectiveness of empirical pattern distillation over schema‑only approaches.
By Remo Grillo, Lukas Klic, Giovanni Colavizza
arXiv:2610.17305v1 Announce Type: new
Abstract: Large Language Models (LLMs) have recently improved Text-to-SQL generation, but their computational cost can hinder deployment in resource-constrained...
By Bilel Abderrahmane Benziane, Xavier Schepler
The paper introduces FlockMTL, an extension for database management systems that deeply integrates large language models and retrieval‑augmented generation into DuckDB. It provides model‑driven scalar and aggregate functions, cost‑based optimizations like batching and caching, and new SQL DDL abstractions (PROMPT and MODEL) to treat LLMs as first‑class schema objects. These features aim to simplify the development of knowledge‑intensive analytical applications by reducing the effort required to orchestrate heterogeneous data systems and manage LLM context.
By Anas Dorbani, Sunny Yasser, Jimmy Lin, Amine Mhedhbi
arXiv:2502. 11491v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown remarkable capabilities in natural language processing.
By Runxuan Liu, Bei Luo, Jiaqi Li, Baoxin Wang, Ming Liu, Dayong Wu, Shijin Wang, Bing Qin
arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.
By Blake G. Fitch, Cato Elia Kurtz
The paper introduces GLaMoR, a reasoning pipeline that converts OWL ontologies into graph-structured data and applies a Graph Language Model (GLM) for consistency checking. It addresses the challenge of verifying ontology consistency, especially for large ontologies where classical reasoners become computationally expensive. Experiments on NCBO BioPortal ontologies show that GLaMoR achieves 95% accuracy and is 20 times faster than traditional reasoners.
By Justin M\"ucke, Ansgar Scherp