mlsanityData to 14 Oct 2026

Can LLMs Normalize Databases? A Benchmark and Multi-Agent Framework for Schema Normalization

arXiv 2609.11141 ↗First posted 10 Sep 2026cs.CLcited by 0

On the map: LLM agents and tool use → Web, search, and research agents

Abstract (arXiv)

Large Language Models (LLMs) are increasingly used to generate structured outputs, but their reliability remains unclear when those outputs must satisfy database-level constraints. We study this issue through database normalization, involving reasoning about functional dependencies, lossless join decompositions, and inter-table constraints. We introduce a Database Normalization Benchmark (DNBENCH), comprising 3,275 samples for evaluating LLM-driven database normalization from 1NF to BCNF. DNBENCH uses a three-axis protocol to measure semantic equivalence, structural accuracy, and logical validity. Across Single, Complex, and Real World levels, DNBENCH uncovers recurring failures in dependency inference, schema decomposition, and inter-table constraint reconstruction. We further propose Multi-Agent Reasoning for Schemas (MARS), which separates evidence extraction, violation diagnosis, and decomposition planning from schema generation and verification. MARS improves the DNB-SCORE by 82.0% over the single-prompt baseline. All artifacts will be released upon acceptance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use. Builds on 8 of its 37 references that are in the map. First posted on arXiv 10 Sep 2026.

Builds on

8 of 37 references.

References · 8

  1. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  2. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  3. 2018Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Taskcited by 1,139
  4. 2023ChatDev: Communicative Agents for Software Developmentcited by 919
  5. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 688
  6. 2025Judge's Verdict: A Comprehensive Analysis of LLM Judge Capability Through Human Agreementcited by 25
  7. 2024NormTab: Improving Symbolic Reasoning in LLMs Through Tabular Data Normalizationcited by 16
  8. 2025Database Normalization via Dual-LLM Self-Refinementcited by 1

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  2. 2025Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Collaboration
  3. 2025Text2Schema: Filling the Gap in Designing Database Table Structures based on Natural Language
  4. 2025RAISE: Reasoning Agent for Interactive SQL Exploration
  5. 2024Towards Agentic Schema Refinement
  6. 2025BAPPA: Benchmarking Agents, Plans, and Pipelines for Automated Text-to-SQL Generation
  7. 2026ACTS-SQL: Agentic and Critic-Oriented Tree-Structured SQL Correctness with Large Language Models
  8. 2026DW-Bench: Benchmarking LLMs on Data Warehouse Graph Topology Reasoning