# Toward Multi-Database Query Reasoning for Text2Cypher

arXiv: https://arxiv.org/abs/2605.10373 · HTML: https://mlsanity.com/paper/2605.10373/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 11 May 2026.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 12 of 12)

- [Text2Cypher: Bridging Natural Language and Graph Databases](https://mlsanity.com/paper/2412.10064/) (2024)
- [Is Long Context All You Need? Leveraging LLM's Extended Context for NL2SQL](https://mlsanity.com/paper/2501.12372/) (2025)
- [LinkAlign: Scalable Schema Linking for Real-World Large-Scale Multi-Database Text-to-SQL](https://mlsanity.com/paper/2503.18596/) (2025)
- [RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking](https://mlsanity.com/paper/2504.01346/) (2025)
- [MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL](https://mlsanity.com/paper/2402.10666/) (2024)
- [Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrieval](https://mlsanity.com/paper/2504.09824/) (2025)
- [Conversational Lexicography: Querying Lexicographic Data on Knowledge Graphs with SPARQL through Natural Language](https://mlsanity.com/paper/2505.19971/) (2025)
- [Text2GraphQuery-Bench: A Text to Graph Query Benchmark](https://mlsanity.com/paper/2602.11745/) (2026)
- [Towards Agentic Schema Refinement](https://mlsanity.com/paper/2412.07786/) (2024)
- [Decomposition-Driven Multi-Table Retrieval and Reasoning for Numerical Question Answering](https://mlsanity.com/paper/2603.07950/) (2026)
- [AI Agent-Driven Framework for Automated Product Knowledge Graph Construction in E-Commerce](https://mlsanity.com/paper/2511.11017/) (2025)
- [Fine-Grained Table Retrieval Through the Lens of Complex Queries](https://mlsanity.com/paper/2603.07146/) (2026)

## Abstract (arXiv)

Large language models have significantly improved natural language interfaces to databases by translating user questions into executable queries. In particular, Text2Cypher focuses on generating Cypher queries for graph databases, enabling users to access graph data without query language expertise. Most existing Text2Cypher systems assume a single preselected graph database, where queries are generated over a known schema. However, real-world systems are often distributed across multiple independent graph databases organized by domain or system boundaries, where relevant information may span multiple sources. To address this limitation, we propose a shift from single-database query generation to multi-database query reasoning. Instead of assuming a fixed execution context, the system must reason about (i) relevant databases, (ii) how to decompose a question across them, and (iii) how to integrate partial results. We formalize this setting through a three-phase roadmap: database routing, multi-database decomposition, and heterogeneous query reasoning across database types and query languages. This work provides a structured formulation of multi-database reasoning for Text2Cypher and identifies challenges in source selection, query decomposition, and result integration, aiming to support more realistic and scalable natural language interfaces to graph databases.