# REaR: Retrieve, Expand and Refine for Effective Multitable Retrieval

arXiv: https://arxiv.org/abs/2511.00805 · HTML: https://mlsanity.com/paper/2511.00805/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2025.

## Built on by (most cited first, 4 of 4)

- [Topo-RAG: Topology-aware retrieval for hybrid text-table documents](https://mlsanity.com/paper/2601.10215/) (2026)
- [CORE-T: COherent REtrieval of Tables for Text-to-SQL](https://mlsanity.com/paper/2601.13111/) (2026)
- [Open Tabular Insight Extraction: Where Do We Stand, and Where Should We Go?](https://mlsanity.com/paper/2609.07629/) (2026)
- [JoinGR: Learning to Traverse Join Graphs for Table Retrieval](https://mlsanity.com/paper/2610.01064/) (2026)

## Abstract (arXiv)

Answering natural language queries over relational data often requires retrieving and reasoning over multiple tables, yet most retrievers optimize only for query-table relevance and ignore table table compatibility. We introduce REAR (Retrieve, Expand and Refine), a three-stage, LLM-free framework that separates semantic relevance from structural joinability for efficient, high-fidelity multi-table retrieval. REAR (i) retrieves query-aligned tables, (ii) expands these with structurally joinable tables via fast, precomputed column-embedding comparisons, and (iii) refines them by pruning noisy or weakly related candidates. Empirically, REAR is retriever-agnostic and consistently improves dense/sparse retrievers on complex table QA datasets (BIRD, MMQA, and Spider) by improving both multi-table retrieval quality and downstream SQL execution. Despite being LLM-free, it delivers performance competitive with state-of-the-art LLM-augmented retrieval systems (e.g.,ARM) while achieving much lower latency and cost. Ablations confirm complementary gains from expansion and refinement, underscoring REAR as a practical, scalable building block for table-based downstream tasks (e.g., Text-to-SQL).