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).
@article{agarwal2025rear,title={REaR: Retrieve, Expand and Refine for Effective Multitable Retrieval},author={Agarwal, Rishita and Singhal, Himanshu and Chen, Peter Baile and Choudhury, Manan Roy and Roth, Dan and Gupta, Vivek},journal={ACL Main 2026},year={2026},url={https://arxiv.org/abs/2511.00805}}
GAFSV-Net: A Vision Framework for Online Signature Verification
@misc{singhal2026gafsvnetvisionframeworkonline,title={GAFSV-Net: A Vision Framework for Online Signature Verification},author={Singhal, Himanshu and Sundaram, Suresh},year={2026},eprint={2605.00120},archiveprefix={arXiv},primaryclass={cs.CV},url={https://arxiv.org/abs/2605.00120}}
