MetaJoin is a label‑free framework that adaptively selects join methods for heterogeneous tables by first choosing an initial specialist based on input evidence and then validating or repairing the result using specialist‑specific output evidence. It introduces MNN Join, a transformation specialist that finds string transformations by maximizing mutual‑nearest‑neighbor coverage and uses reciprocal validation. Evaluated on 670 datasets, MetaJoin achieves a macro row‑F1 of 0.817, surpassing a benchmark oracle and outperforming other label‑free policies by over 0.13, while MNN Join dominates all transformation baselines on 609 transformation‑heavy datasets.
arXiv:2601.13111v3 Announce Type: replace-cross Abstract: Realistic text-to-SQL workflows often require joining multiple tables. As a result, accurately retrieving the relevant set of tables becomes...
arXiv:2606. 29532v1 Announce Type: cross Abstract: Integrating unstructured data into relational database systems is increasingly important as demand grows for natural language querying and analysis.
TabJoinBench is a benchmark designed to evaluate join discovery methods across semantic, relational, and hybrid data lake scenarios. It constructs query‑candidate pairs using source‑specific validation strategies and introduces systematic perturbations—structural, representation, and semantic—while preserving reliable ground truth. The benchmark evaluates set‑based, feature‑based, learned, and language‑model embedding approaches, and publicly releases processed datasets, annotations, and a generation pipeline for reproducible research.
arXiv:2606. 19079v2 Announce Type: replace Abstract: Parameter-efficient fine-tuning (PEFT) has led to model ecosystems in which a single backbone is paired with many task-specialized adapters.
JoinGR is a join‑aware table retrieval method that treats a database’s join graph as the retrieval space. It represents columns as graph nodes and both intra‑table and foreign‑key relationships as typed edges, then selects anchor tables, traverses join edges with a query‑conditioned scorer, and aggregates edge deposits into table scores. Experiments on BIRD, Spider, and BEAVER show that JoinGR matches or surpasses strong retrieval baselines, especially improving recall on multi‑hop table requirements, and its learned scorer transfers across benchmarks, indicating reusable join‑graph traversal behavior.
arXiv:2607. 24688v1 Announce Type: cross Abstract: Entity matching identifies records that refer to the same real-world entity.
MetaSieve is a metapath selection layer that reduces subgraph size in relational deep learning by pruning uninformative metapaths using SQL join and aggregation statistics. It scores candidate metapath extensions with a lightweight function that favors informative yet lightweight paths, discarding those below a threshold. The method is independent of GNN parameters and, when applied to the RelBench benchmark, consistently cuts per‑epoch training time while preserving or improving accuracy.
arXiv:2606. 08090v1 Announce Type: cross Abstract: Evaluating a natural-language yes/no predicate over a document corpus under an accuracy target - the semantic filter - is a cornerstone of LLM-based data processing.
arXiv:2603. 22455v5 Announce Type: replace Abstract: Reusable skills let LLM agents package task-specific procedures, tool affordances, and execution guidance into modular building blocks.
arXiv:2606. 09323v1 Announce Type: new Abstract: Tabular encoders are usually evaluated inside task-specific end-to-end pipelines, so models from different training paradigms are difficult to compare directly even when they operate on similar tabular signals.
arXiv:2607. 28680v1 Announce Type: cross Abstract: Entity linking in tables matches short and ambiguous cell mentions to their corresponding knowledge-base entities.
SAGE (Self-Adaptive Generative Execution) introduces a unified framework for integrating AI functions into SQL by defining three typed primitives—AI_SCALAR, AI_AGG, and AI_JOIN—that correspond to the relational roles of transforming rows, aggregating groups, and joining row pairs. The framework standardizes a confidence-gated execution interface and tailors physical strategies to each primitive’s shape, with AI_JOIN employing predicate analysis and a recipe card to select optimal execution plans. Evaluations across scalar, aggregate, and join workloads demonstrate that SAGE consistently improves execution quality and efficiency, achieving the best overall SemBench performance and dramatically reducing model calls in factorable joins.