arXiv AI
Jun 24
Entity Resolution via Batched Oracle Queries
arXiv:2606. 24407v1 Announce Type: cross Abstract: We consider an oracle that processes a limited batch of records at a time and clusters those that refer to the same real-world entity.
Entity Resolution via Batched Oracle Queries
We consider an oracle that processes a limited batch of records at a time and clusters those that refer to the same real-world entity. We study how to interrogate such an oracle to resolve entities in a dataset whose size is far larger than a single batch, and where no batch is guaranteed to contain all records of any given entity.
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arXiv Machine Learning
Jul 30
Entity Resolution in Practice: Lessons from a Self-Serve Pipeline
arXiv:2607. 26298v1 Announce Type: new Abstract: We built and evaluated a self-serve entity resolution (ER) system on six benchmarks spanning 864 to 5M records, and three lessons emerged that are absent from existing ER literature.
arXiv AI
Aug 25
MEMONDEMAND: A Memory Management System for Large-Scale Enterprise Data
arXiv:2608.22141v1 Announce Type: new Abstract: Enterprise repositories are large, heteroge- neous, and continuously updated, making re- trieval difficult when efficient access, source- faithful evid...
arXiv AI
Jul 3
Evergreen: Efficient Claim Verification for Semantic Aggregates
arXiv:2604. 26180v2 Announce Type: replace-cross Abstract: With recent semantic query processing engines, semantic aggregation has become a primitive operator, enabling the reduction of a relation into a natural language aggregate using an LLM.
arXiv AI
Sep 1
CORE-T: COherent REtrieval of Tables for Text-to-SQL
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 AI
Jul 29
ScalableRAG: High-Quality RAG at Zero Ingestion Cost
arXiv:2607. 25135v1 Announce Type: new Abstract: Recent advances in RAG aim to optimize for performance by paying high ingestion costs for knowledge ingestion: building knowledge graphs or extracting SQL tables.
arXiv Machine Learning
Jul 13
Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem
arXiv:2607. 09236v1 Announce Type: new Abstract: Machine unlearning in LLMs is the targeted removal of specific knowledge while preserving all other capabilities, critical for privacy and safety.
arXiv AI
Jul 7
Streaming Model Cascades for Semantic SQL
arXiv:2604. 00660v2 Announce Type: replace-cross Abstract: Modern data warehouses extend SQL with semantic operators that invoke large language models on each qualifying row, making per-row inference orders of magnitude more expensive than traditional SQL.
arXiv AI
Sep 18
Efficiently Linking Unstructured Data for Multi-step Reasoning
The paper introduces DASE, a query engine designed to efficiently link unstructured data for multi-step reasoning tasks. DASE combines a multi-step reasoning model, a sparse materialized embedding-similarity join index (SemJI), and a co‑designed execution layer to perform multi‑attribute filtering, multi‑vector search, exact relational joins, and thresholded embedding‑similarity joins. In scientific discovery workloads, DASE outperforms traditional RDBMS, rerank, and vector‑database baselines by 6x to 46x in retrieval speed while maintaining comparable recall, and it serves as a high‑recall prefilter that reduces downstream LLM evaluation cost and improves accuracy on benchmarks such as SemBench E‑Commerce.
arXiv AI
Sep 16
ORDER: Task-Conditioned Routing for Retrieval-Augmented Generation
The paper introduces ORDER, a task‑conditioned retrieval‑augmented generation framework that dynamically adapts both indexing and retrieval strategies to each incoming query. It first clusters questions to learn cluster‑specific chunking, metadata filtering, and reranking settings, then routes queries to the appropriate pre‑built index via nearest‑centroid assignment. Additionally, a supervised query router predicts relevant collections and a Uniform Multi‑source Sampler distributes the retrieval budget evenly across selected sources, yielding superior performance on heterogeneous historical archives compared to existing RAG systems.
arXiv Machine Learning
1d ago
STEER: Reducing Inference Cost in Relational Foundation Models through Semantically Informed Sampling
STEER is a sampling method for relational foundation models that reduces inference cost by focusing on the most relevant tables for a prediction task. It uses a large language model to rank foreign‑key edges in the database schema into relevance tiers, then assigns traversal probabilities based on these tiers. Evaluated on three state‑of‑the‑art RFMs, STEER cuts inference context size by roughly 40% on average while preserving or improving accuracy.
arXiv AI
Jun 30
Database Context Compression for Text-to-SQL on Real-World Large Databases
arXiv:2606. 28601v1 Announce Type: cross Abstract: Recent progress in Text-to-SQL has been driven by stronger language models and prompting strategies, yet performance on real enterprise benchmarks such as Spider 2.