KathDB-FAO is a query evaluation subsystem for the KathDB multimodal DBMS that transforms natural language queries into executable plans. Each operator in the plan is a function synthesized during evaluation, enabling query‑specific optimizations. The system extracts atomic actions, establishes contracts, groups them for efficiency, and synthesizes functions on the fly, achieving an average 58.8% reduction in execution cost on SemBench compared to the next best system.
arXiv:2607. 20489v1 Announce Type: new Abstract: Text-to-SQL has advanced rapidly with large language models, but complex database queries still require reasoning beyond one-shot generation, including multi-step decomposition, execution-based diagnosis, and targeted correction.
arXiv:2606. 18108v1 Announce Type: cross Abstract: We develop a text-to-SQL (structured query language) system based on large language models (LLMs) using in-context learning and apply it to the Automatic Learning for the Rapid Classification of Events (ALeRCE) astronomical database.
text2ql is an open‑source Python framework that enables natural language querying of databases without relying on large language models at query time. It uses a language‑agnostic intermediate representation (QueryIR) and a pluggable renderer to support both SQL and GraphQL targets through a single seven‑stage detection pipeline. In deterministic mode, it achieves 100% execution accuracy with a median latency of 3.2 ms, while the LLM‑backed mode delivers 62‑70% exact match and 84‑91% execution accuracy on benchmark samples.
text2ql is an open‑source Python framework that enables natural language querying of databases without being limited to SQL, without requiring large language model inference at query time, and with a runtime confidence score for each generated query. It uses a language‑agnostic Intermediate Representation (QueryIR) and a pluggable renderer that supports both SQL and GraphQL through a single seven‑stage detection pipeline. In deterministic mode, it achieves 100% execution accuracy with a median latency of 3.2 ms, while the LLM‑backed mode reaches 62‑70% exact match and 84‑91% execution accuracy on benchmark samples.
arXiv:2607. 18241v1 Announce Type: new Abstract: Large language models (LLMs) excel at analyzing individual documents but break down on exhaustive, cross-entity analytical questions over enterprise-scale datasets due to context overflow, loss of per-entity attribution, and linear latency from sequential tool calls.
arXiv:2606. 17821v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in translating natural language to SQL, yet existing methods still falter on complex queries requiring multi-step, data-aware reasoning.
arXiv:2607. 21756v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in database-backed applications to classify tuples, filter records using semantic predicates, extract structured attributes, and enrich query results.
arXiv:2606. 08300v1 Announce Type: new Abstract: Many real-world queries over personal data span multiple applications and require structured planning, as individual tools expose only partial information.
QueryGraph is a system that transforms natural language queries into structured graphs for reliable multi-tool execution. It employs a deterministic planner that uses depth-first search to resolve tool dependencies and combine results, improving reliability over traditional keyword searches. The approach works well even with smaller or locally hosted large language models, achieving high accuracy in multi-step, cross-tool queries.
arXiv:2607. 06229v1 Announce Type: cross Abstract: Major cloud data platforms now expose large language model capabilities as native SQL functions, enabling analysts to perform classification, filtering, sentiment analysis, extraction, similarity search, and aggregation within ordinary SQL queries.
arXiv:2608.22651v1 Announce Type: new Abstract: Modern text-to-SQL systems have become increasingly elaborate, relying on schema-linking modules, retrieval-augmented prompting, candidate generation,...
arXiv:2607. 10911v1 Announce Type: cross Abstract: In the age of large language models, Natural Language to SQL (NL2SQL) translation remains an open problem with many useful applications.