APEX-SQL: Talking to the data via Agentic Exploration for Text-to-SQL

2026-02-11

AI Summary

Existing Text-to-SQL systems struggle with semantic ambiguity and limited scalability in complex enterprise databases due to their reliance on static schema representations. This work proposes APEX-SQL, a novel framework that shifts the Text-to-SQL paradigm from passive translation to active exploration. During schema linking, APEX-SQL integrates logical planning, dual-path pruning, and parallel data profiling to generate hypotheses, which are then validated through global topological synthesis. In the SQL generation phase, it employs a deterministic mechanism to retrieve exploration instructions, thereby enhancing semantic accuracy. The approach significantly improves reasoning capabilities over complex databases, achieving execution accuracies of 70.65% on BIRD and 51.01% on Spider 2.0-Snow—outperforming current baselines while reducing token consumption.

Technology Category

• Natural Language Processing: Information Extraction
• Data Mining & Knowledge Management: Intelligent Query Processing
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs

Abstract

Text-to-SQL systems powered by Large Language Models have excelled on academic benchmarks but struggle in complex enterprise environments. The primary limitation lies in their reliance on static schema representations, which fails to resolve semantic ambiguity and scale effectively to large, complex databases. To address this, we propose APEX-SQL, an Agentic Text-to-SQL Framework that shifts the paradigm from passive translation to agentic exploration. Our framework employs a hypothesis-verification loop to ground model reasoning in real data. In the schema linking phase, we use logical planning to verbalize hypotheses, dual-pathway pruning to reduce the search space, and parallel data profiling to validate column roles against real data, followed by global synthesis to ensure topological connectivity. For SQL generation, we introduce a deterministic mechanism to retrieve exploration directives, allowing the agent to effectively explore data distributions, refine hypotheses, and generate semantically accurate SQLs. Experiments on BIRD (70.65% execution accuracy) and Spider 2.0-Snow (51.01% execution accuracy) demonstrate that APEX-SQL outperforms competitive baselines with reduced token consumption. Further analysis reveals that agentic exploration acts as a performance multiplier, unlocking the latent reasoning potential of foundation models in enterprise settings. Ablation studies confirm the critical contributions of each component in ensuring robust and accurate data analysis.

Problem

• Text-to-SQL
• semantic ambiguity
• enterprise databases
• schema representation
• large language models

Innovation

• Agentic Exploration
• Hypothesis-Verification Loop
• Schema Linking
• Data Profiling
• Text-to-SQL