CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation

Researchers introduce CA-SQL, an advanced Text-to-SQL pipeline that dynamically allocates computational resources based on task complexity to improve LLM reasoning. The method achieves state-of-the-art performance on the BIRD benchmark's challenging tier using only GPT-4o-mini, outperforming larger models and demonstrating the efficiency gains possible through intelligent inference-time optimization.

CA-SQL represents a meaningful advancement in how large language models approach complex database query generation tasks. Rather than applying uniform computational effort across all problems, the system estimates task difficulty and scales exploration breadth accordingly, enabling more efficient use of inference resources. This complexity-aware approach mirrors optimization techniques used in other domains and suggests that one-size-fits-all prompting strategies waste computational potential on simpler tasks while under-allocating resources to harder ones.

The technical innovation combines three components: adaptive solution space exploration, evolutionary search-inspired prompt seeding, and a novel voting mechanism for candidate selection. By achieving competitive results with GPT-4o-mini rather than larger models like GPT-4, CA-SQL demonstrates that intelligent algorithmic design can partially compensate for model capacity limitations. This has direct implications for cost-conscious developers and organizations seeking to deploy sophisticated AI systems without proportional increases in inference spending.

The benchmark results are particularly significant because BIRD's "challenging" tier represents genuinely difficult problems where current systems struggle. Reaching 51.72% accuracy on this subset indicates meaningful progress on real-world database query generation, a task with practical applications in data analytics and business intelligence platforms. The 61.06% execution accuracy and 68.77% Soft F1 score on the full development set show the method maintains strong general performance while specializing in harder problems.

Future developments should focus on whether this complexity-aware allocation strategy generalizes to other reasoning-intensive tasks beyond SQL generation, and whether similar techniques can be applied to open-source models with comparable efficiency gains.

• →CA-SQL achieves state-of-the-art results on BIRD benchmark using GPT-4o-mini through dynamic computational resource allocation based on task complexity.
• →Complexity-aware inference strategies demonstrate that model capacity can be partially substituted through algorithmic optimization, reducing inference costs.
• →The method combines adaptive exploration, evolutionary search principles, and novel voting mechanisms to improve solution quality for challenging database queries.
• →Reaching 51.72% accuracy on BIRD's challenging tier represents significant progress on genuinely difficult text-to-SQL problems.
• →Results suggest that one-size-fits-all prompting wastes computational resources and that intelligent difficulty estimation can improve both efficiency and performance.