CA-SQL: Complexity-Aware Inference Time Reasoning for Text-to-SQL via Exploration and Compute Budget Allocation
CA-SQL tackles a persistent weakness in LLM-based database query generation by dynamically adjusting exploration breadth based on task complexity. The approach combines adaptive compute allocation with evolutionary search principles to improve performance on hard instances in the BIRD benchmark, addressing a core limitation of current inference-time reasoning methods. This work signals growing sophistication in how systems allocate computational resources during generation, a pattern likely to influence broader reasoning architectures beyond SQL tasks.
Modelwire context
ExplainerCA-SQL's core insight is that not all SQL queries need equal computational effort. By measuring task complexity upfront and allocating search breadth accordingly, the system avoids wasting compute on simple queries while investing heavily where it matters, rather than applying a one-size-fits-all inference budget.
This connects directly to the AutoTTS work from earlier this week, which automated discovery of test-time scaling strategies by replacing hand-tuned heuristics with agent-driven search. CA-SQL takes that principle further by making compute allocation itself responsive to input difficulty rather than static. Both papers treat inference-time reasoning as a resource optimization problem rather than a fixed procedure. The difference: AutoTTS discovers what strategies to use; CA-SQL discovers how much of each strategy to apply per instance. Together they suggest the field is moving from 'how do we reason at test time' to 'how do we allocate reasoning budget efficiently'.
If CA-SQL's gains on BIRD's hard instances (where complexity-aware allocation should matter most) exceed its gains on easy instances by more than 5 percentage points, that confirms the complexity signal is doing real work. If performance is flat across difficulty tiers, the method is just a more expensive search procedure with no actual adaptation.
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MentionsCA-SQL · BIRD · Bird-Bench · Text-to-SQL
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