From Syntactic to Semantic: Controlling Text-to-SQL Complexity via Semantically Anchored Targeted Synthesis

Text-to-SQL failures are often driven not by surface SQL syntax, but by semantic structures that govern how queries are scoped, composed and constrained. We introduce a semantically grounded Depth-Width complexity coordinate, where Depth measures the longest chain of cross-scope dependencies and Width characterizes within-scope combinatorial semantic burden. To operationalize this coordinate for controlled analysis and synthesis, we define structural motifs as atomic, detectable, verifiable, and synthesizable patterns that anchor semantic complexity. We cast synthesis as a generate-and-verify loop with multi-stage quality validation to filter decorative complexity. On Spider-dev and BIRD-dev, our motif taxonomy captures real-world structure with high coverage, reaching 70.6% and 88.0% overall, respectively, and achieving near-complete coverage on high-complexity subsets. Finally, we propose a complexity-controlled targeted synthesis pipeline that injects motifs into SQL skeletons, instantiates parameters via data probing, and produces executable training data annotated with Depth, Width, and motif labels for expansion toward structurally complex semantic regions. Training on validated synthetic data yields consistent execution-accuracy gains, with improvements concentrated in high-complexity regions and in Depth-Width buckets corresponding to these failure-prone structures.