As natural language becomes a key interface for controlling text-to-image generation, users specify not only which visual concepts should appear but also how entities are constrained by attributes, relations, quantifiers, and cross-entity dependencies. Reliable controllable generation therefore requires generalization over semantic structure, not merely over novel combinations of concepts. Existing benchmarks mainly evaluate conceptual compositional generalization, while leaving the internal semantic structure of instructions underexplored. We study structural compositional generalization: whether text-to-image models can generalize across increasingly coupled semantic dependencies. We introduce FormalImG, a first-order-logic-based benchmark with 4,000 instructions across three complexity levels in two scenarios, together with computable structural compositional complexity and ε-structural compositional generalizability. Experiments on advanced text-to-image models show consistent degradation as structural complexity increases; even the strongest evaluated model generalizes only up to G_0.7 = 6 in Natural and G_0.7 = 4 in Knolling. Further analysis suggests that the main bottleneck lies in language-to-vision instantiation and can be partially mitigated by explicit intermediate layouts. The benchmark and code are available at https://github.com/yhj137/FormalImG.

Large language models (LLMs) have demonstrated impressive performance on reasoning tasks, including mathematical reasoning. However, the current evaluation mostly focuses on carefully constructed benchmarks and neglects the consideration of real-world reasoning problems that present missing or contradictory conditions, known as ill-defined problems. To further study this problem, we develop a large-scale benchmark called Problems with Missing and Contradictory conditions (PMC) containing over 5,000 validated ill-defined mathematical problems. Our preliminary experiments through PMC reveal two challenges about existing methods: (1) traditional methods exhibit a trade-off between solving accuracy and rejection capabilities, and (2) formal methods struggle with modeling complex problems. To address these challenges, We develop Variable-Constraint Search (VCSearch), a training-free framework that leverages formal language to detect ill-defined problems, where a variable-constraint pair search strategy is incorporated to improve the modeling capability of formal language. Extensive experiments demonstrate that VCSearch improves the accuracy of identifying unsolvable problems by at least 12% across different LLMs, thus achieving stronger robust mathematical reasoning ability.