Jigsaw puzzle solving remains difficult because models must reconcile local fragment cues with global structure. Most prior work leans solely on visual signals (edge or texture coherence) and rarely exploits natural-language descriptions, which are especially helpful for puzzles with eroded gaps. We introduce a vision–language framework that uses textual context to guide assembly. At its core, the Vision–Language Hierarchical Semantic Alignment (VLHSA) module aligns image patches with text via multi-level matching—from local tokens to global summaries—within a multimodal design that couples dual visual encoders with language features for cross-modal reasoning. Across multiple datasets, the method surpasses the state of the art, including a 14.2 percentage point gain in piece accuracy; ablations identify VLHSA as the principal source of improvement. These results suggest a practical shift for jigsaw solving: augmenting vision with language to resolve ambiguous placements