Dense retrieval has become the dominant paradigm for large-scale table retrieval. However, existing contrastive retrievers often suffer from bag-of-words degradation: they rely on lexical overlap rather than row–column bindings. To quantify and mitigate this, we propose an adversarial benchmark and a structure-aware retrieval framework. Our benchmark uses "evidence cell" anchoring and a Homologous Structural Swapping mechanism to construct lexically equivalent but structurally invalid negative samples. Our model uses text generation as a structural regularizer for dense table representations. It decouples row and column anchors through orthogonal attention pooling, and employs dynamic masking to encourage autoregressive reconstruction. This process encourages the retrieval representations to retain table content and structure-related cues. Extensive experiments on OTT-QA, NQ-Tables, and our adversarial benchmark demonstrate the effectiveness of our method. It achieves competitive performance on conventional retrieval datasets and substantially improves structural robustness under adversarial settings.