A significant ``modality gap" exists between the abundance of text-only data and the increasing power of multimodal models. This work systematically investigates whether images generated on-the-fly by Text-to-Image (T2I) models can serve as a mechanism to unlock latent visual priors for text-centric reasoning. Through a comprehensive evaluation framework on text classification, we analyze the impact of critical variables, including T2I model quality (e.g., Flux.1, SDXL), prompt engineering strategies, and multimodal fusion architectures. Our findings demonstrate that this ``synthetic perception" can yield significant performance gains by effectively projecting text into a visual semantic space, even when augmenting strong large language model baselines like Llama-3 and Qwen-2.5. We show that this approach serves as a form of cross-modal probing, mitigating the sensory deprivation inherent in pure text training. However, the effectiveness is highly conditional, depending on the semantic alignment between text and the generated image, the task's visual groundability, and the generative fidelity of the T2I model. Our work establishes a rigorous benchmark for this paradigm, demonstrating its viability as a pathway to enrich language understanding in traditionally unimodal scenarios.

翻译：在纯文本数据丰富性与多模态模型能力日益增强之间，存在显著的“模态鸿沟”。本研究系统性地探讨了由文本到图像（T2I）模型即时生成的图像，能否作为一种机制来解锁文本中心推理所需的潜在视觉先验。通过在文本分类任务上构建的综合评估框架，我们分析了关键变量的影响，包括T2I模型质量（例如Flux.1、SDXL）、提示工程策略以及多模态融合架构。我们的研究结果表明，这种“合成感知”能够通过将文本有效投射到视觉语义空间中，带来显著的性能提升，即使是在增强如Llama-3和Qwen-2.5等强大的大语言模型基线时亦然。我们证明，该方法作为一种跨模态探针，能够缓解纯文本训练固有的感官剥夺问题。然而，其有效性高度依赖于文本与生成图像之间的语义对齐、任务的可视觉化程度以及T2I模型的生成保真度。本研究为该范式建立了一个严谨的基准，证明了其作为在传统单模态场景中丰富语言理解能力的一条可行路径。