Recent advancements in Hierarchical Reasoning Models (HRM) have demonstrated strong capabilities in complex algorithmic and abstract reasoning tasks by mimicking multi-timescale cognitive processes. In this work, we extend this architecture to medical image captioning, introducing specific ImageHRM variants. Furthermore, we explore a radical simplification of this paradigm: the Tiny Recursive Model (TRM). Challenging the necessity of complex dual-loop biological hierarchies, TRM employs a single "tiny" network (7M parameters) that recurses deeply to achieve superior generalization. We introduce ImageTRM, which adapts this "Less is More" philosophy to vision-language tasks. Our experiments on ROCOv2 show that while the Triple-Loop FuseLIP ImageHRM achieves stateof- the-art results, the tiny ImageTRM with a Swin backbone surprisingly outperforms it, demonstrating that deep recursive reasoning with high-quality visual features can surpass larger, more complex architectures.