Multimodal alignment is critical for bridging the semantic gap in information retrieval. However, traditional pairwise strategies introduce a geometric blind spot: while they align anchor modalities (e.g., text) with others, they lack constraints to enforce mutual consistency between peripheral modalities (e.g., video and audio). The TRIANGLE framework addresses this by minimizing the area of modality triplets on a hypersphere to enforce holistic alignment. In this reproducibility study, we verify the robustness of this geometric objective for retrieval tasks. We confirm that TRIANGLE outperforms pairwise baselines in zero-shot settings, achieving Recall@1 gains of up to +8.7 points, though benefits are domain-dependent. However, we fail to reproduce the reported learning-from-scratch results. Analysis using a synthetic toy dataset attributes this to instability when jointly optimizing geometric alignment with Data-Text Matching (DTM) loss. Furthermore, we find that cosine regularization primarily stabilizes text-to-video retrieval, and fine-tuning with domain supervision amplifies geometric benefits but reduces cross-dataset generalization. Our findings support the efficacy of geometric alignment while highlighting critical optimization sensitivities. Code available at https://github.com/ARIJIT00171/RE-TRIANGLE.

翻译：[translated abstract in Chinese] 多模态对齐对于弥合信息检索中的语义鸿沟至关重要。然而，传统的成对策略存在几何盲点：虽然它们将锚点模态（如文本）与其他模态对齐，但缺乏强制外围模态（如视频和音频）间相互一致性的约束。TRIANGLE框架通过最小化超球面上模态三元组的面积来实现整体对齐，从而解决了上述问题。在本可重复性研究中，我们验证了该几何目标对检索任务的鲁棒性。我们确认TRIANGLE在零样本设置下优于成对基线方法，Recall@1最高提升达8.7个百分点，但优势具有领域依赖性。然而，我们未能复现其从头训练的报告结果。通过合成玩具数据集的分析表明，这归因于联合优化几何对齐与数据-文本匹配（DTM）损失时的不稳定性。此外，我们发现余弦正则化主要稳定文本到视频检索，而基于领域监督的微调放大了几何优势却降低了跨数据集的泛化能力。我们的研究结果既支持了几何对齐的有效性，也揭示了关键的优化敏感性。代码开源地址：https://github.com/ARIJIT00171/RE-TRIANGLE。