The Indra Representation Hypothesis
Jianglin Lu, Hailing Wang, Kuo Yang, Yitian Zhang, Simon Jenni, Yun Fu
Recent studies have uncovered an interesting phenomenon: unimodal foundation models tend to learn convergent representations, regardless of differences in architecture, training objectives, or data modalities. However, these representations are essentially internal abstractions of samples that characterize samples independently, leading to structural myopia and limited expressiveness.In this paper, we propose *The Indra Representation Hypothesis*, inspired by the philosophical metaphor of Indra’s Net. We argue that representations from unimodal foundation models are converging to implicitly reflect a shared relational structure underlying reality, akin to the relational ontology of Indra’s Net. We formalize this hypothesis using the $\mathcal{V}$-enriched Yoneda embedding from category theory, defining the Indra representation as a relational profile of each sample with respect to all others. This formulation is shown to be unique, complete, and structure-preserving under a given cost function. We instantiate the Indra representation using angular distance and evaluate it in cross-model and cross-modal scenarios involving vision, language, and audio. Extensive experiments demonstrate that Indra representations consistently enhance matching performance across modalities and architectures, providing a theoretically grounded and practical framework for post-training alignment in unimodal models.