Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale

2026-04-20

AI Summary

This study investigates whether cross-modal neural networks can still learn unified semantic representations of text and images under large-scale, real-world conditions. By introducing a mutual nearest neighbor metric, scaling to million-sample datasets, and constructing a many-to-many image–text matching setup, the authors evaluate multiple state-of-the-art multimodal models. Their findings reveal that current cross-modal alignment heavily relies on small-scale, constrained evaluation protocols and degrades substantially under more realistic settings. The residual alignment observed reflects only coarse-grained semantic overlap, lacking fine-grained structural consistency. These results challenge the prevailing assumption that stronger language models inherently yield better visual alignment and expose fundamental limitations in contemporary cross-modal representation learning.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The Platonic Representation Hypothesis suggests that neural networks trained on different modalities (e.g., text and images) align and eventually converge toward the same representation of reality. If true, this has significant implications for whether modality choice matters at all. We show that the experimental evidence for this hypothesis is fragile and depends critically on the evaluation regime. Alignment is measured using mutual nearest neighbors on small datasets ($\approx$1K samples) and degrades substantially as the dataset is scaled to millions of samples. The alignment that remains between model representations reflects coarse semantic overlap rather than consistent fine-grained structure. Moreover, the evaluations in Huh et al. are done in a one-to-one image-caption setting, a constraint that breaks down in realistic many-to-many settings and further reduces alignment. We also find that the reported trend of stronger language models increasingly aligning with vision does not appear to hold for newer models. Overall, our findings suggest that the current evidence for cross-modal representational convergence is considerably weaker than subsequent works have taken it to be. Models trained on different modalities may learn equally rich representations of the world, just not the same one.

Problem

• cross-modal representation
• representational convergence
• Platonic Representation Hypothesis
• modality alignment
• neural network representations

Innovation

• cross-modal alignment
• representational convergence
• Platonic Representation Hypothesis
• large-scale evaluation
• multimodal learning