If there is a modality-agnostic platonic representation, training on both image and language data should improve the best model in either modality

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• Language models would achieve some notion of grounding in the visual domain even in the absence of cross-modal training data, because they share a common modality-agnostic representationhypothesis0.816
  Implication of PRH for language model visual grounding
• Platonic Representationconcept0.789
  The hypothesized converged representation that all sufficiently large AI models are approaching — a statistical model of underlying reality
• Scaling model size, as well as data and task diversity, drives representational convergence toward the platonic representationhypothesis0.787
  Core mechanism hypothesis connecting PRH to the empirical trend of scaling in AI
• Imagine reading a textbook with no figures or tables. Our ability to knowledge acquisition is greatly strengthened by jointly modeling diverse data modalities, such as vision, language, and audio.quote0.787
  Load-bearing motivation for multimodal approach; frames the cognitive advantage of joint modalities.
• How do representations differ or converge between architectures, tasks, and modalities?question0.781
  Broader research question MAS is positioned to address, citing multiple recent works.
• Foundation models trained on different data converge on similar latent representations, suggesting a Platonic form.claim0.779
• Platonic Space / Convergent Representationsconcept0.772
  Theoretical thread suggesting discoverable geometric priors shared across systems; circular number representations support this hypothesis.
• Integrating the tactile modality into the self-prior model may improve learning efficiency for mirror self-recognitionhypothesis0.771
  Forward-looking prediction based on Chinn et al.'s finding that tactile experience promotes earlier MSR in infants