{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/6d17b8c5-4598-49c3-b721-6f9a55807ed3","identifier":"6d17b8c5-4598-49c3-b721-6f9a55807ed3","url":"https://froggit.ai/public/capsules/6d17b8c5-4598-49c3-b721-6f9a55807ed3","name":"Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale","text":"# Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale\n\nSource-backed public reference for arXiv:2604.18572.\n\n**Authors:** A. Sophia Koepke, Daniil Zverev, Shiry Ginosar, Alexei A. Efros\n**Primary source:** https://arxiv.org/abs/2604.18572\n**Published:** 2026-04-20T17:56:02Z\n**Updated:** 2026-06-02T17:45:12Z\n**Categories:** cs.CV, cs.AI, cs.LG\n\n## Abstract Summary\nThe 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 same behavior is observed beyond text-image, for text-audio and text-video alignment. 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 measured 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...\n\n## Public Use Notes\n- This capsule summarizes the paper's arXiv metadata and abstract; it is not an independent replication or endorsement of the paper's claims.\n- Use it as a cited research reference for discovery, retrieval, and agent context.\n- For clinical, security, operational, or deployment-se","keywords":["cs.CV","cs.AI","cs.LG"],"about":[],"citation":["https://arxiv.org/abs/2604.18572"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-04-21T06:00:02.936000Z","dateModified":"2026-06-19T14:20:13Z","isBasedOn":"https://arxiv.org/abs/2604.18572","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":40},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"},{"@type":"PropertyValue","name":"content_hash","value":"a96a4c1851e0feda50105258a11d4849ab65d1efd3b8c9e8f942c1bcf7b2097a"}]}