The Wittgensteinian Representation Hypothesis: Is Language the Attractor of Multimodal Convergence?

Understanding why independently trained neural networks from different modalities converge toward shared representations, and where this convergence leads, remains an open question in representation learning. All existing evidence relies on symmetric similarity measures, which can detect convergence but are structurally blind to its direction. We introduce directional convergence analysis using cycle-kNN, an asymmetric alignment measure, applied across dozens of independently trained unimodal mo

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ethics.ai (10 May 2026), “The Wittgensteinian Representation Hypothesis: Is Language the Attractor of Multimodal Convergence?,” evidence record 4631, https://ethics.ai/record/4631 (originally published by arXiv).

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