Escaping Plato's Cave: JAM for Aligning Independently Trained Vision and Language Models

• URL: http://arxiv.org/abs/2507.01201v4
• Date: Wed, 16 Jul 2025 21:17:46 GMT
• Title: Escaping Plato's Cave: JAM for Aligning Independently Trained Vision and Language Models
• Authors: Lauren Hyoseo Yoon, Yisong Yue, Been Kim,
• Abstract summary: We introduce a framework that trains modality-specific autoencoders on latent representations of single modality models.<n>By analogy, this framework serves as a method to escape Plato's Cave, enabling the emergence of shared structure from disjoint inputs.
• Score: 29.59537209390697
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Independently trained vision and language models inhabit disjoint representational spaces, shaped by their respective modalities, objectives, and architectures. Yet an emerging hypothesis - the Platonic Representation Hypothesis - suggests that such models may nonetheless converge toward a shared statistical model of reality. This compatibility, if it exists, raises a fundamental question: can we move beyond post-hoc statistical detection of alignment and explicitly optimize for it between such disjoint representations? We cast this Platonic alignment problem as a multi-objective optimization task - preserve each modality's native structure while aligning for mutual coherence. We introduce the Joint Autoencoder Modulator (JAM) framework that jointly trains modality-specific autoencoders on the latent representations of pre-trained single modality models, encouraging alignment through both reconstruction and cross-modal objectives. By analogy, this framework serves as a method to escape Plato's Cave, enabling the emergence of shared structure from disjoint inputs. We evaluate this framework across three critical design axes: (i) the alignment objective - comparing contrastive loss (Con), its hard-negative variant (NegCon), and our Spread loss, (ii) the layer depth at which alignment is most effective, and (iii) the impact of foundation model scale on representational convergence. Our findings show that our lightweight Pareto-efficient framework reliably induces alignment, even across frozen, independently trained representations, offering both theoretical insight and practical pathways for transforming generalist unimodal foundations into specialist multimodal models.

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