CodeBind: Decoupled Representation Learning for Multimodal Alignment with Unified Compositional Codebook

Multimodal representation alignment is pivotal for large language models and robotics. Traditional methods are often hindered by cross-modal information discrepancies and data scarcity, leading to suboptimal alignment spaces that overlook modality-unique features. We propose CodeBind, a framework that optimizes multimodal representation spaces through a modality-shared-specific codebook design. By incrementally aligning target and bridging modalities, CodeBind bypasses the need for fully paired

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ethics.ai (18 May 2026), “CodeBind: Decoupled Representation Learning for Multimodal Alignment with Unified Compositional Codebook,” evidence record 4127, https://ethics.ai/record/4127 (originally published by arXiv).

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