MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning

• URL: http://arxiv.org/abs/2607.16789v1
• Date: Sat, 18 Jul 2026 11:53:22 GMT
• Title: MultiLoReFT: Decoupling Shared and Modality-Specific Subspaces in Multimodal Learning via Low-Rank Representation Fine-Tuning
• Abstract summary: MultiLoReFT is an efficient and scalable low-rank representation framework for multimodal learning.<n>It learns interpretable projection subspaces that decouple shared and modality-specific information.<n>It produces representations that support multimodal prediction across simulated and real-world benchmarks.
• Score: 13.327494159383356
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-world perception and decision making are inherently multimodal, integrating complementary signals across modalities. However, training multimodal models faces two main obstacles. First, collecting large-scale, well-aligned paired multimodal datasets is often impractical, making end-to-end multimodal training difficult. Second, existing multimodal representations frequently entangle information shared across modalities with modality-specific information, hindering interpretability and control. We introduce MultiLoReFT, an efficient and scalable low-rank representation fine-tuning framework for multimodal learning with pretrained unimodal models. MultiLoReFT extends low-rank adaptation to the multimodal setting and learns interpretable projection subspaces that decouple shared and modality-specific information. Across simulated and real-world benchmarks, it produces representations that support multimodal prediction while explicitly revealing how shared and modality-specific information is distributed across modalities.

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