arXiv:2603.01471v2 Announce Type: replace-cross Abstract: Multimodal embedding models, rooted in multimodal large language models (MLLMs), have yielded significant performance improvements across diverse tasks such as retrieval and classification. However, most existing approaches rely heavily on large-scale contrastive learning, with limited exploration of how the architectural and training paradigms of MLLMs affect embedding quality. While effective for generation, the causal attention and next-token prediction paradigm of MLLMs does not explicitly encourage the formation of globally compact
This research is published as multimodal large language models (MLLMs) are becoming mainstream, and there is an increasing focus on improving their underlying efficiency and performance beyond current contrastive learning paradigms.
This research explores fundamental improvements to multimodal embedding quality by addressing architectural limitations of MLLMs, which could lead to more efficient and powerful AI systems for various applications.
The focus shifts from solely large-scale contrastive learning to optimizing MLLM architectures for embedding quality, potentially enabling new approaches to multimodal AI development.
- · AI researchers
- · Multimodal AI developers
- · Cloud AI providers
- · Companies using retrieval & classification
- · Developers relying solely on brute-force contrastive learning
- · Inefficient multimodal AI models
Improved multimodal embedding leads to more accurate and efficient AI systems for tasks like search and content understanding.
Enhanced multimodal capabilities could accelerate the development and deployment of advanced AI agents and intelligent systems.
More sophisticated multimodal AI could indirectly contribute to the compute supply chain by driving demand for specialized hardware to run these optimized models.
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Read at arXiv cs.LG