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 representations, limiting their effectiveness as multimodal embedding backbones. To address this, we propose CoCoA, a Content reconstruction pre-training paradigm based on Collaborative Attention for multimodal embedding optimization. Specifically, we restructure the attention flow and introduce an EOS-based reconstruction task, encouraging the model to reconstruct input from the corresponding <EOS> embeddings. This drives the multimodal model to compress the semantic information of the input into the <EOS> token, laying the foundations for subsequent contrastive learning. Extensive experiments on MMEB-V1 demonstrate that CoCoA built upon Qwen2-VL and Qwen2.5-VL significantly improves embedding quality. Results validate that content reconstruction serves as an effective strategy to maximize the value of existing data, enabling multimodal embedding models generate compact and informative representations, raising their performance ceiling.

翻译：多模态嵌入模型根植于多模态大语言模型（MLLMs），在检索、分类等多项任务中取得了显著性能提升。然而，现有方法大多依赖大规模对比学习，对MLLMs的架构设计与训练范式如何影响嵌入质量的探索仍显不足。尽管MLLMs的因果注意力与下一标记预测范式在生成任务中表现有效，但其并未显式促进全局紧致表征的形成，从而限制了其作为多模态嵌入骨干网络的能力。为此，我们提出CoCoA——一种基于协作注意力进行内容重建预训练范式的多模态嵌入优化方法。具体而言，我们重构注意力流动机制，引入基于终元标记（EOS）的重建任务，促使模型从对应的<EOS>嵌入重建输入内容。此举驱动多模态模型将输入语义信息压缩至<EOS>标记中，为后续对比学习奠定基础。在MMEB-V1基准上的大量实验表明，基于Qwen2-VL与Qwen2.5-VL构建的CoCoA显著提升了嵌入质量。结果验证了内容重建作为有效策略，可最大化现有数据价值，使多模态嵌入模型生成紧致且富含信息的表征，从而突破其性能瓶颈。