MLLMs have been successfully applied to multimodal embedding tasks, yet their generative reasoning capabilities remain underutilized. Directly incorporating chain-of-thought reasoning into embedding learning introduces two fundamental challenges. First, structural misalignment between instance-level reasoning and pairwise contrastive supervision may lead to shortcut behavior, where the model merely learns the superficial format of reasoning. Second, reasoning is not universally beneficial for embedding tasks. Enforcing reasoning for all inputs may introduce unnecessary computation and latency, and can even obscure salient semantic signals for simple cases. To address these issues, we propose MMEmb-R1, an adaptive reasoning-based multimodal embedding framework. We formulate reasoning as a latent variable and introduce pair-aware reasoning selection that employs counterfactual intervention to identify reasoning paths beneficial for query-target alignment. Furthermore, we adopt reinforcement learning to selectively invoke reasoning only when necessary. Experiments on the MMEB-V2 benchmark demonstrate that our model achieves a score of 71.2 with only 4B parameters, establishing a new state-of-the-art while significantly reducing reasoning overhead and inference latency.

翻译：多模态大语言模型已成功应用于多模态嵌入任务，但其生成式推理能力仍未得到充分利用。直接将思维链推理融入嵌入学习会引发两个根本性挑战：首先，实例级推理与成对对比监督之间的结构性错位可能导致捷径行为，即模型仅学习推理的表面形式；其次，推理并非对所有嵌入任务都有益，对所有输入强制推理不仅会引入不必要的计算和延迟，甚至可能遮蔽简单场景中显著的语义信号。针对这些问题，我们提出MMEmb-R1——一种基于自适应推理的多模态嵌入框架。我们将推理视为潜变量，并引入成对感知推理选择机制，通过反事实干预识别有利于查询-目标对齐的推理路径。此外，我们采用强化学习仅在必要时选择性调用推理。在MMEB-V2基准上的实验表明，我们的模型在仅4B参数下达到71.2分，在显著降低推理开销和推理延迟的同时，确立了新的最优性能。