Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. Despite the strong performance across both general and complex tasks, this paradigm suffers from two core limitations: (i) autoregressive CoT reasoning incurs high computational cost, making it impractical for low-latency retrieval; and (ii) embedding performance is heavily coupled with CoT annotation quality, making large-scale training unreliable. These raise fundamental questions: Is textual CoT the optimal form of reasoning for embedding, and can effective embedding reasoning be accomplished in latent space? To this end, we propose LaME (Latent Reasoning Multimodal Embedding), which formulates embedding-oriented latent reasoning as a weakly supervised information bottleneck. LaME employs K learnable reason tokens as a fixed-capacity bottleneck, completing all reasoning within a single forward pass. The two weak supervision signals structurally decouple contrastive from autoregressive objectives and eliminate dependence on CoT annotations, while a two-stage training pipeline ensures stable convergence. Experiments on MMEB-v2 and MRMR show that LaME achieves competitive performance, surpassing some explicit CoT-based models, while delivering 60x faster inference than explicit CoT methods and 2x faster than latent baselines with throughput comparable to discriminative embedding models. Code will be released.

翻译：基于推理驱动的通用多模态嵌入通过将链式思维（Chain-of-Thought，CoT）推理引入嵌入流程取得了快速进展。尽管该范式在通用任务和复杂任务上均表现出色，但其存在两个核心局限：（i）自回归CoT推理带来了高计算成本，难以应用于低延迟检索任务；（ii）嵌入性能与CoT标注质量高度耦合，导致大规模训练不可靠。这引发了根本性问题：文本形式的CoT是否是嵌入推理的最优形式？有效的嵌入推理能否在潜在空间中完成？为此，我们提出LaME（潜在推理多模态嵌入），将面向嵌入的潜在推理形式化为弱监督信息瓶颈。LaME采用K个可学习推理令牌作为固定容量瓶颈，在单次前向传播中完成所有推理。两个弱监督信号从结构上解耦了对比目标和自回归目标，消除了对CoT标注的依赖；同时，两阶段训练流程确保了稳定的收敛性。在MMEB-v2和MRMR上的实验表明，LaME取得了具有竞争力的性能，超越了部分基于显式CoT的模型，同时推理速度比显式CoT方法快60倍，比潜在基线方法快2倍，吞吐量可与判别式嵌入模型媲美。代码将开源。