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Life sciences · Preprint
arXiv · September 8, 2026
Posted before peer review. The findings may change or fail to hold.
MoEMB is an unrefereed preprint describing a mixture-of-experts scaling approach for universal multimodal embeddings that reports superior benchmark performance compared to prior methods (TTE) with fewer active parameters. The work is a computational architecture study without peer review, clinical validation, or real-world deployment outcomes, and therefore carries no evidentiary weight for clinical practice or policy.
Preprint. Intervention: MoEMB: mixture-of-experts scaling approach for universal multimodal embedding encoders with adaptive computation strategies.. Compared with: Think-Then-Embed (TTE)-based methods and prior scaling approaches..
MoEMB with 3B active parameters surpasses TTE-based methods with >4x active parameters on MMEB-V2 and MRMR benchmarks MoEMB achieves state-of-the-art results using significantly less compute than comparator methods Adaptive computation strategies for MoE-based embedding further improve efficiency
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This is an unrefereed arXiv preprint presenting a novel machine learning architecture (MoEMB) with benchmark comparisons but no peer review, clinical validation, or human efficacy data.
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Universal multimodal embedding (UME) increasingly demands encoder's capacity for handling a broad range of tasks and modalities with increased complexity. Prior scaling methods either increase the representation size, retrieval effort, or scales the encoder into a heavy multimodal LLM. Recent works, such as Think-Then-Embed (TTE), explore scaling via reasoning tokens. However, embedding models are hard to scale up: increasing parameters directly tradeoffs for the large training batch size that contrastive learning needs, and retrieval has to be served under tight latency. Moreover, UME tasks are diverse in complexity, where scaling up embedders can bring significant redundant computation. In this work, we propose MOEMB, which instead scales UME along the expert axis through mixture-of-experts (MoE), growing encoder capacity while preserving single-vector, non-autoregressive encoding. Through a systematic study of the design space and training recipes for MoE-based UME, MoEMB sets a new state of the art on both MMEB-V2 and MRMR among models trained on public MMEB-family data: with only 3B active parameters, MoEMB surpasses TTE-based methods with >4x active parameters, using significantly less computes. To further improve the scalability and efficiency, we conduct the first comprehensive study of adaptive computation for MoE-based embedding, spanning diverse strategies across training-based and inference-only methods. Together, these results support expert scaling as an effective and efficient direction for UME, with adaptive computation further improving efficiency for MLLM-based embedding models towards large-scale retrieval and recommendation systems.
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