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This work introduces MetaEncoder, which fine-tunes a pre-trained Muse-Glimmer 30B decoder into an instruction-following decision-making encoder, and employs a bi-encoder architecture trained via unidirectional contrastive learning for request-candidate alignment.
TTE-Flash-2B is introduced, a reasoning-aware multimodal representation model that outperforms its explicit-CoT counterpart on the MMEB-v2 benchmark, while producing latent think tokens that are interpretable both textually and visually.
This work proposes MOEMB, which instead scales UME along the expert axis through mixture-of-experts (MoE), growing encoder capacity while preserving single-vector, non-autoregressive encoding, and conducts the first comprehensive study of adaptive computation for MoE-based embedding.
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