e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings

Modern information systems often involve different types of items, e.g., a text query, an image, a video clip, or an audio segment. This motivates omni-modal embedding models that map heterogeneous modalities into a shared space for direct comparison. However, most recent omni-modal embeddings still rely heavily on implicit alignment inherited from pretrained vision-language model (VLM) backbones. In practice, this causes three common issues: (i) similarity logits have modality-dependent sharpness, so scores are not on a consistent scale; (ii) in-batch negatives become less effective over time because mixed-modality batches create an imbalanced hardness distribution; as a result, many negatives quickly become trivial and contribute little gradient; and (iii) embeddings across modalities show mismatched firstand second-order statistics, which makes rankings less stable. To tackle these problems, we propose e5-omni, a lightweight explicit alignment recipe that adapts off-the-shelf VLMs into robust omni-modal embedding models. e5-omni combines three simple components: (1) modality-aware temperature calibration to align similarity scales, (2) a controllable negative curriculum with debiasing to focus on confusing negatives while reducing the impact of false negatives, and (3) batch whitening with covariance regularization to better match crossmodal geometry in the shared embedding space. Experiments on MMEB-V2 and AudioCaps show consistent gains over strong bi-modal and omni-modal baselines, and the same recipe also transfers well to other VLM backbones. We release our model checkpoint at https:// huggingface.co/Haon-Chen/e5-omni-7B . Recent work has started to move from bi-modal to omni-modal embedding models, e.g., by adopting a VLM-centered bi-encoder architecture (Ma et al., 2025; Xu et al., 2025b; Xiao et al., 2025) . However, these methods still largely rely on implicit modality alignment inherited from the generative pretraining of VLMs. Contrastive fine-tuning provides only a limited corrective signal, and it may not fully calibrate modality-specific similarity