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    <abstract>Modern information systems often involve different types of items, , 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 first- and 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 cross-modal 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/collections/Haon-Chen/e5-omni.</abstract>
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%0 Conference Proceedings
%T e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings
%A Chen, Haonan
%A Gao, Sicheng
%A Timofte, Radu
%A Sakai, Tetsuya
%A Dou, Zhicheng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chen-etal-2026-e5
%X Modern information systems often involve different types of items, , 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 first- and 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 cross-modal 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/collections/Haon-Chen/e5-omni.
%U https://aclanthology.org/2026.findings-acl.970/
%P 19430-19443

Markdown (Informal)

[e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings](https://aclanthology.org/2026.findings-acl.970/) (Chen et al., Findings 2026)

• e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings (Chen et al., Findings 2026)

ACL

• Haonan Chen, Sicheng Gao, Radu Timofte, Tetsuya Sakai, and Zhicheng Dou. 2026. e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings. In Findings of the Association for Computational Linguistics: ACL 2026, pages 19430–19443, San Diego, California, United States. Association for Computational Linguistics.