# Vela: Scalable Embeddings with Voice Large Language Models for Multimodal Retrieval

arXiv: https://arxiv.org/abs/2506.14445 · HTML: https://mlsanity.com/paper/2506.14445/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Document understanding and multimodal RAG, one of 16 directions in Multimodal LLMs / vision-language models.
- First posted on arXiv 17 Jun 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Document understanding and multimodal RAG](https://mlsanity.com/mllm/document-understanding-and-multimodal-rag/)

## Built on by (most cited first, 5 of 5)

- [DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment](https://mlsanity.com/paper/2507.02768/) (2025)
- [Generative Reasoning Recommendation via LLMs](https://mlsanity.com/paper/2510.20815/) (2025)
- [DocRetriever: A Plug-and-Play Framework for Multimodal Document Retrieval with Comprehensive Benchmark](https://mlsanity.com/paper/2605.30027/) (2026)
- [INSPIRE: A Benchmark for Instruction-Aware Speech Retrieval](https://mlsanity.com/paper/2608.16203/) (2026)
- [Doc-REFRAG: Rethinking Multimodal Document Retrieval-Augmented Generation](https://mlsanity.com/paper/2608.30163/) (2026)

## Abstract (arXiv)

Multimodal large language models (MLLMs) have seen substantial progress in recent years. However, their ability to represent multimodal information in the acoustic domain remains underexplored. In this work, we introduce Vela, a novel framework designed to adapt MLLMs for the generation of universal multimodal embeddings. By leveraging MLLMs with specially crafted prompts and selected in-context learning examples, Vela effectively bridges the modality gap across various modalities. We then propose a single-modality training approach, where the model is trained exclusively on text pairs. Our experiments show that Vela outperforms traditional CLAP models in standard text-audio retrieval tasks. Furthermore, we introduce new benchmarks that expose CLAP models' limitations in handling long texts and complex retrieval tasks. In contrast, Vela, by harnessing the capabilities of MLLMs, demonstrates robust performance in these scenarios. Our code will soon be available.