Unified Vision–Language Modeling via Concept Space Alignment

We introduce vSONAR, a vision–language embedding space extended from the text-only embedding space SONAR, which supports 200 text languages and 37 speech languages. To construct vSONAR, we propose a post-hoc alignment pipeline that maps the representations of an existing vision encoder into the SONAR space. We thoroughly evaluate vSONAR and show that its embeddings achieve competitive performance on text-to-video retrieval. Equipped with the SONAR text decoder, vSONAR further surpasses state-of-the-art vision–language models on video captioning tasks, including DREAM-1K (BLEU 24.3 vs. 19.6) and VATEX (BLEU 45.0 vs. 41.5). Leveraging vSONAR, we first demonstrate that the Large Concept Model (LCM) operating in SONAR and trained with English text only, can perform both single- and multi-visual concept understanding in a zero-shot manner. Finally, we introduce vLCM, which extends the LCM with vision–language instruction tuning. vLCM encodes vision and language inputs into an unified sequence of latent embeddings via vSONARand SONAR, and it is trained with the same latent diffusion objective for next-embedding prediction as in LCM's text-only pre-training. Experiments on a large-scale multilingual and -modal instruction–tuning data mixture highlight the potential of vLCM: vLCM matches state-of-the-art vision-language models on tasks covering image/video captioning and question answering, while significantly outperforming them across 61 rich- to low-resource languages out of all 62 tested languages.