From Modalities to Propositions: A Language-Centric Framework for Multimodal Intelligence

Nadine Chang; Maying Shen; Shizhe Diao; Jialiang Wang; Jingde Chen; Thomas Breuel; Pavlo Molchanov; Rafid Mahmood; Jose M. Alvarez

arXiv · 2607.16560

From Modalities to Propositions: A Language-Centric Framework for Multimodal Intelligence

Nadine Chang·Maying Shen·Shizhe Diao·Jialiang Wang·Jingde Chen·Thomas Breuel·Pavlo Molchanov·Rafid Mahmood·Jose M. Alvarez

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Abstract

We propose a language representation for multimodal data in which any observation, whether image, video, or text, is expressed as a bag of atomic propositions, simple statements about the entities, actions, and relations in a scene. A global semantic codebook unifies these into a shared vocabulary of canonical atomic propositions, placing every modality and observation into one interpretable space that spans fine grained facts to high level concepts and composes into richer ones. This brings interpretability with reasoning, cross-modal understanding and retrieval, and compositionality that enables complex multimodal understanding, rich data curation and complex structured retrieval. We demonstrate the framework on autonomous driving and open-world data.

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Nadine Chang, Maying Shen, Shizhe Diao, Jialiang Wang, Jingde Chen, Thomas Breuel, Pavlo Molchanov, Rafid Mahmood, Jose M. Alvarez. 2026-07-18. From Modalities to Propositions: A Language-Centric Framework for Multimodal Intelligence. https://arxiv.org/abs/2607.16560

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