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This paper proposes representing multimodal data (image, video, text) as bags of atomic propositions (e.g., 'person holding cup'), unified via a global semantic codebook, enabling interpretable, compositional, and cross-modal understanding. The framework is demonstrated on autonomous driving and open-world data.
The paper proposes a hybrid pre-training objective combining JEPA latent-space prediction with MLM reconstruction for language models, showing improved embedding uniformity and semantic-lexical balance.
This paper argues that designing advanced language representations to shape cognitive schemas is a key frontier for expanding LLM intelligence without scaling parameters. It provides formalizations and empirical evidence showing that different linguistic structures significantly impact model performance and internal feature activations.