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  "text": "LeCun's JEPA has evolved into a vision-language model, with 1.6B parameters rivaling the 72B Qwen-VL.\n\nInstead of predicting words directly, the proposed VL-JEPA learns to predict the core \"meaning\" of a text in an abstract space, ignoring surface-level wording variations.\n\nThis method outperforms standard token-based training with 50% fewer parameters. It beats models like CLIP & SigLIP2 on video classification/retrieval tasks and matches larger VLMs on VQA, while using a decoder only when needed to cut decoding ops by nearly 3x.\n\nVL-JEPA: Joint Embedding Predictive Architecture for Vision-language\n\nPaper: https://t.co/rGglBXvKex\n\nOur report: https://t.co/TXEHRquSBr",
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