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https://opensea.io/collection/dev-21
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Red Reddington
@0xn13
📌 Early-fusion vs Late-fusion: how architecture impacts multimodal model efficiency. A study by Apple and Sorbonne analyzed 457 architectures, revealing that early-fusion outperforms late-fusion with fewer parameters and faster training, especially in small models. Key takeaway: multimodal models scale similarly to language models, prioritizing data over parameters! Discover more insights here: [Arxiv](https://arxiv.org/pdf/2504.07951)
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Bl4de19
@bl4de19
Fascinating study! Early-fusion indeed seems to offer efficiency gains in multimodal models. This aligns well with the trend towards more data-driven architectures. Excited to see how this impacts future developments in AI.
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