它接收视频或图像输入，将其压缩成一串紧凑的视觉嵌入向量。这里研究团队选用的是冻结参数的V-JEPA 2 ViT-L模型。这个模型本身就在自监督视觉任务上表现优异，能把复杂的视频画面浓缩成高密度的信息流。

NEPA 正是将这种 GPT 式的哲学引入视觉领域的一次大胆尝试。作者认为，与其学习如何重建图像，不如学习如何“推演”图像。如果模型能够根据已有的视觉片段（Patches），准确预测出下一个片段的特征表示（Embedding），那么它一定已经理解了图像的语义结构和物体间的空间关系。

Google's real-time translator looks ahead and anticipates what is being said, explains Niklas Blum, Director Product ...

This study presents a valuable advance in reconstructing naturalistic speech from intracranial ECoG data using a dual-pathway model. The evidence supporting the claims of the authors is solid, ...

AI2 has unveiled Bolmo, a byte-level model created by retrofitting its OLMo 3 model with <1% of the compute budget.

Ai2 releases Bolmo, a new byte-level language model the company hopes would encourage more enterprises to use byte level ...

Chinese AI startup Zhipu AI aka Z.ai has released its GLM-4.6V series, a new generation of open-source vision-language models (VLMs) optimized for multimodal reasoning, frontend automation, and ...

Add a description, image, and links to the encoder-decoder-architecture topic page so that developers can more easily learn about it.

直接给结论，不用。 甚至可以说，都要2026年了，如果你现在还抱着十年前的教材，非要先啃明白RNN，再搞懂LSTM里那个该死的遗忘门，最后才敢翻开Transformer的第一页，那你纯粹是在浪费生命。

We break down the Encoder architecture in Transformers, layer by layer! If you've ever wondered how models like BERT and GPT process text, this is your ultimate guide. We look at the entire design of ...