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Atlas: Advancing Long-Context NLP Through Enhanced Memory
Source : https://arxiv.org/abs/2505.23735
Examines Google Research's "Atlas" paper, which addresses the limitations of current language models in handling very long contexts. The paper introduces innovations like the Omega rule for contextual memory updates, higher-order kernels to boost memory capacity, and the Muon optimizer for enhanced memory management.
It proposes DEEPTRANSFORMERS as a generalization of existing Transformer architectures by adding deep memory. Atlas shows promise in tasks requiring extensive recall and ultra-long context reasoning, outperforming some baselines and highlighting the importance of explicit, learnable memory systems for future NLP progress.
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By Benjamin Alloul 🗪 🅽🅾🆃🅴🅱🅾🅾🅺🅻🅼Source : https://arxiv.org/abs/2505.23735
Examines Google Research's "Atlas" paper, which addresses the limitations of current language models in handling very long contexts. The paper introduces innovations like the Omega rule for contextual memory updates, higher-order kernels to boost memory capacity, and the Muon optimizer for enhanced memory management.
It proposes DEEPTRANSFORMERS as a generalization of existing Transformer architectures by adding deep memory. Atlas shows promise in tasks requiring extensive recall and ultra-long context reasoning, outperforming some baselines and highlighting the importance of explicit, learnable memory systems for future NLP progress.