🤖🧠 Kimi Linear: The Future of Efficient Attention in Large Language Models
🗓️ 08 Nov 2025
📚 AI News & Trends
The rapid evolution of large language models (LLMs) has unlocked new capabilities in natural language understanding, reasoning, coding and multimodal tasks. However, as models grow more advanced, one major challenge persists: computational efficiency. Traditional full-attention architectures struggle to scale efficiently, especially when handling long context windows and real-time inference workloads. The increasing demand for agent-like ...
#KimiLinear #EfficientAttention #LargeLanguageModels #LLM #ComputationalEfficiency #AIInnovation
🗓️ 08 Nov 2025
📚 AI News & Trends
The rapid evolution of large language models (LLMs) has unlocked new capabilities in natural language understanding, reasoning, coding and multimodal tasks. However, as models grow more advanced, one major challenge persists: computational efficiency. Traditional full-attention architectures struggle to scale efficiently, especially when handling long context windows and real-time inference workloads. The increasing demand for agent-like ...
#KimiLinear #EfficientAttention #LargeLanguageModels #LLM #ComputationalEfficiency #AIInnovation
✨Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence
📝 Summary:
This work converts pretrained non-recurrent language models into depth-recurrent ones. Using a curriculum of recurrences improves performance on tasks like mathematics at a lower compute budget compared to standard post-training.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07384
• PDF: https://arxiv.org/pdf/2511.07384
• Github: https://github.com/mcleish7/retrofitting-recurrence
✨ Datasets citing this paper:
• https://huggingface.co/datasets/smcleish/retrofitting-llama-fineweb-edu-tokenized
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For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #DeepLearning #AIResearch #NeuralNetworks #ComputationalEfficiency
📝 Summary:
This work converts pretrained non-recurrent language models into depth-recurrent ones. Using a curriculum of recurrences improves performance on tasks like mathematics at a lower compute budget compared to standard post-training.
🔹 Publication Date: Published on Nov 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.07384
• PDF: https://arxiv.org/pdf/2511.07384
• Github: https://github.com/mcleish7/retrofitting-recurrence
✨ Datasets citing this paper:
• https://huggingface.co/datasets/smcleish/retrofitting-llama-fineweb-edu-tokenized
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#LLM #DeepLearning #AIResearch #NeuralNetworks #ComputationalEfficiency