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    Topic
    Hardware-Aligned and Natively Trainable Sparse Attention
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    The latest paper from DeepSeek introduces a new attention mechanism — NSA, a locally trainable sparse attention mechanism for ultra-fast long-context training and inference.
    Multi-Dimensional Insights: Benchmarking Real-World Personalization in Large Multimodal Models
    YiFan Zhang,Shanglin Lei,Runqi Qiao,Zhuoma GongQue,Xiaoshuai Song,Guanting Dong, Qiuna Tan, Zhe Wei, Peiqing Yang, Ye Tian, Yadong Xue, Xiaofei Wang,
    CoRR （2024）
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    Are Your LLMs Capable of Stable Reasoning
    Junnan Liu,Hongwei Liu, Linchen Xiao, Ziyi Wang,Kuikun Liu,Songyang Gao,Wenwei Zhang,Songyang Zhang,Kai Chen
    Computing Research Repository （2024）
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    Mixture of Block Attention for Long-Context LLMs
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    Kimi proposed a new attention mechanism, MoBA, which combines the principles of MoE and improves the efficiency of LLMs in long-text scenarios without sacrificing performance.
    No More Adam: Learning Rate Scaling at Initialization is All You Need
    Minghao Xu, Lichuan Xiang,Xu Cai,Hongkai Wen
    CoRR （2024）
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    Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
    Benjamin Warner, Antoine Chaffin,Benjamin Clavié,Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Gallagher, Raja Biswas,Faisal Ladhak, Tom Aarsen,Nathan Cooper,Griffin Adams,
    CoRR （2024）
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    TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
    Frank F. Xu, Yufan Song, Boxuan Li, Yuxuan Tang, Kritanjali Jain, Mengxue Bao, Zora Z. Wang,Xuhui Zhou, Zhitong Guo, Murong Cao, Mingyang Yang, Hao Yang Lu,
    Computing Research Repository （2024）
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