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Adaptive Transformers: Balancing Speed and Accuracy with Dynamic Computation

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NOSA: Boosting LLM Decoding Throughput with Smart KV Cache Offloading

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vAttention: A New Approach to Sparse Attention with Guaranteed Accuracy

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VideoNSA: A Smart Approach to Scaling Video Understanding in AI Models

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FlashOmni: A Universal Engine for Accelerating Diffusion Transformers

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Optimizing Image Generation: HilbertA’s Efficient Sparse Attention for Diffusion Models

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AQUA: Enhancing LLM Efficiency Through Dynamic Attention Optimization

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Generating Minutes-Long Videos: How Mixture of Contexts Overcomes Memory Challenges

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Unlocking NPU Potential for Mobile LLMs with Dynamic Sparse Attention

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Dense vs. Sparse Attention: Navigating Scalability in Graph Transformers

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Hydra: A Unified Approach to Long-Context Language Models

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Erwin NSA: Enhancing Transformer Efficiency for Large Point Cloud Datasets

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BLADE: A Framework for Faster, Smarter AI Video Generation

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Navigating the Efficiency Frontier: A Deep Dive into Next-Generation LLM Architectures

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LessIsMore: A Training-Free Approach to Efficient AI Reasoning

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Keeping Large Language Models Current: A New Framework for Real-Time Knowledge Integration

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SPARTA: Enhancing Spiking Neural Networks with Precise Spike Timing for Efficient Attention

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Dynamic Mask Attention: A New Paradigm for Efficient Long-Context LLMs

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Optimizing Large Language Models: A Look at Efficient Attention Strategies

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DyCAST-Net: A New Approach to Understanding Cause and Effect in Complex Time Series Data

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