CEO Wenfeng Liang personally co-authors groundbreaking research that won ACL 2025 Best Paper. DeepSeek has released a research paper introducing Native Sparse Attention (NSA). This mechanism fundamentally improves how Transformer models process long sequences.
What Makes NSA Different?
The research paper published on arXiv reveals a sophisticated approach to the attention bottleneck.
Traditional Transformer attention mechanisms face a critical bottleneck. As sequence length increases, computational costs grow quadratically. This makes processing long documents, extensive codebases, or multi-turn conversations extremely expensive.
NSA solves this through a three-pronged approach. First, it compresses tokens into coarse-grained representations for global context. Second, it selectively retains fine-grained tokens for important details. Third, it uses sliding windows for local contextual information.
The results are remarkable. NSA achieves up to 11.6x faster decoding and 9x faster forward propagation compared to full attention models. Despite being a sparse mechanism, it actually outperforms traditional full attention on benchmarks.
Technical Innovation
The key innovation lies in NSA’s “natively trainable” design. Previous sparse attention methods applied sparsity only during inference, forcing models to deviate from their training behavior. NSA learns sparse patterns from the start, ensuring consistency between training and deployment.
The architecture is also hardware-aligned. DeepSeek’s team optimized NSA for modern GPU architectures, ensuring theoretical efficiency gains translate into real-world performance improvements.
Benchmark Performance
DeepSeek evaluated NSA on a 27B-parameter Transformer backbone with 260B training tokens. The model excelled across multiple benchmarks including MMLU, BBH, GSM8K, and MATH.
Long-context capabilities proved particularly impressive. NSA achieved perfect retrieval accuracy in 64k-context needle-in-a-haystack tests. On LongBench, it outperformed all baselines including full attention models.
Chain-of-thought reasoning also improved. When combined with knowledge distillation from DeepSeek-R1, NSA demonstrated better mathematical reasoning abilities on the challenging AIME 2024 benchmark.
CEO on the Author List
Wenfeng Liang’s presence on the author list signals the strategic importance of this research. The DeepSeek founder, who was recently named among Nature’s “top 10 people who shaped science in 2025,” continues to stay deeply involved in technical development.
This follows a pattern. Liang co-authored the DeepSeek-R1 paper that made the cover of Nature in September 2025. His hands-on approach to research distinguishes DeepSeek from competitors where executives rarely participate in technical papers.
Implications for AI Development
NSA addresses one of the most pressing challenges in AI development. Long-context modeling is essential for applications like legal document analysis, code generation across entire repositories, and multi-turn agent systems.
If you’re new to DeepSeek’s ecosystem, our comprehensive guide on what DeepSeek is and how to use it provides a solid foundation before diving into these advanced architectural innovations.
By reducing computational requirements while maintaining or improving performance, NSA makes these applications more practical. Organizations can now deploy powerful AI models without massive infrastructure investments.
The open research approach also matters. DeepSeek published full technical details, enabling the broader AI community to build upon their work. GitHub implementations already exist, making NSA accessible to developers worldwide.
What Comes Next
NSA represents DeepSeek’s latest contribution to Transformer architecture improvement. Combined with their earlier innovations like Multi-Head Latent Attention (MLA) and DeepSeekMoE, the company has established itself as a leader in efficient AI architecture design.
Industry observers expect NSA to influence the next generation of language models. As context windows continue expanding, sparse attention mechanisms will become increasingly critical.
DeepSeek has demonstrated that fundamental improvements to Transformer architecture remain possible. With Wenfeng Liang personally driving research, the company shows no signs of slowing down.
