Update README.md

Author: DefTruth

README.md

@@ -80,8 +80,6 @@ python3 download_pdfs.py # The code is generated by Doubao AI
 |2024.12|🔥🔥🔥[**DeepSeek-V3**] DeepSeek-V3 Technical Report(@deepseek-ai) | [[pdf]](https://github.com/deepseek-ai/DeepSeek-V3/blob/main/DeepSeek_V3.pdf) | [[DeepSeek-V3]](https://github.com/deepseek-ai/DeepSeek-V3) ![](https://img.shields.io/github/stars/deepseek-ai/DeepSeek-V3.svg?style=social) | ⭐️⭐️ |
 |2025.01|🔥🔥🔥 [**MiniMax-Text-01**] MiniMax-01: Scaling Foundation Models with Lightning Attention | [[report]](https://filecdn.minimax.chat/_Arxiv_MiniMax_01_Report.pdf) | [[MiniMax-01]](https://github.com/MiniMax-AI/MiniMax-01) ![](https://img.shields.io/github/stars/MiniMax-AI/MiniMax-01.svg?style=social) | ⭐️⭐️ |
 |2025.01|🔥🔥🔥[**DeepSeek-R1**] DeepSeek-R1 Technical Report(@deepseek-ai) | [[pdf]](https://arxiv.org/pdf/2501.12948v1) | [[DeepSeek-R1]](https://github.com/deepseek-ai/DeepSeek-R1) ![](https://img.shields.io/github/stars/deepseek-ai/DeepSeek-R1.svg?style=social) | ⭐️⭐️ |
-|2025.02| 🔥🔥🔥[**microsoft**] SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs | [pdf](https://arxiv.org/abs/2410.13276) | [SeerAttention](https://github.com/microsoft/SeerAttention) ![](https://img.shields.io/github/stars/microsoft/SeerAttention.svg?style=social) | ⭐️⭐️⭐️ |
-|2025.03| [**OpenMachine.ai**] Slim attention: cut your context memory in half without loss of accuracy, K-cache is all you need for MHA | [pdf](https://arxiv.org/pdf/2503.05840) | [OpenMchine](https://github.com/OpenMachine-ai/transformer-tricks) ![](https://img.shields.io/github/stars/OpenMachine-ai/transformer-tricks.svg?style=social) | ⭐️⭐️⭐️ |
 
 ### 📖DeepSeek/Multi-head Latent Attention(MLA) ([©️back👆🏻](#paperlist))
 <div id="mla"></div>
@@ -281,6 +279,8 @@ python3 download_pdfs.py # The code is generated by Doubao AI
 |2025.04|🔥🔥[**MMInference**] MMInference: Accelerating Pre-filling for Long-Context Visual Language Models via Modality-Aware Permutation Sparse Attention(@microsoft) | [[pdf]](https://arxiv.org/pdf/2504.16083)|[[MInference]](https://github.com/microsoft/MInference/) ![](https://img.shields.io/github/stars/microsoft/MInference) | ⭐️⭐️ |
 |2025.04|🔥🔥[**Sparse Frontier**] The Sparse Frontier: Sparse Attention Trade-offs in Transformer LLMs (@Cohere) | [[pdf]](https://arxiv.org/pdf/2504.17768)|[[SparseFrontier]](https://github.com/PiotrNawrot/sparse-frontier) ![](https://img.shields.io/github/stars/PiotrNawrot/sparse-frontier) | ⭐️⭐️ |
 |2024.12|🔥🔥[**Flex Attention**] FLEX ATTENTION: A PROGRAMMING MODEL FOR GENERATING OPTIMIZED ATTENTION KERNELS(@pytorch) | [[pdf]](https://arxiv.org/pdf/2412.05496)|[[attention-gym]](https://github.com/pytorch-labs/attention-gym) ![](https://img.shields.io/github/stars/pytorch-labs/attention-gym) | ⭐️⭐️ |
+|2025.02| 🔥🔥🔥[**SeerAttention**] SeerAttention: Learning Intrinsic Sparse Attention in Your LLMs(@microsoft) | [[pdf]](https://arxiv.org/abs/2410.13276) | [[SeerAttention]](https://github.com/microsoft/SeerAttention) ![](https://img.shields.io/github/stars/microsoft/SeerAttention.svg?style=social) | ⭐️⭐️⭐️ |
+|2025.03| [**Slim attention**] Slim attention: cut your context memory in half without loss of accuracy, K-cache is all you need for MHA(@OpenMachine.ai) | [[pdf]](https://arxiv.org/pdf/2503.05840) | [[OpenMchine]](https://github.com/OpenMachine-ai/transformer-tricks) ![](https://img.shields.io/github/stars/OpenMachine-ai/transformer-tricks.svg?style=social) | ⭐️⭐️⭐️ |
 
 
 ### 📖KV Cache Scheduling/Quantize/Dropping ([©️back👆🏻](#paperlist))