Update README.md

README.md

@@ -15,12 +15,12 @@ KernelGen-LM-32B is a state-of-the-art domain-adaptive large language model spec
 The Ascend KernelGen Technical Report is published at https://arxiv.org/abs/2601.07160.
 
 **Other artifacts:**
-* The **Ascend KernelGen Technical Report** is published at https://arxiv.org/abs/2601.07160.
+* The **AscendKernelGen Technical Report** is published at https://arxiv.org/abs/2601.07160.
 * The **NPUKernelBench** evaluation framework is published at https://git.openi.org.cn/PCL-Benchmark/NPUKernelBench.
 
 ## Introduction
 
-Our framework, **Ascend KernelGen (AKGen)**, bridges the gap between general-purpose code generation and hardware-specific programming through a closed-loop system of data construction, training, and evaluation. Key innovations include:
+Our framework, **AscendKernelGen (AKGen)**, bridges the gap between general-purpose code generation and hardware-specific programming through a closed-loop system of data construction, training, and evaluation. Key innovations include:
 
 * **Ascend-CoT Dataset:** A high-quality, domain-specific dataset incorporating **Chain-of-Thought (CoT)** reasoning. It combines documentation-based reasoning, code-centric reasoning derived from real-world kernel implementations, and general reasoning chains to capture the structured logic required for low-level NPU programming.
 * **Domain-Adaptive Post-Training:** A two-stage optimization process that yields **KernelGen-LM**. We first employ **Supervised Fine-Tuning (SFT)** with error-derived supervision (correcting API misuse and numerical errors). This is followed by **Reinforcement Learning (RL)** using Direct Preference Optimization (DPO), driven by execution-based correctness and performance signals.
@@ -29,7 +29,7 @@ Our framework, **Ascend KernelGen (AKGen)**, bridges the gap between general-pur
 
 ## Citation
 @article{cao2026ascendkernelgen,
-  title={Ascend KernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units},
+  title={AscendKernelGen: A Systematic Study of LLM-Based Kernel Generation for Neural Processing Units},
   author={Xinzi Cao and Jianyang Zhai and Pengfei Li and Zhiheng Hu and Cen Yan and Bingxu Mu and Guanghuan Fang and Bin She and Jiayu Li and Yihan Su and Dongyang Tao and Xiansong Huang and Fan Xu and Feidiao Yang and Yao Lu and Chang-Dong Wang and Yutong Lu and Weicheng Xue and Bin Zhou and Yonghong Tian},
   journal={arXiv preprint arXiv:2601.07160},
   year={2026},