Update README.md

README.md

@@ -11,6 +11,6 @@ pipeline_tag: translation
 
 We further evaluate **DatA-SQL-1.5B** on the **BIRD** development set using self-consistency voting.  
 Under **Vote@8**, our model achieves an **execution accuracy (EX) of 55.86 %**, establishing a new state of the art among open-source Text-to-SQL systems at the 1.5 B scale.  
-This result demonstrates that even compact models can acquire strong reasoning and SQL generation abilities when trained with our **reasoning-guided data augmentation** and **execution-aware multi-agent framework**.  
+This result demonstrates that even compact models can acquire strong reasoning and SQL generation abilities when trained with our **reasoning-guided data augmentation**. 
 The lightweight design ensures low training and inference costs while maintaining high execution robustness.  
 Overall, **DatA-SQL-1.5B** achieves competitive accuracy compared with much larger GPT-based pipelines, highlighting the efficiency and practicality of our approach.