In September 2026, as the Transformer architecture became widely used in NLP, bottlenecks such as high memory usage, low training efficiency, and high inference costs became increasingly apparent. Researchers proposed a new method called “Impact Score” to address these challenges. This research was published on arXiv, aiming to quantify the effective contribution of attention heads in prompt injection detection classification decisions. By combining the directional impact on logits with the structural contribution of residual flows, this method achieved multi-scale analysis from individual heads and layers to the entire network. Experiments showed that applying this framework to the DeBERTa model significantly revealed differences in decision-making behavior between correct and incorrect predictions, and achieved a balance between fine-grained circuit analysis and global output methods. Additionally, industry observations indicate that the Transformer architecture is facing four major technological challenges, seeking to find the next wave of AI opportunities.
# The bottleneck of the Transformer architecture is becoming increasingly apparent. Which of the four major technical approaches will become the next AI trend?
## Background: The Rise and Bottlenecks of Transformer
After its release in 2017, the Transformer architecture quickly became the cornerstone of natural language processing. Its core idea—using self-attention mechanisms to achieve sequence modeling—has been widely applied in models such as BERT, GPT, and LLaMA. However, as the size of models continues to expand, the Transformer architecture has also revealed increasingly obvious bottlenecks:
- **High memory consumption**: As the number of parameters increases, memory requirements grow exponentially, limiting the scale of model training.
- **Low training efficiency**: Long-sequence modeling leads to the gradient vanishing problem, and the training speed is much slower than that of traditional RNN architectures.
- **High inference costs**: Large-scale pre-trained models have slow inference speeds, making it difficult to meet the requirements of real-time applications.
- **Data dependence…