For the Inter PLM, we experimented with a larger model, `Qwen3-embedding-0.6B`, and also applied LoRA fine-tuning to it. For the Tracer LLM, we used `GPT2-M`. The results are shown below:

| Inter PLM              | Tracer LLM | LoRA | ASSIST09 | ASSIST17 |
| ---------------------- | ---------- | ---- | -------- | -------- |
| Qwen3-embedding-0.6B   | GPT2-M     | No   | 0.828    | 0.765    |
| Qwen3-embedding-0.6B   | GPT2-M     | Yes  | **0.835**| **0.772**|

These results confirm that using a larger backbone model and applying LoRA for more efficient fine-tuning can further enhance the performance of LLM4KT. We have updated our paper with these findings. Regarding the LoRA configuration, we used a rank of r=32 and applied it to the query and value projection layers (`q_proj` and `v_proj`) of the Inter PLM.

**3. Regarding the request for 5-fold cross-validation:**

We appreciate this suggestion for ensuring the robustness of our results. In fact, all the experimental results reported in our paper were obtained through 5-fold cross-validation. We have clarified this in the revised manuscript. The standard deviation of the results across the 5 folds was consistently low (less than 1.2% in AUC), which demonstrates the stability and reliability of our proposed method. We apologize for not making this clearer in the initial submission and have updated the experimental setup section accordingly.

We hope these detailed responses and new experiments have addressed your concerns. We are grateful for your constructive feedback, which has helped us improve the quality of our work. 