Replace Arxiv paper link with Hugging Face paper link

README.md

@@ -3,15 +3,15 @@ base_model:
 - Qwen/Qwen2.5-3B-Instruct
 datasets:
 - ulab-ai/Time-Bench
+library_name: transformers
 license: apache-2.0
+pipeline_tag: text-generation
 tags:
 - temporal-reasoning
 - reinforcement-learning
 - large-language-models
 paperswithcode:
   arxiv_id: 2505.13508
-library_name: transformers
-pipeline_tag: text-generation
 ---
 
 <center>
@@ -19,12 +19,12 @@ pipeline_tag: text-generation
 </center>
 
 <div align="center">
-<a href="https://huggingface.co/datasets/ulab-ai/Time-Bench"> 📊 <strong>Dataset</strong></a> | <a href="https://github.com/ulab-uiuc/Time-R1">🚀 <strong>Code</strong></a> | <a href="https://arxiv.org/abs/2505.13508">📖 <strong>Paper</strong></a>
+<a href="https://huggingface.co/datasets/ulab-ai/Time-Bench"> 📊 <strong>Dataset</strong></a> | <a href="https://github.com/ulab-uiuc/Time-R1">🚀 <strong>Code</strong></a> | <a href="https://huggingface.co/papers/2505.13508">📖 <strong>Paper</strong></a>
 </div>
 
 # Time-R1 Model Series
 
-This collection hosts the official checkpoints for the **Time-R1** model, as described in the paper "Time-R1: Towards Comprehensive Temporal Reasoning in LLMs". Time-R1 is a 3B parameter Large Language Model trained with a novel three-stage reinforcement learning curriculum to endow it with comprehensive temporal abilities: understanding, prediction, and creative generation.
+This collection hosts the official checkpoints for the **Time-R1** model, as described in the paper [Time-R1: Towards Comprehensive Temporal Reasoning in LLMs](https://huggingface.co/papers/2505.13508). Time-R1 is a 3B parameter Large Language Model trained with a novel three-stage reinforcement learning curriculum to endow it with comprehensive temporal abilities: understanding, prediction, and creative generation.
 
 These models are trained using the [Time-Bench dataset](https://huggingface.co/datasets/ulab-ai/Time-Bench).
 
@@ -52,7 +52,7 @@ This model builds upon Stage 1 capabilities to predict future event timings.
 * **[Time-R1-Theta2](https://huggingface.co/ulab-ai/Time-R1-Theta2):** Checkpoint θ₂, after Stage 2 training.
    * *Focus: Predicting the timing of future events occurring after its initial knowledge cutoff.*
 
-Please refer to the [main paper](https://arxiv.org/abs/2505.13508) for detailed discussions on the architecture, training methodology, and comprehensive evaluations.
+Please refer to the [main paper](https://huggingface.co/papers/2505.13508) for detailed discussions on the architecture, training methodology, and comprehensive evaluations.
 
 ## How to Use
 
@@ -76,4 +76,5 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
   author={Liu, Zijia and Han, Peixuan and Yu, Haofei and Li, Haoru and You, Jiaxuan},
   journal={arXiv preprint arXiv:2505.13508},
   year={2025}
-}
+}
+```