Replace Arxiv paper link with Hugging Face paper link

This PR replaces the arXiv paper link with the Hugging Face Papers link for improved accessibility and discoverability of the paper associated with this model.

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}
-}
+}
+```