Improve HSCodeComp dataset card: Add metadata, update paper/code links, and sample usage

This PR significantly enhances the dataset card for `AIDC-AI/HSCodeComp` by:
- Adding `license`, `task_categories` (`text-classification`, `question-answering`), and relevant `tags` (`e-commerce`, `agentic-ai`, `code-classification`, `rule-application`, `benchmark`) to the YAML metadata, improving discoverability.
- Updating the primary paper link to the specific HSCodeComp arXiv paper: `https://arxiv.org/abs/2510.19631`.
- Updating the code link to the HSCodeComp subdirectory within the main GitHub repository: `https://github.com/AIDC-AI/Marco-Search-Agent/tree/main/HSCodeComp`.
- Renaming the `Quick Start` section to `Sample Usage` for clarity, while preserving its valuable content.
- Consolidating and streamlining external links at the top of the card for better navigation.
- Updating all internal relative paper links and the specific `Data` link to ensure they point to the correct external resources.

These updates provide a more comprehensive and user-friendly dataset card for researchers.

README.md

@@ -1,5 +1,20 @@
+---
+license: apache-2.0
+task_categories:
+  - text-classification
+  - question-answering
+tags:
+  - e-commerce
+  - agentic-ai
+  - code-classification
+  - rule-application
+  - benchmark
+---
+
 # HSCodeComp: A Realistic and Expert-Level Benchmark for Deep Search Agents in Hierarchical Rule Application
 
+[Paper](https://arxiv.org/abs/2510.19631) | [Code](https://github.com/AIDC-AI/Marco-Search-Agent/tree/main/HSCodeComp) | [Dataset on Hugging Face](https://huggingface.co/datasets/AIDC-AI/HSCodeComp)
+
 [![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
 [![Python 3.10+](https://img.shields.io/badge/Python-3.10%2B-blue.svg)](https://www.python.org/downloads/)
 [![Hugging Face](https://img.shields.io/badge/🤗%20Hugging%20Face-Dataset-yellow.svg)](https://huggingface.co/datasets/AIDC-AI/HSCodeComp)
@@ -10,8 +25,7 @@
 
 [_**Alibaba International Digital Commerce**_](https://aidc-ai.com)
 
-<img src="https://octodex.github.com/images/original.png" alt="GitHub Octocat" width="22" height="22"> [**Github**](https://github.com/AIDC-AI/Marco-DeepWideSearch-Agent/HSCodeComp) 🤗  [**Hugging Face**](https://huggingface.co/datasets/AIDC-AI/HSCodeComp) 📝  [**Paper**](../assets/HSCodeComp.pdf) 🗂️  [**Data**](https://github.com/AIDC-AI/Marco-DeepWideSearch-Agent/HSCodeComp/data/test_data.jsonl)
-
+🗂️  [**Data**](https://github.com/AIDC-AI/Marco-Search-Agent/tree/main/HSCodeComp/data/test_data.jsonl)
 
 </div>
 
@@ -37,7 +51,7 @@ Besides, ablation study also reveals that **inference-time scaling fails to impr
 ---
 
 ## 🔥 News
-* [2025/10/] 🔥 We released the [paper](../assets/HSCodeComp.pdf) and [dataset](https://huggingface.co/datasets/AIDC-AI/HSCodeComp) of our challenging HSCodeComp dataset.
+* [2025/10/] 🔥 We released the [paper](https://arxiv.org/abs/2510.19631) and [dataset](https://huggingface.co/datasets/AIDC-AI/HSCodeComp) of our challenging HSCodeComp dataset.
 
 ---
 
@@ -69,7 +83,7 @@ The HSCode $y \in \mathcal{Y}$ is a single **10-digit numeric string** $\mathcal
 - **First 6 digits**: HS sub-heading
 - **Last 4 digits (7-10)**: Country-specific codes
 
-The 10-digit HSCode must follow a valid path in the official HS taxonomy. Please refer to [our paper](../assets/HSCodeComp.pdf) for more details about these data.
+The 10-digit HSCode must follow a valid path in the official HS taxonomy. Please refer to [our paper](https://arxiv.org/abs/2510.19631) for more details about these data.
 
 ### Dataset Collection and Statistic
 
@@ -90,7 +104,7 @@ We engage several domain experts in HSCode prediction, and conduct a well-design
 
 ---
 
-## ⚙️ Quick Start
+## ⚙️ Sample Usage
 
 ### 📁 Repository Structure
 
@@ -159,7 +173,7 @@ Performance degrades when human decision rules are included in the system prompt
 
 Two kinds of inference-time scaling strategy (majority voting and self-reflection) fails to effectively improve the performance.
 
-> For complete experimental results, please refer to [our paper](../assets/HSCodeComp.pdf).
+> For complete experimental results, please refer to [our paper](https://arxiv.org/abs/2510.19631).
 
 ---
 
@@ -178,4 +192,4 @@ This project is licensed under the **Apache-2.0 License**
 ## ⚠️ DISCLAIMER
 Our datasets are constructed using publicly accessible product data sources. Although we remove the product image and url in the HSCodeComp, we still cannot guarantee that our datasets are completely free of copyright issues or improper content. If you believe anything infringes on your rights or generates improper content, please contact us ([Tian Lan](https://github.com/gmftbyGMFTBY) and [Longyue Wang](https://www.longyuewang.com/)), and we will promptly address the matter.
 
----
+---