Add paper link, code link and task category

README.md

@@ -1,5 +1,9 @@
 ---
 license: mit
+task_categories:
+- image-text-to-text
+language:
+- en
 configs:
 - config_name: default
   data_files:
@@ -26,17 +30,22 @@ dataset_info:
   download_size: 2215158720
   dataset_size: 2215159257.0
 ---
+
+# HR-MMSearch
+
+[**Paper**](https://huggingface.co/papers/2512.24330) | [**Code**](https://github.com/OpenSenseNova/SenseNova-MARS)
+
 ## Dataset Description
 
 **HR-MMSearch** is a benchmark designed to evaluate the **Agentic Reasoning** and **Search** capabilities of Multimodal Large Language Models in complex visual tasks.
 
-This dataset was introduced by **SenseTime Research** in the paper *SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning*.
+This dataset was introduced by **SenseTime Research** in the paper [SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning](https://huggingface.co/papers/2512.24330).
 
 ### Key Features:
 * **High-Resolution Images:** Contains high-resolution image inputs, requiring the model to possess fine-grained visual perception capabilities.
 * **Knowledge-Intensive:** Questions often cannot be answered solely by looking at the image; they require the model to combine visual information with external knowledge.
 * **Search-Driven:** Designed to assess the model's ability to use tools (such as search engines and image cropping tools) to acquire information and perform reasoning.
-* **Multi-Domain Coverage:** Covers various vertical domains including Sports, Entertainment \& Culture, Science \& Technology, Business \& Finance, Games, Academic Research, Geography \& Travel, and Others.
+* **Multi-Domain Coverage:** Covers various vertical domains including Sports, Entertainment & Culture, Science & Technology, Business & Finance, Games, Academic Research, Geography & Travel, and Others.
 
 ## Data Fields
 
@@ -44,7 +53,7 @@ The dataset typically follows a JSON structure. Below are the descriptions of th
 
 * `sample_id` (string): A unique identifier for the sample.
 * `query` (string): The user's query text.
-* `query_image` (string): The file path to the image corresponding to the query.
+* `query_image` (image): The image corresponding to the query.
 * `ground_truth` (string): The ground truth answer to the question.
 * `difficulty` (string): The difficulty level of the question (e.g., `hard`, `easy`).
 * `category` (string): The domain category of the question (e.g., `sports`, `technology`).
@@ -85,4 +94,4 @@ print(dataset['train'][0])
   journal={arXiv preprint arXiv:2512.24330},
   year={2025}
 }
-```
+```