Update README.md

README.md

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----
-license: mit
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-dataset_info:
-  features:
-  - name: sample_id
-    dtype: string
-  - name: query
-    dtype: string
-  - name: query_image
-    dtype: image
-  - name: ground_truth
-    dtype: string
-  - name: difficulty
-    dtype: string
-  - name: category
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2215159257.0
-    num_examples: 305
-  download_size: 2215158720
-  dataset_size: 2215159257.0
----
+---
+license: mit
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: sample_id
+    dtype: string
+  - name: query
+    dtype: string
+  - name: query_image
+    dtype: image
+  - name: ground_truth
+    dtype: string
+  - name: difficulty
+    dtype: string
+  - name: category
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 2215159257.0
+    num_examples: 305
+  download_size: 2215158720
+  dataset_size: 2215159257.0
+---
+## Dataset Description
+
+**HR-MMSearch** is a benchmark designed to evaluate the **Agentic Reasoning** and **Search** capabilities of Multimodal Large Language Models (VLMs) 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*.
+
+### 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.
+
+## Data Fields
+
+The dataset typically follows a JSON structure. Below are the descriptions of the main fields in each sample:
+
+* `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.
+* `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`).
+
+## Data Example
+
+Here is an example of a data entry from `HR-MMSearch`:
+
+```json
+{
+  "sample_id": "sample_0000",
+  "query": "How many seats will this team's home stadium have in 2025?",
+  "query_image": "images/sports/train_data_251015_H21.png",
+  "ground_truth": "66210",
+  "difficulty": "hard",
+  "category": "sports"
+}
+```
+
+## Data Usage
+
+You can load this dataset by:
+
+```python
+from datasets import load_dataset
+# Load the dataset
+dataset = load_dataset("sensenova/HR-MMSearch")
+# View the first sample
+print(dataset['train'][0])
+```
+
+## Citation
+
+```bibtex
+@article{xxxx,
+  title={SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning},
+  author={...},
+  journal={arXiv preprint arXiv:xxxxx},
+  year={2025}
+}
+```