Add paper link, GitHub link, and sample usage

README.md

@@ -1,12 +1,19 @@
 ---
-license: mit
-task_categories:
-- question-answering
 language:
 - en
+license: mit
+task_categories:
+- other
+tags:
+- image-retrieval
+- multimodal-agents
+- visual-history
 ---
+
 # DISBench: DeepImageSearch Benchmark
 
+[**Paper**](https://huggingface.co/papers/2602.10809) | [**GitHub**](https://github.com/RUC-NLPIR/DeepImageSearch) | [**Leaderboard**](https://huggingface.co/spaces/RUC-NLPIR/DISBench-Leaderboard)
+
 DISBench is the first benchmark for context-aware image retrieval over visual histories. It contains 122 queries across 57 users and 109,467 photos, requiring multi-step reasoning over corpus-level context.
 
 ## Download
@@ -23,6 +30,28 @@ huggingface-cli download RUC-NLPIR/DISBench --local-dir DISBench
 python download_images.py --photo-ids-path photo_ids --images-path images
 ```
 
+## Sample Usage
+
+To use DISBench with the ImageSeeker agent framework, you can follow these steps from the official repository:
+
+### 1. Installation
+```bash
+git clone https://github.com/RUC-NLPIR/DeepImageSearch.git
+cd DeepImageSearch
+pip install -r requirements.txt
+```
+
+### 2. Run the Agent
+```bash
+python ImageSeeker/run_agent.py \
+    --dataset_path DISBench \
+    --model_name "claude-opus-4-5-20251101" \
+    --api_base_url "YOUR_API_BASE_URL" \
+    --api_key "YOUR_API_KEY" \
+    --embedding_model "qwen3-vl-embedding-8b" \
+    --max_turns 30 \
+    --output_dir results/claude-opus-4.5/
+```
 
 ## File Structure
 
@@ -93,10 +122,6 @@ Each line is a JSON object representing one photo's metadata:
 
 > **Note:** Optional fields (`longitude`, `latitude`, `accuracy`, `address`, `capturedevice`) are omitted entirely when unavailable — they will not appear as keys in the JSON object.
 
-### images/{user_id}/{photo_id}.jpg
-
-Photo files organized by user. Each user's collection contains approximately 2,000 photos accumulated chronologically from their photosets.
-
 ### photo_ids/{user_id}.txt
 
 Each line represents one photo ID and its hash on aws storage in the format `{photo_id}\t{hash}`:
@@ -104,11 +129,6 @@ Each line represents one photo ID and its hash on aws storage in the format `{ph
 1205732595	c45044fd7b5c9450b2a11adc6b42d
 ```
 
-| Field | Type | Description |
-|:------|:-----|:------------|
-| `photo_id` | string | Unique photo identifier |
-| `hash` | string | Hashed value of the photo on aws storage |
-
 ## Dataset Statistics
 
 | Statistic | Value |
@@ -127,6 +147,20 @@ Each line represents one photo ID and its hash on aws storage in the format `{ph
 
 DISBench is constructed from [YFCC100M](https://multimediacommons.wordpress.com/yfcc100m-core-dataset/), which preserves a hierarchical structure of users → photosets → photos. All images are publicly shared under Creative Commons licenses. Photoset boundaries are used during construction but are **not** provided to models during evaluation.
 
+## Citation
+
+```bibtex
+@misc{deng2026deepimagesearchbenchmarkingmultimodalagents,
+  title={DeepImageSearch: Benchmarking Multimodal Agents for Context-Aware Image Retrieval in Visual Histories}, 
+  author={Chenlong Deng and Mengjie Deng and Junjie Wu and Dun Zeng and Teng Wang and Qingsong Xie and Jiadeng Huang and Shengjie Ma and Changwang Zhang and Zhaoxiang Wang and Jun Wang and Yutao Zhu and Zhicheng Dou},
+  year={2026},
+  eprint={2602.10809},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2602.10809}
+}
+```
+
 ## License
 
 The DISBench dataset follows the Creative Commons licensing terms of the underlying YFCC100M data. Please refer to individual image licenses for specific usage terms.