Add metadata, paper link, and GitHub repository to dataset card

README.md

@@ -1,22 +1,36 @@
 ---
-license: mit
 language:
 - en
+license: mit
+task_categories:
+- text-retrieval
 ---
 
-This repo contains the fixed wiki corpus, which is based on the wiki-18 corpus and supplemented by the HotpotQA, 2WikiMultiHopQA, Musique datasets. Comparing with the original Wiki-18 corpus, this version contains 295,311 new documents which are critical for answering the question for the above three datasets.
+This repository contains the **Wiki-Fixed** corpus, presented in the paper [Retrieval, Reward, and Training Protocols: What Matters in Training Search Agents?](https://huggingface.co/papers/2605.27881).
 
-We have already embedded the corpus with the Qwen3-8B-Embedding. As the Qwen3-8B-Embedding is trained with the MRL, we just provide the 4096-dimension version. If you are limited by the computing resource, please just run
+**Code:** [https://github.com/YiboZhao624/SearchAgentReview](https://github.com/YiboZhao624/SearchAgentReview)
 
-```
+### Description
+
+The Wiki-Fixed corpus is based on the Wikipedia 2018 (Wiki-18) corpus and supplemented by the HotpotQA, 2WikiMultiHopQA, and Musique datasets. Compared to the original Wiki-18 corpus, this version contains 295,311 new documents which are critical for answering questions in these multi-hop reasoning datasets.
+
+The corpus has been embedded using **Qwen3-8B-Embedding**. As the Qwen3-8B-Embedding is trained with Matryoshka Representation Learning (MRL), we provide the 4096-dimension version. 
+
+### Sample Usage
+
+If you are limited by computing resources, you can use the provided dimensionality reduction script to target a lower dimension (any dimension between 32 and 4096):
+
+```bash
 python convert_faiss_dim.py --input {your_faiss_manifest_path} --output {your_target_file_name_without_postfix} --target_dim {any_dim_between_32_and_4096} --normalize
 ```
 
-Then you can launch the search service with the converted index along with the original corpus. To enable the Qwen3-8B-Embedding's MRL capability, you should launch the vllm server with the config ----hf-overrides {"is_matryoshka": true}, and send request with the dimension argument.
+After conversion, you can launch the search service with the converted index along with the original corpus. To enable the Qwen3-8B-Embedding's MRL capability, launch the vLLM server with the config `--hf-overrides {"is_matryoshka": true}`, and send requests with the dimension argument.
+
+### Citation
 
 If you find this repo helpful, please cite:
 
-```
+```bibtex
 @misc{zhao2026retrievalrewardtrainingprotocols,
       title={Retrieval, Reward, and Training Protocols: What Matters in Training Search Agents?}, 
       author={Yibo Zhao and Zichen Ding and Jiayi Wu and Zun Wang and Xiang Li},