Update README.md

README.md

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     path: dataset/sample_10.csv
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-**This work is currently under review. The full dataset will be released progressively.**
+> **Note:** This work is currently under review. The full dataset will be released progressively.
 
 # MobilityBench: A Benchmark for Evaluating Route-Planning Agents in Real-World Mobility Scenarios
 
 [Paper](https://arxiv.org/abs/2602.22638) | [GitHub](https://github.com/AMAP-ML/MobilityBench)
 
-IntTravel is the first large-scale public dataset for integrated travel recommendation, including **4.1 billion interactions from 163 million users with 7.3 million POIs**. Built upon this dataset, the authors introduce an end-to-end, decoder-only generative framework for multi-task recommendation. It incorporates information preservation, selection, and factorization to balance task collaboration with specialized differentiation.
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-## Dataset
 **MobilityBench** is a scalable benchmark for evaluating route-planning agents in real-world mobility scenarios. It is built from large-scale, anonymized mobility queries from **Amap**, organized with a comprehensive task taxonomy, and provides **structured ground truth** (required tool calls + verifiable evidence). All tool calls are executed in a **deterministic replay sandbox** for reproducible, multi-dimensional evaluation.
 
 **Scale & Coverage:** 100,000 episodes across **22** countries and **350+** cities (including metropolitan areas), with a **long-tailed** geographic distribution.
@@ -35,8 +30,6 @@ IntTravel is the first large-scale public dataset for integrated travel recommen
 - **42.5%** Basic Route Planning
 - **11.3%** Preference-Constrained Route Planning
 
-**Download:** [HuggingFace - MobilityBench](https://huggingface.co/datasets/GD-ML/MobilityBench/tree/main) -> place files into `data/`
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 #### Data Format
 
 | Field | Description |