Update README.md

README.md

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 ---
 license: apache-2.0
+pipeline_tag: time-series-forecasting
+library_name: pytorch
+base_model:
+- amazon/chronos-2
+- google/timesfm-2.5-200m-pytorch
+- ibm-research/flowstate
+- NX-AI/TiRex-1.1-gifteval
+- ibm-research/patchtst-fm-r1
+- Datadog/Toto-2.0-2.5B
+tags:
+- time-series-forecasting
+- foundation-models
+- pretrained-models
+- time-series
+- timeseries
+- forecasting
+- ensemble
+- meta-learning
+- agentic
+- gift-eval
+- xgboost
+
 ---
+
+> [!WARNING]
+> **This is a benchmarking artifact for the GIFT-Eval submission.**
+> CastStar is an agentic time-series forecasting system built on top of multiple foundation forecasting models. The submitted GIFT-Eval result uses pre-computed forecasts and a meta-selection/ensemble layer over the model pool. It is intended to make the leaderboard submission reproducible and comparable under the GIFT-Eval protocol.
+
+## What This Is
+
+CastStar combines forecasts from a pool of time-series foundation models and uses a learned gating strategy to select or weight base-model predictions for each forecasting setting. The system follows the same general meta-forecasting idea as FFORMA-style model selection, where lightweight time-series features and dataset metadata guide the choice of forecasting experts.
+
+The GIFT-Eval submission evaluates CastStar on all 97 dataset configurations using the official CSV format.
+
+## Model Pool
+
+CastStar uses the following base forecasting models. Compared with the Toto-2.0-FnF setup, CastStar only uses the **Toto-2.0-2.5B** checkpoint from the Toto family.
+
+|  #   | Model                                                        |  Family   |
+| :--: | ------------------------------------------------------------ | :-------: |
+|  0   | [chronos-2](https://huggingface.co/amazon/chronos-2)         |  Chronos  |
+|  1   | [timesfm-2.5](https://huggingface.co/google/timesfm-2.5-200m-pytorch) |  TimesFM  |
+|  2   | [flowstate](https://huggingface.co/ibm-research/flowstate)   | FlowState |
+|  3   | [tirex](https://huggingface.co/NX-AI/TiRex-1.1-gifteval)     |   TiRex   |
+|  4   | [patchtst-fm](https://huggingface.co/ibm-research/patchtst-fm-r1) | PatchTST  |
+|  5   | [toto-2.0-2.5b](https://huggingface.co/Datadog/Toto-2.0-2.5B) | Toto 2.0  |
+
+## Key Features
+
+- **Agentic forecasting system:** CastStar uses multiple forecasting experts and a decision layer to produce final probabilistic forecasts.
+- **Foundation-model pool:** The model pool includes Chronos-2, TimesFM-2.5, FlowState, TiRex, PatchTST-FM, and Toto-2.0-2.5B.
+- **GIFT-Eval compatible:** Results are submitted in the official GIFT-Eval format with 97/97 dataset configurations and 11 evaluation metrics.
+- **No test-data leakage:** The submitted configuration reports no GIFT-Eval test-data leakage.
+- **Benchmark-focused artifact:** The submitted result is designed for leaderboard evaluation and reproducibility under the GIFT-Eval protocol.
+
+## GIFT-Eval Submission
+
+The submitted files are:
+
+```text
+results/CastStar/all_results.csv
+results/CastStar/config.json
+```
+
+Submission metadata:
+
+```json
+{
+  "model": "CastStar",
+  "model_type": "agentic",
+  "model_dtype": "float32",
+  "model_link": "https://huggingface.co/USTC-AGI/CastStar",
+  "code_link": "https://github.com/ustc-time-series/CastStar",
+  "org": "CastStar",
+  "testdata_leakage": "No",
+  "replication_code_available": "No"
+}
+```
+
+## Additional Resources
+
+- [GIFT-Eval benchmark](https://huggingface.co/spaces/Salesforce/GIFT-Eval)
+- [GIFT-Eval GitHub repository](https://github.com/SalesforceAIResearch/gift-eval)
+- [CastStar GitHub repository](https://github.com/ustc-time-series/CastStar)
+- [CastStar model page](https://huggingface.co/USTC-AGI/CastStar)
+
+## Citation
+
+If you use CastStar, please cite the corresponding CastStar paper or repository when available.