Update README.md

README.md

@@ -90,8 +90,6 @@ The tables below describe the **stored metadata**.
 | `text` | Query text. |
 | `query_image_file_name` | Query image reference information. |
 | `query_image_alt_texts` | Alternative text associated with query images. |
-| `target` | Relevant gallery IDs within the same topic. |
-| `hard_negative` | Targeted hard-negative annotations. |
 | `reasoning_demand` | Overall cognitive demand: perception-only, knowledge-intensive, reasoning-intensive, or both. |
 | `reasoning_skill` | Reasoning-skill annotation; an instance may involve multiple skills. |
 
@@ -101,17 +99,9 @@ The tables below describe the **stored metadata**.
 | `file_name` | Candidate image path. |
 | `alt_text` | Alternative text associated with the image. |
 
-Image paths are relative to the corresponding metadata directory. [ImageFolder](https://huggingface.co/docs/datasets/image_dataset#imagefolder) converts image references into image features—for example, `file_name` becomes `image`. Inspect `.features` for the loaded column names, and account for queries without images.
-
 ## Evaluation
 
-- **Candidate pool:** evaluate each topic against its own full gallery. Use `target` for relevance judgments and reasoning annotations for analysis, keeping these annotations out of model inputs.
-- **Metrics:** Recall@1 (primary), Recall@5, Recall@10, nDCG@5, nDCG@10, and nDCG@20.
-- **Aggregation:** report equal-weight macro averages across the 17 topics and across the six reasoning skills. The leaderboard's Overall Avg is the mean of these two macro averages; submitted scores use the 0–100 scale.
-
-The [paper](https://arxiv.org/abs/2602.09839) documents construction, sources, and the evaluation setup, including image captioning for text-only baselines. Preserve the test split for held-out evaluation and document any preprocessing when reporting results.
-
-**Submit results:** the leaderboard currently covers embedding models. Follow its submission instructions, complete the [score template](https://huggingface.co/spaces/XLearning-SCU/ARK-Bench-Leaderboard/resolve/main/scores_template.json), and submit a pull request to the Space's [`scores/` directory](https://huggingface.co/spaces/XLearning-SCU/ARK-Bench-Leaderboard/tree/main/scores).
+If you would like to evaluate your model, submit your results, and compare its performance with other leading models, visit the [ARK-Bench Leaderboard](https://huggingface.co/spaces/XLearning-SCU/ARK-Bench-Leaderboard) for detailed information. We welcome your participation and contributions to ARK-Bench.
 
 ## License