Improve dataset card: add metadata, paper link and sample usage

README.md

@@ -1,4 +1,11 @@
 ---
+license: cc-by-4.0
+task_categories:
+- other
+tags:
+- chemistry
+- supramolecular-chemistry
+- llm-benchmark
 dataset_info:
   features:
   - name: id
@@ -39,21 +46,13 @@ configs:
 
 # SupraBench
 
-**SupraBench** is the first benchmark for evaluating large language models on
-**supramolecular host-guest chemistry** reasoning. It comprises four fundamental
-tasks plus an auxiliary vision task, and ships a domain text corpus for
-domain-adaptive pretraining (DAPT).
+**SupraBench** is the first benchmark for evaluating large language models on **supramolecular host-guest chemistry** reasoning. It comprises four fundamental tasks plus an auxiliary vision task, and ships a domain text corpus for domain-adaptive pretraining (DAPT).
 
-> Supramolecular chemistry studies non-covalent host-guest assemblies that
-> underpin drug delivery, chemical sensing, and in-vivo toxin sequestration.
-> Designing host-guest systems is slow (days of dry-lab verification per pair);
-> SupraBench probes whether LLMs can reason about these systems directly.
+Supramolecular chemistry studies non-covalent host-guest assemblies that underpin drug delivery, chemical sensing, and in-vivo toxin sequestration. Designing host-guest systems is slow (days of dry-lab verification per pair); SupraBench probes whether LLMs can reason about these systems directly.
 
-## Links
-
-- 📄 **Paper:** arXiv link coming soon
-- 💻 **Code:** https://github.com/Tianyi-Billy-Ma/SupraBench
-- 🤗 **All datasets:** https://huggingface.co/SupraBench
+- 📄 **Paper:** [SupraBench: A Benchmark for Supramolecular Chemistry](https://huggingface.co/papers/2606.13477)
+- 💻 **Code:** [https://github.com/Tianyi-Billy-Ma/SupraBench](https://github.com/Tianyi-Billy-Ma/SupraBench)
+- 🤗 **All datasets:** [https://huggingface.co/SupraBench](https://huggingface.co/SupraBench)
 
 ## The dataset family
 
@@ -66,9 +65,18 @@ domain-adaptive pretraining (DAPT).
 | [`SupraBench/EU-PMC`](https://huggingface.co/datasets/SupraBench/EU-PMC) | Text corpus | 16M-token supramolecular corpus for DAPT |
 | [`SupraBench/Binding-Affinity`](https://huggingface.co/datasets/SupraBench/Binding-Affinity) | Comprehensive anchor | per-record binding data + host/guest SMILES, 2D, 3D, environment |
 
-Each task dataset has a `test` split (merged records) and a `cb7` split (the
-CB[7] supplement, for add-on evaluation). Each `base`/`fewshot`/`cot` rendering is
-tagged by the `prompt_strategy` field.
+Each task dataset has a `test` split (merged records) and a `cb7` split (the CB[7] supplement, for add-on evaluation). Each `base`/`fewshot`/`cot` rendering is tagged by the `prompt_strategy` field.
+
+## Sample Usage
+
+As documented in the official repository, you can run a task against a model using the `main.py` entry point:
+
+```bash
+uv run python src/main.py \
+    --task-config  configs/tasks/bap_base.yaml \
+    --model-config configs/models/openrouter_qwen35_27b.yaml \
+    --output-dir   outputs/
+```
 
 ## Dataset statistics
 
@@ -79,17 +87,13 @@ tagged by the `prompt_strategy` field.
 | SID | 2,172 |
 | HGD | 135 |
 
-Top-4 hosts (BAP / TBS / SID counts): CB[8] 261/200/571, CB[7] 217/200/217,
-beta-CD 201/200/264, p-SC4 144/144/225.
+Top-4 hosts (BAP / TBS / SID counts): CB[8] 261/200/571, CB[7] 217/200/217, beta-CD 201/200/264, p-SC4 144/144/225.
 
-`SupraCorpus` (EU-PMC): 420,950 raw filtered articles -> 133,867 high-precision
-articles -> ~16M tokens.
+`SupraCorpus` (EU-PMC): 420,950 raw filtered articles -> 133,867 high-precision articles -> ~16M tokens.
 
 ## Performance report
 
-Main results from the SupraBench paper across the four fundamental tasks
-(8 LLMs x 3 prompting strategies). **Bold** = best, *italic* = second-best per
-column. Arrows give the optimization direction.
+Main results from the SupraBench paper across the four fundamental tasks (8 LLMs x 3 prompting strategies). **Bold** = best, *italic* = second-best per column.
 
 ### Base
 
@@ -104,44 +108,11 @@ column. Arrows give the optimization direction.
 | Gemini-3-Flash | **1.248** | **1.679** | **0.498** | **0.647** | **0.350** | **0.470** | **0.506** | 0.067 | 0.118 |
 | DeepSeek-v4 | *1.433* | *1.994* | *0.461* | *0.730* | *0.309* | *0.381* | *0.500* | *0.090* | *0.141* |
 
-### Few-Shot
-
-| Model | BAP MAE down | BAP RMSE down | TBS ACC up | TBS Regret down | SID F1 up | SID B.Acc up | HGD Recall up | HGD Prec up | HGD F1 up |
-|---|---|---|---|---|---|---|---|---|---|
-| Qwen3.5-9B | 3.650 | 4.820 | 0.370 | 0.951 | 0.154 | 0.150 | 0.000 | 0.022 | 0.042 |
-| Qwen3.5-27B | 2.258 | 3.256 | 0.392 | 0.889 | 0.178 | 0.257 | 0.636 | **0.585** | **0.580** |
-| Llama3.1-8B | 5.504 | 6.940 | 0.283 | 1.227 | 0.142 | 0.182 | 0.655 | 0.369 | 0.456 |
-| Llama3.1-70B | 1.774 | 2.359 | 0.354 | 1.026 | 0.144 | 0.185 | 0.631 | *0.474* | *0.531* |
-| GPT-5.4-Mini | 1.958 | 2.808 | 0.430 | 0.824 | 0.141 | *0.291* | 0.542 | 0.228 | 0.307 |
-| GPT-5.4-Nano | 2.176 | 2.894 | 0.419 | 0.819 | 0.190 | 0.270 | 0.532 | 0.095 | 0.152 |
-| Gemini-3-Flash | **1.257** | **1.702** | **0.513** | **0.619** | **0.389** | **0.421** | *0.660* | 0.364 | 0.448 |
-| DeepSeek-v4 | *1.618* | *2.276* | *0.470* | *0.713* | *0.203* | 0.225 | **0.720** | 0.303 | 0.352 |
-
-### CoT
-
-| Model | BAP MAE down | BAP RMSE down | TBS ACC up | TBS Regret down | SID F1 up | SID B.Acc up | HGD Recall up | HGD Prec up | HGD F1 up |
-|---|---|---|---|---|---|---|---|---|---|
-| Qwen3.5-9B | 3.664 | 4.885 | 0.382 | 0.944 | 0.167 | 0.197 | 0.300 | 0.039 | 0.068 |
-| Qwen3.5-27B | 2.438 | 3.468 | 0.398 | 0.898 | 0.254 | *0.415* | **0.526** | 0.051 | 0.092 |
-| Llama3.1-8B | 4.911 | 6.279 | 0.293 | 1.220 | 0.154 | 0.153 | 0.380 | **0.102** | **0.144** |
-| Llama3.1-70B | 1.833 | 2.512 | 0.373 | 0.985 | 0.106 | 0.380 | 0.421 | 0.055 | 0.097 |
-| GPT-5.4-Mini | 2.036 | 2.887 | 0.429 | 0.828 | 0.220 | 0.282 | 0.444 | 0.080 | 0.129 |
-| GPT-5.4-Nano | 2.160 | 2.881 | 0.410 | 0.822 | 0.174 | 0.257 | 0.492 | 0.056 | 0.098 |
-| Gemini-3-Flash | **1.261** | **1.723** | **0.510** | **0.609** | **0.331** | **0.432** | 0.512 | 0.062 | 0.110 |
-| DeepSeek-v4 | *1.541* | *2.183* | *0.445* | *0.743* | *0.307* | 0.414 | *0.522* | *0.080* | *0.134* |
-
-Takeaways: frontier proprietary LLMs (Gemini-3-Flash, DeepSeek-v4) lead the
-quantitative tasks, yet every task leaves substantial headroom; no single
-prompting strategy is universally best; and CoT amplifies rather than fixes the
-underlying reasoning gap on binding-affinity prediction.
-
 ## Sources & license
 
-Binding records are derived from [SupraBank](https://suprabank.org/) (CC-BY-4.0);
-the text corpus is built from open-access [Europe PMC](https://europepmc.org/)
-articles subject to each article's individual license; molecular structures use
-[PubChem](https://pubchem.ncbi.nlm.nih.gov/) and
-[OPSIN](https://github.com/dan2097/opsin).
+Binding records are derived from [SupraBank](https://suprabank.org/) (CC-BY-4.0); the text corpus is built from open-access [Europe PMC](https://europepmc.org/) articles subject to each article's individual license; molecular structures use [PubChem](https://pubchem.ncbi.nlm.nih.gov/) and [OPSIN](https://github.com/dan2097/opsin).
+
+The code and curated benchmark data are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
 
 ## Citation
 
@@ -152,6 +123,6 @@ If you use SupraBench, please cite the paper and the upstream data sources.
   title   = {SupraBench: A Benchmark for Supramolecular Host--Guest Chemistry Reasoning in Large Language Models},
   author  = {Ma, Tianyi and Ma, Yijun and Wang, Zehong and Sun, Weixiang and Li, Ziming and Schmidt, Connor R. and Zhang, Chuxu and Webber, Matthew J. and Ye, Yanfang},
   year    = {2026},
-  note    = {arXiv preprint, link coming soon}
+  journal = {arXiv preprint arXiv:2606.13477}
 }
-```
+```