Update dataset card with repository and citation

README.md

@@ -21,10 +21,18 @@ tags:
 
 # Pi-Bench Tasks
 
+**Code and full benchmark:** [GitHub repository](https://github.com/Simplified-Reasoning/Pi-Bench)  
+**Paper:** [arXiv:2605.14678](https://arxiv.org/abs/2605.14678)  
+**Project page:** [simplified-reasoning.github.io/Pi-Bench](https://simplified-reasoning.github.io/Pi-Bench/)
+
 This lightweight dataset exposes only the `task.yaml` files from Pi-Bench so
 people can quickly inspect the benchmark tasks in the Hugging Face Dataset
 Viewer.
 
+Pi-Bench evaluates proactive personal assistant agents in long-horizon
+workflows. It contains 100 multi-turn tasks across 5 domain-specific personas:
+`researcher`, `marketer`, `pharmacist`, `law_trainee`, and `financier`.
+
 Each row corresponds to one `data/<role>/tasks/<task_id>/task.yaml` file. The
 `yaml` column preserves the original YAML text, while the other columns extract
 common fields for filtering and browsing.
@@ -54,4 +62,20 @@ common fields for filtering and browsing.
 - `title`, `display_title`, `description`, `task_type`, `difficulty`
 - `initial_input`, `hidden_intents`, `hidden_intent_count`
 - `has_objectives`, `objectives_json`, `metadata_json`
-- `yaml_path`, `yaml`
+- `yaml_path`, `yaml`
+
+## Citation
+
+If you use Pi-Bench, please cite:
+
+```bibtex
+@misc{zhang2026pibenchevaluatingproactivepersonal,
+  title={${\pi}$-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows},
+  author={Haoran Zhang and Luxin Xu and Zhilin Wang and Runquan Gui and Shunkai Zhang and Haodi Lei and Zihao He and Bingsu He and Chicheng Qin and Tong Zhu and Xiaoye Qu and Yang Yang and Yu Cheng and Yafu Li},
+  year={2026},
+  eprint={2605.14678},
+  archivePrefix={arXiv},
+  primaryClass={cs.AI},
+  url={https://arxiv.org/abs/2605.14678}
+}
+```