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README.md

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+# GISA: A Benchmark for General Information-Seeking Assistant</h1>
+
+<p>
+<a href="https://github.com/RUC-NLPIR/GISA/blob/main/LICENSE"><img src="https://img.shields.io/badge/License-Apache-blue" alt="license"></a>
+<a href=""><img src="https://img.shields.io/badge/Paper-Arxiv-red"></a>
+<a href="https://ruc-nlpir.github.io/GISA/"><img src="https://img.shields.io/badge/Leaderboard-GISA-orange"></a>
+</p>
+
+**Authors**: Yutao Zhu, Xingshuo Zhang, Maosen Zhang, Jiajie Jin, Liancheng Zhang, Xiaoshuai Song, Kangzhi Zhao, Wencong Zeng, Ruiming Tang, Han Li, Ji-Rong Wen, and Zhicheng Dou
+
+## Benchmark Highlights
+GISA is a benchmark for General Information-Seeking Assistants with 373 human-crafted queries that reflect real-world information needs. It includes both stable and live subsets, four structured answer formats (item, set, list, table), and complete human search trajectories for every query.
+- **Diverse answer formats with deterministic evaluation.**  
+  GISA uses four structured answer types (item, set, list, table) with strict matching metrics for reproducible evaluation, avoiding subjective LLM judging while preserving task diversity.
+- **Unified deep + wide search capabilities.**  
+  Tasks require both vertical reasoning and horizontal information aggregation across sources, evaluating long-horizon exploration and summarization in one benchmark.
+- **Dynamic, anti-static evaluation.**  
+  Queries are split into stable and live subsets; the live subset is periodically updated to reduce memorization and keep the benchmark challenging over time.
+- **Process-level supervision via human trajectories.**  
+  Full human search trajectories are provided for every query, serving as gold references for process reward modeling and imitation learning while validating task solvability.
+
+## Evaluation
+Please refer to our [GitHub](https://github.com/RUC-NLPIR/GISA).
+
+## Data Schema
+Each row contains:
+
+- id (int): the ID of the question (it is **not** continuous)
+- question (str): the question after encryption
+- answer_type (str): the type of the answer, can be item, set, list, or table
+- question_type (str): the type of the question, can be stable or live
+- topic (str): the topic of the question, can be TV Shows \& Movies, Science \& Technology, Art, History, Sports, Music, Video Games, Geography, Politics, or Other
+- canary (str): the password used for decryption
+
+## Loading Method
+
+```python
+def derive_key(password: str, length: int) -> bytes:
+    hasher = hashlib.sha256()
+    hasher.update(password.encode())
+    key = hasher.digest()
+    return key * (length // len(key)) + key[: length % len(key)]
+
+
+def decrypt(ciphertext_b64: str, password: str) -> str:
+    encrypted = base64.b64decode(ciphertext_b64)
+    key = derive_key(password, len(encrypted))
+    decrypted = bytes(a ^ b for a, b in zip(encrypted, key))
+    return decrypted.decode()
+
+obj["question"] = decrypt(str(obj["question"]), str(obj["canary"]))
+```
+
+
+## Citation
+```bibtex
+@article{GISA,
+  title     = {GISA: A Benchmark for General Information Seeking Assistant},
+  author    = {Yutao Zhu and
+               Xingshuo Zhang and
+               Maosen Zhang and
+               Jiajie Jin and
+               Liancheng Zhang and
+               Xiaoshuai Song and
+               Kangzhi Zhao and
+               Wencong Zeng and
+               Ruiming Tang and
+               Han Li and
+               Ji-Rong Wen and
+               Zhicheng Dou},
+  booktitle = {TBD},
+  year      = {2026}
+}
+