Update README.md

README.md

@@ -1,3 +1,62 @@
----
-license: mit
----
+---
+license: apache-2.0
+task_categories:
+- question-answering
+- text-generation
+language:
+- en
+tags:
+- agent
+size_categories:
+- n<1K
+---
+# GISA: A Benchmark for General Information-Seeking Assistant</h1>
+
+## 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]().
+
+## Data Schema
+
+#### 1. encrypted_question.jsonl
+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
+  
+#### 2. answer/[id].csv
+The file contains the answer corresponds to the question [id].
+
+#### 3. trace/[id].json
+The file conatins the human trajectory of the question [id], with the following keys:
+- search (list): the queries issued by the annotator
+- result (dict): the search result of each query
+- click (list): the click behaviors made by the annotator
+- 
+## 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"]))
+```