| --- |
| license: mit |
| pretty_name: AISE-Bench |
| task_categories: |
| - question-answering |
| language: |
| - zh |
| - en |
| tags: |
| - academic-search |
| - knowledge-graph |
| - tool-use |
| - llm-agent |
| - benchmark |
| - question-answering |
| - information-seeking |
| configs: |
| - config_name: default |
| default: true |
| data_files: |
| - split: test |
| path: test.json |
| size_categories: |
| - n<1K |
| --- |
| |
| <div align="center"> |
|
|
| # AISE-Bench: A Full-Cycle Curated Benchmark for Information Seeking on Academic Knowledge Graphs |
|
|
| </div> |
|
|
| <p align="center"> |
| 🌐 <a href="https://aise-bench.github.io/" target="_blank">Project Page</a> • |
| 💻 <a href="https://github.com/zai-org/AISE-Bench" target="_blank">GitHub</a> • |
| 📖 <a href="https://arxiv.org/abs/2607.20498" target="_blank">KDD 2026 Paper</a> |
| </p> |
| |
| <div align="center"> |
| <img src="assets/bench.png" width="100%" /> |
| </div> |
| |
| AISE-Bench is a real-world benchmark for information seeking on academic knowledge graphs. It is built from authentic AMiner user search queries and provides human-verified academic question-answering data with executable multi-step API trajectories, standardized tool inputs, API execution outputs, and source-grounded final answers. |
|
|
| The benchmark is designed for evaluating LLM-based tool agents throughout the full information-seeking cycle: understanding user intent, planning API calls, filling parameters, executing multi-step academic KG queries, and producing final answers grounded in canonical references. |
|
|
| ## Dataset Files |
|
|
| The current version places the latest data files at the dataset root. The previous version is archived under `v1/`. |
|
|
| ```text |
| AISE-Bench/ |
| |-- README.md |
| |-- double-review-remain.json |
| |-- single-review.json |
| |-- test.json |
| `-- v1/ |
| |-- README.md |
| |-- double-review-remain.json |
| |-- single-review.json |
| `-- test.json |
| ``` |
|
|
| ### Current Version |
|
|
| | File | Examples | Description | |
| | --- | ---: | --- | |
| | `single-review.json` | 673 | Examples annotated under the single-review setting. | |
| | `test.json` | 500 | Benchmark test set from the double-review collection. | |
| | `double-review-remain.json` | 195 | Remaining double-review examples that are not included in `test.json`. | |
|
|
| The double-review collection is distributed as two non-overlapping files: `test.json` and `double-review-remain.json`. The combined `double-review.json` is intentionally omitted to avoid duplicating the same examples. |
|
|
| The default Hugging Face dataset configuration exposes only the root-level `test.json` as the `test` split. Consequently, the Dataset Viewer displays exactly 500 rows; the other files remain available for direct download. |
|
|
| ### Previous Version |
|
|
| The `v1/` directory contains the earlier release and is excluded from the default Dataset Viewer configuration. |
|
|
| | File | Examples | Description | |
| | --- | ---: | --- | |
| | `v1/single-review.json` | 1,096 | Earlier single-review data. | |
| | `v1/test.json` | 177 | Earlier test data. | |
| | `v1/double-review-remain.json` | 77 | Earlier double-review examples whose normalized question text does not occur in `v1/test.json`. | |
|
|
| The combined `v1/double-review.json` is also intentionally omitted. The archived test and remaining double-review files are kept separately. |
|
|
| ## Data Format |
|
|
| Each JSON file contains a list of examples. Every example follows the same high-level schema. |
|
|
| The `index` field is a one-based sequential position within each individual JSON file. Numbering |
| restarts from `1` in every file and does not replace the stable question identifier in `qid`. |
|
|
| Field descriptions: |
|
|
| - `index`: One-based sequential position of the example within the current file. |
| - `qid`: Unique question identifier. |
| - `question`: Original academic information-seeking query. |
| - `planning_text`: Gold multi-step API plan, including tool names, dependency relations, execution order, and parameters. |
| - `api_input`: Standardized API input parameters used for execution. |
| - `api_output`: Returned results from the academic knowledge graph APIs. |
| - `result_edit`: Human-edited final answer grounded with reference links. |
|
|
| ## Practical Uses |
|
|
| AISE-Bench can be used to evaluate and analyze academic-search agents and tool-using LLM systems. |
|
|
| - Tool-use planning: evaluate whether an agent can decompose an academic query into executable API calls. |
| - Parameter filling: test whether the agent can identify entities, constraints, keywords, institutions, authors, venues, and other required search parameters. |
| - Multi-step execution: evaluate dependency-aware reasoning across chained academic KG calls. |
| - Answer synthesis: test whether the model can generate final answers grounded in API outputs and reference links. |
| - Agent framework comparison: compare different LLM agent workflows on the same academic information-seeking tasks. |
|
|
| ## Loading the Dataset |
|
|
| Load the default 500-example test split with `datasets`: |
|
|
| ```bash |
| pip install -U datasets |
| ``` |
|
|
| ```python |
| from datasets import load_dataset |
| |
| test_data = load_dataset("zhengyang6666/AISE-Bench", split="test") |
| print(len(test_data)) # 500 |
| ``` |
|
|
| To download all current and archived JSON files, use `huggingface_hub`: |
|
|
| ```bash |
| pip install -U huggingface_hub |
| hf download zhengyang6666/AISE-Bench --repo-type dataset --local-dir ./AISE-Bench |
| ``` |
|
|
| Individual JSON files can then be loaded directly: |
|
|
| ```python |
| import json |
| |
| with open("AISE-Bench/test.json", "r", encoding="utf-8") as f: |
| data = json.load(f) |
| |
| print(len(data)) |
| print(data[0].keys()) |
| ``` |
|
|
| ## Citation |
|
|
| If you use AISE-Bench in your research, please cite the paper: |
|
|
| ```bibtex |
| @article{aisebench2026, |
| author={Zhang, Fanjin and Wang, Zhengyang and Huang, Ruixuan and Zhang, Kefan and Xin, Amy and Wang, Yuanchun and Zhao, Shu and Kharlamov, Evgeny and Tang, Jie and Li, Juanzi}, |
| title={AISE-Bench: A Full-Cycle Curated Benchmark for Information Seeking on Academic Knowledge Graphs}, |
| journal={arXiv preprint arXiv:2607.20498}, |
| year={2026} |
| } |
| ``` |
|
|
