| --- |
| license: apache-2.0 |
| language: |
| - zh |
| pretty_name: HyperLogic |
| size_categories: |
| - n<1K |
| task_categories: |
| - question-answering |
| - text-generation |
| tags: |
| - logical-reasoning |
| - reasoning |
| - benchmark |
| - formal-verification |
| - agents |
| configs: |
| - config_name: seed |
| data_files: |
| - split: test |
| path: data/seed.json |
| - config_name: base |
| data_files: |
| - split: test |
| path: data/base.json |
| - config_name: hard |
| data_files: |
| - split: test |
| path: data/hard.json |
| --- |
| |
| <h1 align="center">HyperLogic: A Hard, Forward-Authored Chinese Logical Reasoning Benchmark with Execution-Derived Answers</h1> |
|
|
| <p align="center"> |
| 📄 <a href="https://arxiv.org/abs/2605.19597"><b>Paper</b></a> | |
| 💻 <a href="https://github.com/Tencent-Hunyuan/HyperLogic"><b>GitHub</b></a> | |
| 📮 <a href="#evaluate-on-the-full-benchmark"><b>Evaluate on the Full Benchmark</b></a></p> |
| |
| HyperLogic is a Chinese logical-reasoning benchmark built **forward**: problems are written first, and their answers are derived afterwards by executing formal models of them. A multi-agent workflow hardens seeds written by logic-trained undergraduates without solving them; two agents from different model families independently translate each finished item into executable finite-domain models, and logic solvers derive the answers. An item is released only when its two independently corrected encodings agree, after layered, agent-assisted adjudication under human-expert oversight. |
|
|
| <p align="center"> |
| <img src="assets/overview.png" alt="Overview of HyperLogic" width="95%"> |
| </p> |
|
|
| This dataset is the **public 30%** of HyperLogic: a fixed, uniformly sampled 30% of the items of every tier, with gold answers. The remaining 70% and the formal translations of all items are held out so that results on them stay free of contamination. The prompts, scorer, and Hard agent toolkit are in the [GitHub repository](https://github.com/Tencent-Hunyuan/HyperLogic). |
|
|
| ## Configurations |
|
|
| | Config | Tier | Items | Sub-questions | Full tier | |
| |---|---|---:|---:|---| |
| | `seed` | HyperLogic-Seed | 69 | 69 | 228 items | |
| | `base` | HyperLogic-Base | 59 | 274 | 195 items · 922 sub-questions | |
| | `hard` | HyperLogic-Hard | 30 | 30 | 100 items | |
|
|
| - **Seed**: single-question seeds written by undergraduates with coursework in logic. |
| - **Base**: a shared background (about 3,300 Chinese characters) with 4.7 sub-questions on average. An item counts as correct only if every sub-question is. |
| - **Hard**: single questions over larger, coupled search spaces (backgrounds of about 8,700 characters), intended for direct answering and for agents that write code in a sandbox. |
|
|
| Samples are item-level: all sub-questions of a selected Base item are kept together. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| seed = load_dataset("tencent/HyperLogic", "seed", split="test") |
| base = load_dataset("tencent/HyperLogic", "base", split="test") |
| hard = load_dataset("tencent/HyperLogic", "hard", split="test") |
| ``` |
|
|
| Answers are free-form Chinese text and are scored with a semantic-equivalence LLM judge, not by string matching. To reproduce the paper's protocol, use `evaluate.py` from the [GitHub repository](https://github.com/Tencent-Hunyuan/HyperLogic), which writes the direct-answer prompts and scores predictions; the Hard agent conditions (sandbox only, and sandbox with the logic modeling library) are in its `hard_agent/` folder. |
|
|
| ## Data Fields |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `id` | string (Seed) / int (Base, Hard) | Item ID. Seed IDs are content-derived `seed-...` strings. | |
| | `title` | string | Short Chinese title (Seed and Base only). | |
| | `background` | string | The scenario, in Chinese. | |
| | `question` | list of strings | The question(s); one for Seed and Hard. | |
| | `answer` | list of strings | Gold answers, aligned with `question`. | |
|
|
| ## Leaderboard |
|
|
| Accuracy (%) on the **full** benchmark, mean ± standard deviation over three rollouts, every model at its highest reasoning setting. Scores on this public subset are not comparable to these numbers. |
|
|
| | Model | Seed | Base Item | Base Sub-Q | Hard Direct | Hard Sandbox | Hard + Library | |
| |---|---:|---:|---:|---:|---:|---:| |
| | GPT-5.6 Sol | **89.8** ± 3.7 | **77.6** ± 0.6 | **92.6** ± 0.2 | 13.7 ± 5.9 | 40.3 ± 4.2 | 45.7 ± 2.1 | |
| | Claude Opus 5 | 86.5 ± 0.7 | 74.2<sup>†</sup> ± 2.7 | 88.9<sup>†</sup> ± 0.7 | **15.6**<sup>†</sup> ± 1.8 | **55.7** ± 3.2 | **57.0** ± 2.6 | |
| | Kimi K3 | 88.6 ± 0.0 | 57.9 ± 1.8 | 81.6 ± 1.8 | 4.0 ± 0.0 | 41.3 ± 1.5 | 43.0 ± 8.9 | |
| | Grok 4.6 | 87.0 ± 1.8 | 52.5 ± 4.1 | 82.9 ± 0.7 | 12.0 ± 1.0 | 39.3 ± 1.5 | 38.3 ± 2.1 | |
| | DeepSeek V4 Pro | 83.9 ± 1.7 | 51.8 ± 1.5 | 81.2 ± 1.2 | 9.3 ± 1.2 | 26.0 ± 5.6 | 20.0 ± 1.7 | |
| | GLM-5.3 | 87.3 ± 0.9 | 45.6 ± 3.2 | 67.2 ± 4.9 | 3.0 ± 1.0 | 20.7 ± 0.6 | 31.3 ± 5.1 | |
| | Doubao Seed 2.1 Pro | 85.8 ± 0.7 | 44.6 ± 2.2 | 73.2 ± 1.0 | 5.0 ± 1.0 | 26.0 ± 1.0 | 33.7 ± 0.6 | |
|
|
| <sup>†</sup>Excludes items refused by the model's API service. Grok 4.6 runs at `xhigh`, every other model at `max`. |
|
|
| ## Selection |
|
|
| Each tier is sampled independently and uniformly without replacement with `random.Random("2027:<tier>")`, releasing `ceil(0.30 × full item count)` items from the full corpus sorted by item ID. The selection script and SHA-256 commitments to every full tier are in the GitHub repository (`build_public_subset.py`, `selection_manifest.json`). |
|
|
| ## Evaluate on the Full Benchmark |
|
|
| The held-out 70% is not public. To have a model evaluated on the full benchmark, email Ming Zhang (mingzhang23@m.fudan.edu.cn) or Maxm Pan (maxmpan@tencent.com), and we will run it under the same protocol as the leaderboard. |
|
|
| ## License |
|
|
| Apache-2.0. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{zhang2026hyperlogic, |
| title = {HyperLogic: A Hard, Forward-Authored Chinese Logical Reasoning |
| Benchmark with Execution-Derived Answers}, |
| author = {Zhang, Ming and Peng, Qiyuan and Wei, Yinxi and Shen, Yujiong and |
| Tan, Kexin and Wang, Yuhui and Xiang, Zhenghao and Ye, Junjie and |
| Yin, Zhangyue and Xi, Zhiheng and Dou, Shihan and Wang, Weikang and |
| Zhang, Yuhao and Gui, Tao and Yang, Ruizhi and Zhang, Qi and |
| Huang, Xuanjing and Chen, Alex and Pan, Maxm}, |
| journal = {arXiv preprint arXiv:2605.19597}, |
| year = {2026} |
| } |
| ``` |
|
|
